Building system control

Abstract

Controlling of a system within a building is performed by receiving (1401) sensor data from an environment sensor (1503) within a room (1502) in a building (1501), identifying (1402) stable regions an

Description

TECHNICAL FIELD

[0001] The present invention relates to a method and a control system for controlling a building system. In certain examples the building system may be a building heating, cooling, ventilation, lighting, security or mechanical actuation system. The control method builds upon a method for characterising a room within the building based on data from an environment sensor. The building system is controlled based on the room characterisation. The characterisation method uses a prior method for calibrating the characterisation of a room. BACKGROUND

[0002] Environment sensors, for instance temperature sensors, pressure sensors, humidity sensors, particulate matter sensors, a background pressure and differential pressure sensor a proximity or motion sensor, a light sensor, a distance or range sensor, or gas sensors (such as those for sensing CO2 or CO) are well known. Environment sensors, particularly temperature sensors, are commonly used in buildings including commercial, industrial and office buildings, and domestic premises (that is, houses and apartments, referred to in this document as homes). Environment sensors may be used to gather data for use in building control systems, such as Heating, Ventilation and Air Conditioning (HVAC) systems, lighting systems, building mechanical control systems, or security systems (this list is not exhaustive). In some cases, environment sensors may directly control a building system, for instance, where a temperature sensor forms a thermostat for controlling a building heating system.

[0003] The use of sensors, for instance cameras and motion sensors, is a well-known approach to detect events within a building and to control building systems based on those events. An example of an event that may be detected using a camera or a motion sensor is a person entering or exiting an indoor space (for instance, a building room). A camera or motion sensor may also be used to detect a room state, for instance occupancy levels within a room. However, cameras and motion sensors are relatively expensive, particularly if used extensively throughout a building. Related to this the computing power and ancillary equipment required to process camera and motion sensor data to determine states and events may further add to the cost, besides system complexity and the need for maintenance. In some cases, particularly cameras may not be used due to privacy concerns and potential data breaches, especially in public buildings such as hospitals or hotels. Furthermore, a range of sensor types may be required to detect various indoor space states (for instance, occupancy levels) and events within the indoor space (for instance, people or animals entering or exiting a room, doors or windows being opened or heating or cooling systems being activated or deactivated).

[0004] It is an aim of certain examples of the present invention to solve, mitigate or obviate, at least partly, at least one of the problems and / or disadvantages associated with the prior art. Certain examples aim to provide at least one of the advantages described below. BRIEF SUMMARY OF THE INVENTION

[0005] Embodiments of the present invention are described in connection with controlling building systems, and the term “building” should be broadly construed to mean any structure defining a space which is wholly or partially contained. The term “building” includes industrial, commercial, retail and office buildings or structures as well as domestic premises including houses and apartments (hereinafter referred to as “homes”). However, as will become apparent from the following description, embodiments of the present invention are particularly suited to monitoring or controlling building systems within homes. The terms “room” or “room within a building” should be broadly construed to encompass any indoor space that is fully or partially enclosed within a building. Of particular interest is indoor living space. Where reference is made in the following description, for instance to “characterising an indoor space” this should be construed to include characterising a room within a building.

[0006] According to a first aspect of the present invention there is provided a method of controlling a system within a building, the method comprising: receiving sensor data from an environment sensor within a room in a building; identifying stable regions and change regions within the received sensor data; and inferring at least one of a room state or an event occurring within the room based on the identification; wherein the method further comprises: controlling a building system based upon the inferred room state or event; or supplying information to a building control system for controlling the building system based upon the inferred room state or event.

[0007] An advantage of the first aspect of the present invention is that a relatively low-cost environment sensor may be used to identify a room state or event, for instance the number of occupants or when people enter or leave a room, which may be used to control a building system. An example of this is using room occupant information to control a heating system for increased comfort of the occupants or to increase energy efficiency (for instance by turning off heating in unoccupied rooms).

[0008] Controlling a building system may comprise generating a control signal to control at least one component within a building system; or generating an output signal indicative of a building system state.

[0009] The control signal may instruct at least one component of a building system to turn on or off; to adjust a set parameter; or to change a mode of operation; or the supplied information may indicate to the building control system that at least one component of a building system should be turned on or off; have a set parameter adjusted; or have a mode of operation changed.

[0010] The environment sensor may comprise: a temperature sensor; a pressure sensor; a particulate matter sensor; a humidity sensor; a gas sensor; a background pressure and differential pressure sensor; a proximity or motion sensor; a light sensor; or a distance or range sensor.

[0011] The building system may comprise one or more selected systems or a combination of systems from a group including: a building ventilation system; a building heating system; a building air conditioning system; a building security system; a building lighting system; or a building mechanical control system.

[0012] Controlling a building system based upon the inferred room state or event may comprise controlling one or more of: a ventilation fan; a ventilation valve; a heating control, output device or valve; an air conditioning control, output device or valve; a light; a security alarm, security light or security output signal to a remote device or monitoring service; or a building mechanical actuator.

[0013] Where the environment sensor is a temperature sensor the method may further comprise using the sensed temperature as a thermostat input for a building heating system.

[0014] Where the environment sensor is a temperature sensor, an inferred room space state may comprise a state in which the number of heat sources and heat sinks within the room remains static.

[0015] Where the environment sensor is a temperature sensor, an inferred event that has occurred within the room may comprise one of: a heat source entering or being activated in the room; or a loss of heat being triggered in the room.

[0016] A heat source entering or being activated in the room may comprise: an artificial heating system being activated within the room; or a person or animal entering the room.

[0017] A loss of heat being triggered in the room may comprise: an artificial cooling system being activated in the room; an artificial heating system being deactivated within the room; an aperture, for instance a door or window, being opened allowing warm air to vent from the room; or a person or animal leaving the room.

[0018] The method may further comprise determining occupancy information in respect of the room based upon the inferred room state or event; and controlling the building system based upon the determined occupancy information.

[0019] The method may further comprise receiving sensor data from a plurality of environment sensors located within a plurality of rooms in a building; identifying stable regions and change regions within the received sensor data in respect of each room; inferring at least one of a room state for each respective room or an event occurring within each respective room based on the identification; and controlling a building system based upon the inferred room states or events in respect of each room.

[0020] The method may further comprise determining occupancy information in respect of each room based upon the inferred room states or events; and controlling the building system based upon the determined occupancy information for each room.

[0021] Controlling the system may comprise controlling the building system differently in respect of at least two rooms based upon the inferred room states or events in respect of those rooms.

[0022] The method may further comprise receiving data indicative of whether a door or a window opening onto at least one room with an environment sensor is open; wherein the control of a building system is further based on the received data.

[0023] The method may further comprise predicting future occupancy of a room within a building based on determined occupancy and controlling the building system based on predicted future occupancy.

[0024] Identifying stable regions and change regions within the received sensor data may comprise identifying within the received sensor data at least one: stable region indicative of a period of time within which the received data does not vary by more than a magnitude threshold; trend region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient less than a gradient threshold; or rapid change region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient greater than or equal to the gradient threshold.

[0025] Prior to the step of identification, the method may further comprise filtering the sensor data. The filtering may comprise noise reduction and removal of fluctuations not indicative of change within the room. The filtering may comprise passing the sensor data through a weighted window processing filter. Prior to the step of identification, the method may further comprise curve fitting the sensor data or filtered sensor data to a polynomial curve. The sensor data or filtered sensor data may be fitted to a polynomial curve with an order of two or more. Identifying a stable region, trend region or rapid change region may comprise processing the sensor data, filtered sensor data or curve fitted sensor data using a region weighting mathematical expression.

[0026] For at least one identified stable region, inferring a room state may comprise: identifying a sensor data magnitude or an average sensor data magnitude within the identified stable region; identifying a sensor data magnitude band containing the sensor data magnitude or average magnitude; and inferring a room state based on the identified sensor data magnitude band; wherein the inference of a room state is based on a prior calibration process of measuring sensor data magnitudes for a plurality of known room states and determining sensor data magnitude bands forthose known room states.

[0027] For at least one trend region the method further may comprise: inferring that no event has occurred within the room state or that the room remains within a previously identified room state based on a prior calibration process of measuring sensor data magnitudes for a plurality of known room states and determining expected data trends or drifts in sensor data forthose known room states overtime.

[0028] For at least one identified rapid change region inferring an event occurring within the room may comprise: identifying a sensor data gradient or an average sensor data gradient within the identified rapid change region; identifying a sensor data gradient band containing the sensor data gradient or average gradient; and inferring that an event that has occurred within the room based on the identified sensor data gradient band; wherein the inference of an event that has occurred within the room is based on a prior calibration process of measuring sensor data gradients for a plurality of known events within the room and determining sensor data gradient bands forthose known events within the room.

[0029] Inferring an event that has occurred within the room may be further based upon an identified sequence of two or more different rapid change regions. Inferring an event that has occurred within the room may be further based upon an identified sequence of two or more different rapid change regions having opposite polarity sensor data gradients. Inferring an event that has occurred within the room may be further based upon an identified sequence of one or more stable regions or trend regions and one or more rapid change regions.

[0030] According to a second aspect of the present invention there is provided a computer-readable storage medium having computer-readable program code stored therein that, in response to execution by a processor, causes the processor to perform the above method.

[0031] According to a third aspect of the present invention there is provided an apparatus comprising a processor and a memory storing executable instructions that, in response to execution by the processor, cause the apparatus to perform the above method.

[0032] According to a fourth aspect of the present invention there is provided a control system for controlling a system within a building, the control system comprising: an environment sensor configured to generate sensor data indicative of an environment parameter within a room in a building; and a computing device configured to: receive sensor data from the environment sensor; identify stable regions and change regions within the received sensor data; and infer at least one of a room state or an event occurring within the room based on the identification; wherein the computing device is further configured to: control a building system based upon the inferred room state or event; or supply information to a building control system for controlling the building system based upon the inferred room state or event.

[0033] The control system may further comprise: a plurality of environment sensors, each environment sensor being located within a separate room within a building and generating sensor data indicative of an environment parameter and providing the sensor data to the computing device; wherein the computing device may be further configured to: identify stable regions and change regions within the received sensor data in respect of each room; infer at least one of a room state for each respective room or an event occurring within each respective room based on the identification; and control a building system based upon the inferred room states or events in respect of each room.

[0034] A separate computing device may be associated with each environment sensor and configured to perform at least part of the processing to infer at least one of a room state or an event occurring within the room.

[0035] The control system may further comprise a building system controller configured to: receive data from a computing device associated with each environment sensor, the data being indicative of an environment parameter; identified stable regions and change regions within the sensor data; or inferred room states or events occurring within the room; and control a building system based upon the inferred room states or events in respect of each room.

[0036] The computing device or the building system controller may be further configured to perform the above method.

[0037] There is further disclosed a computer-implemented method for characterising an indoor space, the method comprising: receiving sensor data from an environment sensor within an indoor space; identifying within the received sensor data at least one: stable region indicative of a period of time within which the received data does not vary by more than a magnitude threshold; trend region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient less than a gradient threshold; or rapid change region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient greater than or equal to the gradient threshold; and inferring at least one of an indoor space state or an event occurring within the indoor space based on the identification.

[0038] Advantageously a relatively low-cost environment sensor, for instance a single temperature sensor, may be used in an indoor space to infer an indoor space state or an event within the indoor space. For instance, a temperature sensor may be located within an indoor space, for instance a room in a domestic premises, and room state information such as the number of people present, and which heat sources are activated or deactivated may be inferred. Alternatively or additionally, events such as people entering or exiting the room or doors or windows being opened or closed may be inferred. In some examples this inference of indoor space state or events is based on a prior calibration process for the environment sensor situated in a particular location within the indoor space.

[0039] Prior to the step of identification, the method may further comprise filtering the sensor data. The filtering may comprise noise reduction and removal of fluctuations not indicative of change within the indoor space. The filtering may comprise passing the sensor data through a weighted window processing filter. Prior to the step of identification, the method may further comprise curve fitting the sensor data or filtered sensor data to a polynomial curve. The sensor data or filtered sensor data may be fitted to a polynomial curve with an order of two or more. Filtering and / or curve fitting may reduce noise from the sensor, outline median and data trends, and eliminate small fluctuations that have limited information value for identifying indoor space states or events.

[0040] Identifying a stable region, trend region or rapid change region may comprise processing the sensor data, filtered sensor data or curve fitted sensor data using a region weighting mathematical expression. For at least one identified stable region inferring an indoor space state may comprise: identifying a sensor data magnitude or an average sensor data magnitude within the identified stable region; identifying a sensor data magnitude band containing the sensor data magnitude or average magnitude; and inferring an indoor space state based on the identified sensor data magnitude band; wherein the inference of an indoor space state is based on a prior calibration process of measuring sensor data magnitudes for a plurality of known indoor space states and determining sensor data magnitude bands forthose recognised indoor space states. For at least one trend region the method may comprise: inferring that no event has occurred within the indoor space state or that the indoor space remains within a previously identified indoor space state based on a prior calibration process of measuring sensor data magnitudes for a plurality of known indoor space states and determining expected data trends or drifts in sensor data for those known indoor space states over time. For at least one identified rapid change region inferring an event occurring within the indoor space may comprise: identifying a sensor data gradient or an average sensor data gradient within the identified rapid change region; identifying a sensor data gradient band containing the sensor data gradient or average gradient; and inferring that an event that has occurred within the indoor space based on the identified sensor data gradient band; wherein the inference of an event that has occurred within the indoor space is based on a prior calibration process of measuring sensor data gradients for a plurality of known events within the indoor space and determining sensor data gradient bands forthose known events within the indoor space. The calibration is also used for the identifications from different levels of the indoor space temperature and outlining the change expected for a more accurate inference. In some examples, no more than two or three levels may be required.

[0041] In some examples, inferring an event that has occurred within the indoor space is further based upon an identified sequence of at least one, optionally two or more different rapid change regions. In some examples, inferring an event that has occurred within the indoor space is further based upon an identified sequence of one or more different rapid change regions having opposite polarity sensor data gradients. In some examples, inferring an event that has occurred within the indoor space is further based upon an identified sequence of one or more stable regions or trend regions and one or more rapid change regions. That is, it may be a compound series of multiple identified regions that allows inferences to be drawn about what is happening in a room. The analysis method looks at recent data in a quasi-real time manner. Accordingly, this may allow the recognition of individual events which may precede but are juxtaposed by identifiable later events.

[0042] The environment sensor may comprise: a temperature sensor; a pressure sensor; a particulate matter sensor; a humidity sensor; a gas sensor; a background pressure and differential pressure sensor; a proximity or motion sensor; a light sensor or a distance or range sensor. Sensed particulate matter may be correlated to sensed pressure or temperature and as such sensing particulate matter may allow for similar inferences to be made from sensor data concerning room states and events in the room. For the example of a particulate matter sensor, room states and events can be inferred by identifying passive and active parameters for the measured parameter. Where the environment sensor is a temperature sensor located within the indoor space, an inferred indoor space state may comprise a state in which the number of heat sources and heat sinks within the indoor state remains static. Where the environment sensor is a temperature sensor located within the indoor space, an inferred event that has occurred within the indoor space may comprise one of: a heat source entering or being activated in the indoor space; or a loss of heat being triggered in the room. A heat source entering or being activated in the indoor space comprises: an artificial heating system being activated within the indoor space; or a person or animal entering the indoor space. A loss of heat being triggered in the room may comprise: an artificial cooling system being activated in the indoor space; an artificial heating system being deactivated within the indoor space; an aperture, for instance a door or window, being opened allowing warm air to vent from the indoor space; or a person or animal leaving the indoor space. While the present invention is not limited in its broadest scope to temperature sensors, the information that may be inferred from temperature data is intuitively easier to understand.

[0043] The indoor space may comprise a room in a building or multiple rooms in a building.

[0044] There is further disclosed a computer-readable storage medium having computer-readable program code stored therein that, in response to execution by a processor, causes the processor to perform the above characterisation method.

[0045] There is further disclosed an apparatus comprising a processor and a memory storing executable instructions that, in response to execution by the processor, cause the apparatus to perform the above characterisation method.

[0046] There is further disclosed a sensing system for characterising an indoor space, the system comprising: an environment sensor configured to generate sensor data indicative of an environment parameter within an indoor space; and a computing device configured to: receive sensor data from the environment sensor; identify within the received sensor data at least one: stable region indicative of a period of time within which the received data does not vary by more than a magnitude threshold; trend region indicative of a period of time within which the received data trends upwards ordownwards with a sensor data gradient less than a threshold gradient; or rapid change region indicative of a period of time within which the received data trends upwards ordownwards within a sensor data gradient greater than or equal to a threshold gradient; and infer one of an indoor space state or an event occurring within the indoor space based on the identification.

[0047] There is further disclosed a computer-implemented method for calibrating the characterisation of an indoor space, the method comprising: receiving sensor data from an environment sensor within an indoor space; observing indoor space states or events within the indoor space; and correlating sensor data to observed indoor space states or events within the indoor space.

[0048] Correlating sensor data to observed indoor space states or events within the indoor space may comprise: measuring sensor data magnitudes for a plurality of observed indoor space states and determining sensor data magnitude bands for those observed indoor space states; or measuring sensor data gradients for a plurality of observed events within the indoor space and determining sensor data gradient bands forthose observed events within the indoor space.

[0049] Where the environment sensor is a temperature sensor located within the indoor space, a known indoor space state may comprise a state in which the number of heat sources and heat sinks within the indoor state remains static. Where the environment sensor is a temperature sensor located within the indoor space, a known event that has occurred within the indoor space may comprise one of: a heat source entering or being activated in the indoor space; or a loss of heat being triggered in the room. A heat source entering or being activated in the indoor space may comprise: an artificial heating system being activated within the indoor space; or a person or animal entering the indoor space. A loss of heat being triggered in the room may comprise: an artificial cooling system being activated in the indoor space; an artificial heating system being deactivated within the indoor space; an aperture, for instance a door or window, being opened allowing warm air to vent from the indoor space; or a person or animal leaving the indoor space. In poorly insulated, heat permeable living spaces, a slow temperature trend towards an ambient temperature outside of the living space may occur, and this may be detected.

[0050] The calibration may be specific to a particular indoor space and to a particular sensor location within that indoor space. More specifically, the calibration asserts a typical threshold or value for a given sensed source (for instance, sensed thermal source). The same method may be used to assert or predict other sources.

[0051] There is further disclosed a computer-readable storage medium having computer-readable program code stored therein that, in response to execution by a processor, cause the processor to perform the above calibration method.

[0052] There is further disclosed an apparatus comprising a processor and a memory storing executable instructions that, in response to execution by the processor, cause the apparatus to perform the above calibration method.

[0053] There is further disclosed a sensing system for calibrating the characterisation of an indoor space, the system comprising: an environment sensor configured to generate sensor data indicative of an environment parameter within an indoor space; and a computing device configured to: receive sensor data from the environment sensor; observe indoor space states or events within the indoor space; and correlate sensor data to observed indoor space states or events within the indoor space. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Examples of the invention are further described hereinafter with reference to the accompanying drawings, in which: Figure 1 schematically illustrates a sensing system for characterising a room state or for calibrating the characterisation; Figure 2 is a perspective view of a hybrid home; Figure 3 is a plan view of the hybrid home of figure 2 in which the sensing system of figure 1 is deployed in a room; Figure 4 is a graph showing raw data received from a temperature sensor forming part of the sensing system of figure 1; Figure 5 is a graph showing the raw data for figure 4 and the raw data after filtering; Figure 6 is a graph showing the filtered data of figure 5 and a curved fitted to the filtered data; Figure 7 is a graph showing the curved fitted data of figure 6 after regions of interest have been identified; Figure 8 is a flowchart illustrating a method for characterising a room state; Figure 9 is a flowchart illustrating a method for characterising a room state; Figure 10 is a flowchart illustrating a method for calibrating a system for characterising a room state; Figure 11 is a flowchart illustrating a method for calibrating a system for characterising a room state; Figure 12 schematically illustrates a sensor forming part of the sensing system of figure 1; Figure 13 schematically illustrates a computing device forming part of the sensing system of figure 1; Figure 14 is a flowchart illustrating a method of controlling a building system using a single sensor according to an embodiment of the present invention; Figure 15 is a plan view of a home in which a sensing system according to an embodiment of the present invention is deployed with a sensor in each room; and Figure 16 is a flowchart illustrating a method of controlling a building system using multiple sensors as deployed in separate rooms in accordance with figure 15 according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The present invention concerns a method and a sensing system for controlling a building system. In certain examples the building system may be a building heating, cooling, ventilation, lighting or security system. The control method builds upon a system and method for characterising a room and an associated calibration method. In the following description the system and method for characterising a room will be described first. In the following description the term room is used, particularly a room in a building such as a domestic premises, however embodiments of the present invention are applicable to any indoor space. The characterisation of the indoor space uses data from an environment sensor, for instance a temperature sensor, a pressure sensor, a humidity sensor, a CO2 sensor, a particulate matter sensor, a background pressure and differential pressure sensor, a proximity or motion sensor, a light sensor, or a distance or range sensor. Raw sensor data is processed and used to infer a room state or an event that has taken place or is currently taking place within the room.

[0056] Certain embodiments of the present invention are particularly suited to the detection and identification of human presence and related movements within a living space in a home using a temperature sensor and the control of a building system based on the detected presence and movement. Certain embodiments assess the thermal data from a temperature sensor for both magnitude and gradient indicating a thermal gradient within the room with high precision. Certain embodiments of the present invention also allow the presence or absence of heat sources to be inferred or for thermal variations caused through heat gain / loss appliances, by drafts and leaks or the opening or closing of a window or door to be inferred and a building system to be controlled accordingly.

[0057] Stated broadly, the present invention allows information to be used to infer room states and room events using data from substantially any environment sensor. In the discussion below concerning characterising a room and a calibration process, the type of sensing is considered broadly. However, the present inventor has identified that a temperature sensor with a high precision is particularly suited to detecting changes to the thermal envelope of a room. Such a thermal sensor can sense a thermal background of a room and dynamic heat sources, including heat sources with a low heat footprint. From this sensed thermal data, information can be inferred (based on a prior calibration process). This inferred information is particularly pertinent for the control of building systems as detected thermal changes may be used, for instance, to identify the number of people present or entering a room which is of direct use, for instance to control a building heating system in that room. Accordingly, in some of the examples presented below, specific reference is made to sensing thermal changes. However, the skilled person will immediately appreciate that the methodology set out in these examples is more generally applicable to other sensor types (unless the specific context requires that that methodology is restricted to temperature sensing).

[0058] For the example of thermal sensing, each internal space such as a room can be calibrated to identify sources of heat or their absence through measurement of the thermal state of the room. Sources of heat are identified through their energy or heat release which is captured by a precision thermal sensor. The sensor is place in a salient position in the typical room (4 m x 3 m) and calibrated against various events. In spaces that are identical in footprint such calibration may be similar in details and values for the expected heat loads. The identification of the thermal status for each room may be used, for instance, to control the heating system used for the overall space. The heat loads identified are used to reduce or increase heating level within either the individual rooms or the overall space inhabited.

[0059] Similarly, other sensor types may yield similar information as set out below:

[0060] A pressure sensor may be used to control a building system using barometric sensing with high precision (Pascal level). Such a sensor may sense background pressure and dynamic pressure source with a low “partial” pressure profile or change to overall pressure.

[0061] A background pressure and differential air pressure sensor may be used to control a building system using the measurement of mass flow of air (N2 and O2) in a directional manner. This may reveal local air currents and any air disturbances within the space. This in turn may reveal the presence or absence of air currents for best ventilation assessment. This can be combined with other types of sensors to provide presence of people / pets / objects, and general living space status.

[0062] A humidity sensor may be used to control a building system using sensed moisture in the air. By sensing humidity with high precision changes in humidity may be correlated to a room condition. With sufficient precision of measurement, humidity from perspiration or breathing of a room occupant may be detected. Small changes in humidity can be identified, resulting in it being possible to take prompt action (for instance, to adjust ventilation in the room).

[0063] A CO2 sensor may be used to control a building system using changes in CO2 resulting from perspiration or breathing. Additionally, CO2 emitting sources may be sensed. Changes in CO2 may outline levels of occupation by people, emissions from fires, heated objects, and hazardous levels due to fires or smoke. Lower CO2 values may reveal closed spaces or good ventilation levels which can be used as a reference.

[0064] A particulate matter sensor (sometimes referred to as an air quality sensor) may be used to control a building system using a count of the number of particles in the air (including their distribution). Particulate matter levels may be recorded as per defined standards to infer levels of air quality. Furthermore, changes in presence, movement of people / pets, rising temperature, sunshine, vacant spaces, air currents and their direction as well as still pockets may be detected.

[0065] For embodiments of the present invention that do relate specifically to thermal sensing, the present invention allows for thermal changes due to the presence or absence of a thermal source such as a human body or similar (or more than one body) or slow changes ordrifts in temperature within the living space and which can be due to slow heat gain or loss phenomena and any changes from a steady state caused by these. These outlined phenomena can be caused by: presence or absence (use and non-use) of heat sources and building cooling systems or the appearance of heat loss phenomena (heat leakage / loss and absence or switch off of heaters, warm air etc.), drafts caused either by heat loss (bad insulation / thermal performance), local actions such as door or window opening / closure, the presence of a person in the living space and increase or decrease in the number of persons, detection of subsequent actions such as door opening / closure, window opening / closure, person or persons accessing the living space.

[0066] Heat gain sources include a person or persons, a heater or more, a draft or heat loss occurrences. The gains outline an increase with respect to heat when the sources are exhibiting a warmer output than the current temperature of the living space and they outline a decrease when for example a cooling appliance is used, or a heat loss device or object is present or activated.

[0067] As will be described below in connection with figures 4 to 7 data from an environment sensor is signal processed to remove noise and irrelevant data fluctuations, the filtered data is fitted to a curve and a mathematical identification process is applied. The identification process identifies within the received sensor data at least one: stable region indicative of a period of time within which the received data is relatively stable within a first sensor data magnitude band; trend region indicative of a period of time within which the received data trends upwards or downwards within a first sensor data gradient band; or rapid change region indicative of a period of time within which the received data trends upwards or downwards within a second sensor data gradient band. From the identified stable region, trend region or rapid change region an indoor space state or event occurring within the indoor space can be inferred. In certain embodiments this inference is based on a prior calibration process, as is described below in connection with figures 10 and 11.

[0068] While the signal processing, inference and calibration methods described below are broadly applicable to any form of environment sensor, the present inventor has identified that they are applicable to temperature sensors. Furthermore, the present inventor has identified that identified stable regions, trend regions or rapid change regions derived from temperature sensor data are particularly informative in inferring room events of particular interest within a room such as in a domestic premises, for instance the number of people within the room and people entering and leaving the room. Accordingly, embodiments of the present invention described below focus on the use of a single thermal sensor which is used to detect small changes in the thermal envelope associated with moving or static persons, animals, or objects as well as assessment of the overall space thermal background and any associated changes within a defined space (as shown above). It will be understood however that in its broadest form the present invention is not limited only to temperature sensors.

[0069] Referring now to figure 1, a sensing system suitable for characterising an indoor space (and for calibrating an indoor space characterisation method) is illustrated. The sensing system comprises a single sensor 101, which in certain embodiments of the present invention may be a temperature sensor. The sensor 101 may be any suitable commercially sourced sensor available to the skilled person or a proprietary sensor providing the necessary precision described above. For instance, for the case of a temperature sensor, a sensor with a precision of ±0.1 °C (or better) may suffice to characterise an indoor space. Improved results may be achieved with a sensor with a precision of +0.01 °C (or better). A typical measurement frequency for sensor readings may be 0.05 Hz to 20 Hz. In some cases, the accuracy of the temperature measurement may be less significant than the precision: so long as a temperature change indicative of a room event is detectable, the magnitude of the temperature measurement may not be critical. That is, the value of the temperature reading within a temperature scale is not as important as the ability to detect changes in temperature (or indeed the absence of change in a stable region of the data). Suitable temperature sensors are widely commercially available, for instance from vendors such as ST-Microelectronic Ltd, Analog Devices Inc, Texas Instruments Inc. and similar electronics systems providers.

[0070] The sensor data sensed by the sensor 101 is transmitted to a computing device 102, or other suitable device for processing sensor data. The sensor 101 and the computing device 102 may be coupled together using any available wired or wireless network. The processing of the sensor data is described below starting with figure 4. In some embodiments the computing device 102 may be an embedded processor associated with the sensor 101. That is, the sensor 101 may be coupled directly to or incorporated into a computing device 102 as a single integrated package. The computing device 102 may perform all of the processing described below or part of the processing and communicate the results to another location. In some examples, partially or fully processed sensor data (or raw sensor data) may be communicated from a computing device associated with a sensor to a building system controller responsible for controlling a building system based on the processed sensor data.

[0071] Referring now to figures 2 and 3, as noted previously, while embodiments of the present invention are applicable to buildings in general, they are particularly to suited to domestic premises such as houses and apartments (referred to generically in this document as homes). Partly this is because the daily routines of people within a home and the range of activities that take place within a home are significantly more complex than for industrial, retail or commercial premises, with the result that it may be harder to infer indoor space states or events within the indoor space using conventional techniques. Figure 2 is a perspective view of one particular form of home, and figure 3 illustrates a cross section plan view of such a home incorporating the sensing system according to figure 1.

[0072] Figures 2 and 3, show a home 200 which is constructed as a hybrid building 200 (which may also be referred to as a modular building). It should be appreciated that the specific example of a hybrid home is not limiting on the types of buildings or indoor spaces to which the present application can be applied. The hybrid building 200 comprises a first building section 201 and a second building section 202. The first building section 201 can take the form of a dock. The term “dock” is used to describe a building section to which another building section may be docked, installed, connected, or attached. The first building section 201 is an on-site construction at a final location of the building 200, which may be a fixed, on-site location. That is, the building 200 has a final, fixed, on-site location. In this exemplary embodiment, said final, fixed, on-site location is determined by construction plans and fixed by virtue of building foundations. The term “on-site location” is used to refer to the building site, which will be understood to refer to the immediate proximity of the building 200 and the entire building site, including housing estate, on which the building 200 is to be built. It will be understood that the site may be a large building / construction site comprising a plurality of plots, the final location for the building 100 being provided by one of said plots.

[0073] The second building section 202 can take the form of a module. The term “module" is used to describe a building section which can be docked, or is installable, connectable, or attachable to another building section, particularly a first building section in the form of a dock. The second building section 202 is transportable to the final location in a substantially assembled form.

[0074] During construction of the first building section 201 at the on-site location, the first building section 201 is preconfigured to receive the second building section 202. That is, the first building section 202 is constructed with the knowledge and design that a second building section is to be subsequently connected, and the first building section 201 is thus preconfigured for connection and receipt of the second building section. The second building section 202 may be removable. This could relate to the shaping of the first building section 201, through to sealing and connection features. The second building section 202 is connected to the first building section 201 at the final location of the building 200, thereby to provide said building 200 at the final, fixed, on-site location.

[0075] The first building section 201 further includes a services hub 203, which may include part or the whole of certain building systems. For instance, for a ventilation system, the services hub 203 may include ventilation system components such as the pumping and heat exchange elements of a Mechanical Ventilation with Heat Recovery (MVHR) system.

[0076] Figure 3 shows the sensor 101 located within dock 201, though it will be understood that this location is merely exemplary, and the sensor 101 may be in any room within any part of the home where it is desired to monitor room states and events. Similarly, the computing device 102 is shown located within dock 201, though it may be located at any convenient location so long as it can communicate with sensor 101. In some examples the computing device 102 may be collocated or integrally formed with the sensor 101. The computing device 102 may be an embedded processor. The computing device 102 may be configured to provide an output signal or a control signal, for instance a control signal for a building system (not illustrated).

[0077] Within the room or other indoor space to be characterised the sensor 101 can be positioned at any convenient location, for instance near the entrance, in the periphery, or in the middle of the room. For the selected room or other indoor space that it is desired to monitor or characterise, the selected position within the room for the sensor may affect the ability to infer room states and events and may affect the calibration process described below. In some examples a central location within a room may be preferable so that the sensor readings are not unduly affected by events around the periphery which may be less significant. As will become clear from the discussion of calibration in the following description (particularly in relation to figures 10 and 11), during calibration the relevant heat profiles from a given heat or cold source for example, can be obtained at certain distances from the sensor and this outlines the heat or cool signature of such a source to ascertain the state of the room space (that is what heat or cooling sources are present or activated). Calibration also records one or more thermal states of the room and the signature of the heat sources expected. Such signatures have a similar shape but may differ in the relative magnitude. In some cases, the calibration may be performed iteratively for multiple distances between heating or cooling sources (including humans) and the sensor which can assist with ascertaining the heat or cooling source and its distance from the sensor within the relevant indoor space. It may be that certain locations within a room are particularly sensitive to detecting room states or events, as may be established through a calibration process (which may include changing the location of the sensor within a room until an optimal location is determined). For instance, for detecting when a door is opened using a temperature sensor, it may be that a location for the sensor close to the door gives the clearest indication of a door opening event in the sensor data. Alternatively, for sensing a number of persons present within a room through a temperature sensor, a location within the central region of a room may be preferred. For the case of a temperature sensor, positioning is flexible because it is the overall background temperature that is sensed. In the case sensing the presence and movement of persons is important, and then the sensor can be strategically positioned in an area closer to where people congregate or pass by. In some examples a location close to areas or movement or congregation for the building occupants may be preferable. In some examples a location close to or remote from artificial heating or cooling sources may be preferred, dependent on whether the intention is to capture information about their use or whether other events are more important to detect.

[0078] Turning now to figure 4, this graph illustrates raw temperature data 401 (dotted line) generated by a sensor 101 and received by a computing device 102. The X axis indicates data samples, numbered sequentially from 1 to 1140. Data samples may be received, for instance, 10 times per second. The Y axis shows temperature ranging from 22.05°C to 23°C. It can be seen that the raw temperature sensor data 401 within this temperature range is quite noisy and includes fluctuations that may not be indicative of room states or events. However, broadly, generally steady stable regions and rapid change regions can be observed.

[0079] In certain embodiments of the present invention regions of interest within the temperature sensor may be identified within the raw temperature data. However, in accordance with other embodiments of the present invention, the raw temperature is first pre-processed.

[0080] Turning now to figure 5, this graph shows both the raw temperature data 401 (dotted line) and filtered temperature data 501 (solid line) after a filtering process to remove any noise or irrelevant fluctuations has been applied. The filter allows for the removal of noise fluctuations and yields a trend to the data so that deduction of the heat and temperature parameters are made easier for access.

[0081] The filtering of the raw temperature data may use any conventional technique known to the skilled person that serves to remove noise and irrelevant fluctuations (but retain trend regions and rapid change regions indicative of room states and events, as well as stable regions). The present inventor has identified that for filtering temperature data a weighted window processing filter is suitable, though other filter types such as dedicated multipole filters may be used. A further filter option would be to use a recursive filter. In other embodiments of the invention a machine learning clustering technique for clustering data points based on their similarity may be used, such as K-means clustering. A weighted window processing filter is a type of digital signal processing filter used to modify or enhance a signal by applying a weighted average to a set of samples within a sliding window. The filter operates by first defining a window of a certain size, which may be applied to each sample in turn (with the window looking back over a preceding set of temperature samples within the window). Each sample within the window is then multiplied by a corresponding weight value, which can be predetermined or dynamically calculated based on the characteristics of the input signal. The weighted average of the samples within the window is then computed, with the resulting value replacing the original sample. The temperature data for each sample is thus replaced by a weighted average of that original sample using a predetermined number of the preceding samples resulting in a filtered output signal that is a smoothed or modified version of the original signal. Any shifts in the data can be corrected by shifting the new data by an amount equal to the width of the weighting window.

[0082] The filtering is performed by the computing device 102 and may form part of a programmed data acquisition loop whereby the temperature data is collected, and a portion is kept in memory.

[0083] The raw data stream from the sensor has m samples, as represented by equation (1) presented below, where I is an index: To, Tl, T2, ... T|, Ti+1, Ti+2, ... Tm-2, Tm-1, Tm (1)

[0084] A weighted data stream may be established where A is a window coefficient, according to equation (2) presented below: Aq.To, A1.T1, A2.T2, ... A.T, Ai+1 .Ti+1, A+?T-2, ... Am-2-Tm-2> Am-1.Tm-1, Am-Tm (2)

[0085] The initial index is at a random temperature sample i which is the start of the averaging window. There are n samples or poles in the averaging window, with 0<=n<=m (the window must be smaller than the data stream). As an example: having a window size n=200 poles, then the sum calculated for the filtered sample B(+199 starting at index i+199 would be according to equation (3) presented below (the index varies from the start to the end of a 200 sample window): Bi+199 = AjTj + Ai+iTi+1 + ... + Ai+iggTi+199 (3) Bi+200 = A1+1T+1 + Ai+2Ti+2 +...+ A|+2ooTi+2OO and so on...

[0086] The matrix for the filtered samples would be in accordance with equation (4) presented below: [Bj] with n-1 <= j <=m (4)

[0087] It will be understood that there is a shift over n-1 values, along the abscissa, and this can be corrected to remove the phase delay caused. The discrepancies caused by missing out on a single sample have no significant effect.

[0088] Practically, each output from the filter is sequentially obtained from earlier data by 200 points. This may represent 10 seconds of data (200 samples when sampling at 20 Hz). This kind of time is a good representation for events that occur in real time within the living space. Such an action window may extend a few seconds to an arbitrary number (below the value m).

[0089] The raw temperature data of figure 4 illustrates a scenario in which a person walks into a small, previously empty room at around index 800 along the X axis. That person is joined by another person a little later, with the door being opened and closed rapidly (from around index 5000) leading to a rise in temperature. A door is opened at around index 8000 one person leaves leading to a large drop in temperature, returning to approximately the temperature of just one person in the room at around index 8500. In comparison to the filtered temperature data line 501 in figure 5, it can be seen that there are inflection points visible around these time points, following filtering using a 200 pole weighted window processing filter. While a 200 pole weighted window processing filter has been applied to the data in figure 5, in other embodiments of the present invention a filter with 2 or more poles may be used.

[0090] Referring now to figure 6, following filtering the filtered temperature data in some embodiments is further processed according to a curve fitting function. In one example, the filtered temperature data is passed to a data fitting polynomial having one or more elements. Figure 6 shows the raw temperature data 401 (dotted line), the filtered temperature data 501 (thin solid line) and curve fitted temperature data 601 (thick solid line). In this step the data is outlined using a fitting curve which follows the filtered data and produces a fit which reduces the fluctuations further and highlights regions of interests. Suitable curve fitting functions will be well known to the skilled person, for instance the Chebyshev method. This curve fitting has an order equal to 2 or higher and outputs a curve fit which removes not only further residual noise but also helps show the trends in the data more consistently.

[0091] Following curve fitting there follows a third step of identifying regions of interest within the smoothed temperature data (although the skilled person will appreciate that in principle this identification step may be applied directly to the raw or filtered temperature data, though potentially with a reduction in accuracy). In this step the data output by the polynomial curve fitting function is passed through a region weighting mathematical expression which identifies the regions of remaining fluctuations and outline areas of thermal stability or regions where temperature is on varying trend. The identification of regions of interest outlines one or more of a stable region indicative of a period of time within which the received data is relatively stable within a first sensor data magnitude band; a trend region indicative of a period of time within which the received data trends upwards or downwards within a first sensor data gradient band; or a rapid change region indicative of a period of time within which the received data trends upwards ordownwards within a second sensor data gradient band. The shape of this curve shows multiple “Trapezoidal” sections (that is, flat sections corresponding to stable regions and ramped sections corresponding to trend or rapid change regions).

[0092] Each stable region comprises a quasi-horizontal portion of the curve fitted temperature data 601 (or the raw or filtered temperature data) against time. That is, in a stable region the temperature remains fairly constant. By consideration of the magnitude of the temperature within the stable region (or the identification of a temperature band within which the stable region is located) an inference may be made of what heat sources are located within that room based on a prior calibration process (as is described below).

[0093] Each trend region comprises a time period including a small upward ordownward trend in temperature. This can be used to infer a drift in the living space thermal envelope, but that no major event has occurred within the room. That is, it may be inferred that the room may remain within a state indicated by a preceding stable region.

[0094] Each rapid change region showing a fast change between two temperature levels and is a transient region that may be used to infer the sudden appearance of a heat gain or heat loss, based on a prior calibration process.

[0095] In some embodiments of the present invention potential heat and loss sources in a room are initially identified as part of the calibration process. As is described in greater detail below, these heat and loss sources are initially weighted so that each of the stable regions can be attributed to the relevant source, and rapid changes between those sources identified.

[0096] Referring now to figure 7, an explanation of the identification of different regions and the inference of events based on a prior calibration process is presented for the specific example of a room initially containing a single person (that is, only a single heat source), a door opening and a second person entering, and the door closing.

[0097] For a given room initially at a temperature To, the entry of a single person may yield an increase in temperature ATi. Assuming for the moment that the heat output from two people is similar within a broad tolerance, if N people enter the room, then the change in temperature is N.ATi. It will be appreciated that different people may generate different amounts of heat, however as an approximation it remains the case that the total heat output is directly proportional to the number of people.

[0098] Similarly, a specific heat source H1 like a heater will cause a change in temperature of AThi (assuming for the moment that the heater is self-regulated).

[0099] Stated generally, each heat source of loss of heat that occurs within a room may be referred to as H2 and will cause a change of temperature of-ATH2.

[00100] The thermal profile for a given room, would therefore be subject to a series of changes outlined initially by a base temperature which can then vary according to the presence, introduction or removal or heat generating or heat removing elements, which cause a temperature change of+ATHx.

[00101] As an example, as illustrated in figure 7, the room may initially be stable at To = 16°C while empty. This first region 701 may be identified as an initial stable region or period of time on the basis that the temperature data does not vary by more than a threshold temperature magnitude throughout this region. The size of the threshold temperature magnitude may be specific to a particular sensor and a particular deployment location within a room, but for instance the threshold might be ~0.2°C. Subsequently there follows first and second rapid change regions 702, 703. A region may be identified as a rapid change region on the basis that the temperature varies by more than the threshold temperature magnitude and the temperature increases or decreases by greater than or equal to a gradient threshold through that period of time. If the gradient in a region is less than the gradient threshold, then that region is identified as a trend region. The first rapid change may be identified as a period of time between a door being opened and closed, during which time warm air escapes the room. While the door is open a second person enters the room. The entry of a person may raise the temperature by circa 1,5°C relative to the initial stable temperature in stable region 701. It may be expected that a temperature sensor will indicate an increase in temperature that stabilises after 1.5°C has been added to the baseline of 16°C. During rapid change region 703 the room is heating up as a result of the person (that is, a heat source) entering the room. Therefore, the temperature differential of 1,5°C can then be identified and attributed to the added presence of a person into the space. Subsequently, a second stable region 704 may be identified where the temperature is 17.5°C and the margin that may be considered acceptable to infer that a single person has entered a room may be ±0.2°C (that is, the threshold temperature magnitude is exceeded). That is, the temperature in the second stable region 704 may be determined according to equation (5) presented below: To + Tri= 17.5±0.2°C (5) Where To is the initial room temperature in region 701 and Tri is the heat output from a single person.

[00102] It will be appreciated that the temperature change yielded by a single person entering a room will be dependent on the particular volume of the room, amongst other parameters, hence the necessity of a calibration process. This may vary between for instance 0.8 to 1,5°C, and this temperature increase is indicated by the arrow 705 in figure 7. Furthermore, the area underneath the graph in stable region 704, relative to the baseline temperature in stable region 701, may be calculated and is indicative of the energy released into the room as a result of a single person entering (this is indicated by the shaded area under region 704 indicated by numeral 706.

[00103] When the temperature is stable or within an acceptable noise margin, then a small droop (that is, a trend region) not exceeding, for instance 0.2°C may be identified within the temperature data as a trend region. As noted previously, a trend region is one in which the temperature magnitude variation exceeds the magnitude threshold, but the gradient of that variation does not exceed the gradient threshold. In a trend region it may be inferred that there is no change in the number of people present. In some examples after a person enters a room the initial temperature rise may be almost exponential, and detected as a rapid change region, but after a short time the temperature stabilises (identified as a new stable region) and may rise afterwards marginally (identified as a further trend region). Any change that exceeds a temperature magnitude shift above a predetermined value, or a significant temperature gradient indicative of a rapid change region may be used to infer a further room event, that is a thermal gain or loss indicative of a new event (for instance people entering or leaving the room, a door being opened or closed, a window being opened or closed etc.).

[00104] For the raw, filtered or curve fitted temperature data as set out above according to the preceding signal processing steps, for the third step of identifying stable, trend and rapid change regions the analytical method employed for windowing the values for temperature within bands covering each stable region may be set as follows.

[00105] Mathematically an arrays of values (Tn ,timen), (Tn-i ,timen-i),..... (To, time0) should outline values T according to equation (6) presented below: Ti € [ To , Tn] we have Ti - AT Ti Ti + AT (6)

[00106] AT may be 0.2°C for the example above. Equation (6) defines the interval for temperature bands for stable temperature regions. For all the values for T (indexed as i) belonging to the inclusive interval of temperatures from To to n, the value of the i indexed temperature Ti will be within the between the values T- AT and T+AT. i takes any values from 0 to n.

[00107] AT is the height of the bar that defines the stable and horizontal region or interest.

[00108] A transient region (that is, a trend region or a rapid change region) is a region which stands between two stable regions or outlines a continuous significant increase or decrease in temperature. This is outlined by events which may include opening or closing of the door, window, or the introduction of a heating or cooling source.

[00109] With respect to heating or cooling sources, these have a self-converging thermal output and may eventually lead to a stable region with respect to the heat envelope of the space considered.

[00110] Referring now to figure 8, is a flowchart illustrating a method for characterising an indoor space state according to an embodiment of the present invention. At step 801 a computing device 102 receives sensor data from sensor 101.

[00111] At step 802 regions (specifically, stable, trend and rapid change regions) are identified within the sensor data.

[00112] As noted above in connection with figure 7, the identification of regions may comprise analysing changes of sensor data magnitude and sensor data gradients in different time portions. Specifically, a stable region may be indicative of a period of time within which the received data is relatively stable. It may be identified by determining a time period during which the sensor data does not vary by more than a data magnitude threshold. In some cases, the threshold is uniform irrespective of the absolute magnitude of the sensor data. In other cases, different thresholds may apply in different portions of a sensing range of a sensor.

[00113] A trend region may be indicative of a period of time within which the received data trends upwards or downwards gradually. It may intuitively be understood as a no change region, but with some sensor drift. For the example of a temperature sensor the sensor drift may equate to gradual heat loss or warming within the room as a result of no-optimal insulation. A trend region may be identified based upon a period of time during which the sensor data varies by more than the magnitude threshold, but the gradient of sensor data change is less than a gradient threshold.

[00114] A rapid change region may be indicative of a period of time within which there is a significant change in the sensor data indicative of an event which has occurred within the room. A rapid change region may be identified based upon a period of time during which the sensor data varies by more than the magnitude threshold and for which the gradient of the sensor data gradient is greater than or equal to the gradient threshold. It will be appreciated that like the threshold temperature magnitude, the threshold temperature gradient may be context specific. However, in some examples the gradient threshold which if exceeded indicates a rapid change region may be ~0.05°C / s (degrees Celsius per second). A trend region may have a markedly lower gradient, for instance ~0.01°C / s or lower, which may normally be experienced for a longer time period, for instance in excess of 3 s. Accordingly, the length of a change in temperature data may also be indicative of the type of change.

[00115] At step 803 an inference is drawn regarding what each identified region (or groups of regions) relates to. This inference is based on a prior calibration process forthat sensor, that room and typical room states and events that may occur in that room.

[00116] Foran identified stable region, a room state may be identified by looking at the average sensor data during the stable region and using a look up table showing sensor data magnitude bands and corresponding room states for each band. The room states may comprise, for instance, the number of occupants and whether artificial heating or cooling appliances are operational. The number of room states and their corresponding sensor data bands will be specific to the particular installation. In some cases, the duration of a stable region may also be used as part of the inference of a room state.

[00117] For an identified rapid change region, the average gradient during that region may be compared to a look up table showing sensor data gradient bands and corresponding room events for each band. The events may include, for instance, a person entering a room, a person leaving a room or the operation or discontinuance of an artificial heating or cooling system. In some cases, the duration of a rapid change region may also be used as part of the inference of a room event.

[00118] Figure 9 is a flowchart illustrating a method for characterising an indoor space state according to a further embodiment of the present invention. Steps 801,802 and 803 are as described above for figure 8. Step 901 of filtering the sensor data corresponds to the filtering process described above in connection with figure 5 for reducing noise and fluctuations not indicative of room states or events. Step 902 of curve fitting the sensor data corresponds to the curve fitting process described above in connection with figure 6 for further smoothing the data before regions are identified.

[00119] The above-described method for identifying data regions and inferring room states and events has been proven by the present inventor to be particularly appropriate and robust. Nevertheless the skilled person will be aware that alternative data processing techniques may be applied, for instance the use of a trained machine learning algorithm to proc, sensor data and infer room states and events (again based on a prior calibration process to form a suitable training data set).

[00120] In some embodiments of the present invention, the inference of a room event may take account of two or more concatenated regions. The concatenated regions may comprise at least one stable or trend region and at least one rapid change region. Or they may comprise two or more concatenated rapid change regions. As one example, a sharp drop in temperature sensor data followed by a rapid rise may indicate that a door has been opened, a person has entered, and the door has been closed behind them. Alternatively, an increase in temperature following a stable region may indicate that an artificial heating system has been turned on.

[00121] While the particular example of a temperature sensor has been given above, the sensor may be any suitable type of environmental sensor, for instance, temperature, pressure, humidity, gas (for instance CO2), a particulate matter sensor, a background pressure and differential pressure sensor, a proximity or motion sensor, a light sensor, or a distance or range sensor. The present inventor has however identified that temperature sensors yield particularly useful information for inferring room states and events such as doors opening and people entering and leaving. Advantageously, this information, obtained from a low cost, may have clear applications for controlling building systems such as HVAC and security systems, as will be apparent to the skilled person, and as is described in greater detail in the description below. Nevertheless, a calibration process as will be described below may allow similar determinations to be made of room states and events for different sensor types.

[00122] Where a temperature sensor is use, an inferred room state may comprise a state in which the number of heat sources and heat sinks within the indoor state remains static. Similarly, an inferred event that has occurred within the room may comprise one of: a heat source entering or being activated in the indoor space; or a loss of heat being triggered in the room. A heat source entering or being activated in the indoor space may comprise: an artificial heating system being activated within the indoor space; or a person or animal entering the indoor space. A loss of heat being triggered in the room may comprises: an artificial cooling system being activated in the indoor space; an artificial heating system being deactivated within the indoor space; an aperture, for instance a door or window, being opened allowing warm air to vent from the indoor space; or a person or animal leaving the indoor space.

[00123] When a room state or event is inferred then this information may be output or used in any suitable form. For instance, it may be displayed on a screen or communicated to any required recipient or device. In some cases, the information may directly feedback to a building system that is being controlled, as is described in greater detail below.

[00124] Turning now to figure 10, this is a flowchart illustrating a method for calibrating a system for characterising an indoor space state according to an embodiment of the present invention. Essentially the calibration process comprises the receipt of sensor data 1001 while also observing 1002 indoor space states and events that occur in the room and correlating 1003 these to the sensor data. In particular, the calibration may for instance comprise the collection of sensor data during periods of time where the number of occupants of a room remains static. Also, for the case of a temperature drop sensed when a door opens, a door may be repeatedly opened and closed and a body of sensor data during open periods be recorded.

[00125] Typically, a calibration may need to be repeated for each room or if a sensor location within the room changes. However, in some cases, where two rooms are similar in terms of their layout and fixed heat sources and sinks then an assumption may be made that the calibration performed for one room may apply equally to the other room. This may be the case particularly for new build housing where on a new housing estate there may be multiple instances of houses with the same floor plan, only one of which need be calibrated.

[00126] Referring to figure 11, this expands upon the correlation step 1003. For a body of sensor data relating to known time periods when a room is in a static state, sensor data magnitudes may be measured at step 1100 and at step 1101 a series of data magnitude bands for observed room states may be established. Thus, the sensor data may later be compared to the bands to infer a current room state. The calibration process relies on taking initial measurement of given cooling or heat sources and recording such values at typical distances from the sensor, including one distance or more. The values recorded will allow for not only asserting the source of heating or cooling but potentially the distance from such a source. As an example: a fixed heater appliance will provide a temperature profile that is repeatable because it is fixed. Should it be moved then that the changed temperature profile may be indicative of the distance between the sensor and the heater.

[00127] For room events sensor data gradients may be measured at step 1102 during each observed event. At step 1103 a series of gradient bands may established corresponding to the observed events. Thus, the sensor data may later be compared to the bands to infer a current room event.

[00128] It will be appreciated that the number of room states and room events, and thus the number of bands and their divisions will be context dependent.

[00129] Figure 12 illustrates a sensor 101 configured to communicate sensor data to a computing device according to an embodiment of the present invention. For instance, the sensor 101 may form part of the sensing system of figure 1, communicating with the computing device 102. The sensor 101 may comprise a sensor 1201 capable of generating sensor data indicative of at least one sensed parameter and some form of input / output device 1202 configured to communicate that sensor data to other a computing device across a network. The network may be a wired or wireless network within a home. As noted above, the sensor 101 and the computing device 102 may be integrated into a single package or device. The sensed parameter may be an environmental parameter, for instance temperature, pressure, humidity, gas (for instance, CO2), particulate matter, background pressure and differential pressure, proximity or motion, light, or distance or range. It will be appreciated that in other embodiments the sensor device 101 may include further components, for instance memory configured to buffer sensor data for periodic transmission and a processor to control the other parts.

[00130] Figure 13 illustrates a computing device 102 configured to receive and process sensor data to a computing device according to an embodiment of the present invention. The computing device 102 comprises a processor 1301, memory 1302 and some form of input / output device 1303 configured to communicate with the sensor 101 across a network. The memory 1302 stores executable instructions that, in response to execution by the processor 1301, cause the computing device 102 to perform the indoor space characterisation method of figures 8 and 9 or the calibration method of figures 10 and 11.

[00131] Referring now to figure 14, this is a flowchart illustrating a method of controlling a building system using a sensor according to an embodiment of the present invention. At step 1401 a sensor 101 senses an environmental parameter and passes data indicative of the environmental parameter to a computing device 102. The sensor 101 and the computing device 102 may be collocated or the data may be transmitted across a network. The computing device 102 may be incorporated into a building system controller configured to control components of a building system. In some examples, part of the processing may be performed at a computing device collocated with the sensor and part may be performed at a remotely located building system controller. The skilled person will understand that numerous permutations are possible concerning the distribution of computing resources and processes, and that the computing topology is less significant than the processing performed on the sensed environmental parameter.

[00132] At step 1402 the sensor data is processed to identify stable regions and change regions. The term “change regions” is intended to encompass trend regions and rapid change regions as described above. At step 1403 the identified data regions are interpreted to infer room states or events. The processing of steps 1402 and 1403 may be substantially as described above, particularly in connection with figures 8 and 9. Furthermore, this processing may be based on a prior calibration process performed for a particular sensor 101 disposed in particular room (for instance as illustrated in figure 3). The calibration process may be as described above in connection with figures 10 and 11.

[00133] At step 1404 the inferred room states or events are used to control a building system. The nature of the building system and the nature of the control should be interpreted broadly, and the following examples should not be considered limiting.

[00134] The building system may comprise any form of building system found within a building such as a home. As non-limiting examples the building system may be one or more of a building ventilation system; a building heating system; a building air conditioning system; a building security system; a building lighting system; ora building mechanical control system. A building mechanical control system may include actuators for opening, closing or adjusting any moveable part of a building, or instance windows, doors, curtains, blinds or room dividers. In particular, the building system may suitably be any building system where it is desirable to be able to take account of detected room states or events as described above. As an example, it is desirable to be able to take account of the number of people present within a room or whether doors or windows are open or closed when controlling a heating system.

[00135] Controlling a building system based upon the inferred room state or event may comprise controlling any controllable component of a building system on a per room or per building basis. For instance, for the example building systems set out above, any of the following system components could be controllable (and other examples will be readily apparent to the skilled person): a ventilation fan, a ventilation valve, a heating control, heating output device or valve, an air conditioning control, output device or valve, a light, or a security alarm, security light or security output signal to a remote device or monitoring service.

[00136] It will be appreciated that a building system, for instance a heating system, conventionally incorporates a building system controller for controlling the various components of the system. In certain examples of the present invention the building system controller may be modified to perform some, or all of the above-described processing of steps 1401 to 1403 based on received sensor data or partially processed sensor data. In other examples, the building system controller may be substantially conventional other than being configured to receive a control signal from a separate computing device that is configured to infer room states or events and generate a control signal.

[00137] In some cases, the sensor may be located within a single room and the building system is controlled in respect of the whole house based upon sensor data for that room. As an example, the sensor may be positioned in a central position within the home and configured to infer room states and events that may be indicative of activity throughout the home such that the building system may be controlled throughout the home. In other examples, as described below in connection with figure 15, sensors may be located in a plurality of rooms within a building allowing the building system to be controlled differently for each room.

[00138] The control of a building system according to step 1404 may comprise generating an output signal indicative of a building system state. This output signal may be communicated in any form. For instance, it may comprise the building system state being communicated to a separate control system which is configured to independently determine how a building system should be controlled (based optionally on further sensed or determined factors). In some cases, the output signal may be communicated to a building occupant, for instance by being displayed on a control panel or through an app on a mobile device. The building occupant may thus be empowered to manually control a building system. As an example, the control output may indicate that the detected number of people within a building or a room within the building has increased, permitting the user to determine forthemselves whetherto reduce a heating system output.

[00139] Alternatively, or additionally, the control of a building system according to step 1404 may comprise generating a control signal to control at least one component within a building system. As an example, the control signal may control a component of a heating system (such as a boiler or a thermostatic radiator valve) to turn on or adjust a temperature set point. More generally, the control signal may instruct at least one component of a building system: to turn on or off; to adjust a set parameter; to change a mode of operation or to perform any other appropriate action based on the nature of the building system and the nature of the detected room state or event within the room.

[00140] As for the previous section of the present document concerning the identification of room events and room states, the sensor is not limited to any specific type. Following a suitable calibration process, substantially any measurable environmental parameter may yield information from which room states and events can be inferred. This includes a temperature sensor, a pressure sensor, a particulate matter sensor, a humidity sensor, a gas sensor (for instance CO2), a background pressure and differential pressure sensor, a proximity or motion sensor, a light sensor, or a distance or range sensor. However, the present inventor has identified that a temperature sensor is particularly suited to determining room states such as the number of occupants, whether heating or cooling devices are active or whether doors or windows are open and determining room events such as a change in the number of occupants. These inferred room states and events are self-evidently pertinent to the control of a building system and temperature sensors may be readily and cheaply implemented within a building.

[00141] As explained in detail in the above description, a temperature sensor may be used to infer a room space state comprising a state in which the number of heat sources and heat sinks within the room remains static. Similarly, an inferred event that has occurred within the room may comprise one of: a heat source entering or being activated in the room; or a loss of heat being triggered in the room. A heat source entering or being activated in the room may comprise: an artificial heating system being activated within the room; or a person or animal entering the room. A loss of heat being triggered in the room may comprise: an artificial cooling system being activated in the room; an artificial heating system being deactivated within the room; an aperture, for instance a door or window, being opened allowing warm air to vent from the room; ora person or animal leaving the room. The above-described room characterisation method is able to differentiate between different room events based on parameters including the gradient of a change in temperature and a pattern of consecutive identified stable regions, trend regions and rapid change regions.

[00142] It is particularly important when controlling some building systems to be able to determine the number of occupants within a building or within a room or group of interconnected rooms within a building. It will be apparent to the skilled person that using a temperature sensor to infer room states and events it becomes possible to essentially count people entering or leaving a room and so keep a tally on the number of occupants. The determined number of occupants may be a key control input for a building system. For instance, for a heating system this may be used to control the amount of artificial heating required within a room or a building to maintain a desired temperature, by taking account of the thermal output of each person within the room. For a lighting system, where it is determined that the last occupant has left a room, section of a building or whole building then the lighting can be controlled accordingly, for instance by switching it off. For a security system, if a person is detected within a room at a time of day when people are not expected or previously programmed to not be permitted then this may trigger a security alarm. Other ways of using occupancy information to control building systems will be apparent to the skilled person.

[00143] Where the environment sensor is a temperature sensor, in addition to using sensor data to infer room states and events (based on changes in temperature) the temperature magnitude may also be used as a control input for a building system. As an example, for controlling a building heating system, the current ambient temperature is typically a required control parameter. This removes the need for an independent temperature sensor or thermostat for controlling the heating system.

[00144] Embodiments of the present invention concerning measuring room or building occupancy may be referred to as “quasi real-time occupancy”. This is because using a temperature sensor to detect building occupancy effectively looks back in time to recent changes (within the last few 10s of seconds or a few minutes) as it is dependent on measuring a change in temperature of a series of sensor readings. This contrasts with conventional approaches to measuring occupancy, such as using cameras or motion sensors, which are real-time, or near to real-time. Nevertheless, dependent on the building system being controlled, this may be immaterial. For instance, for a heating system, the response time from a control input to a measurable change in room temperature may be of the order of several minutes. Therefore, quasi real-time occupancy information is sufficiently rapid to be suitable for use as a control input. Furthermore, compared to using cameras to detect occupancy, using a temperature sensor to detect occupancy has numerous advantages: temperature sensors are much lower cost and so may be more widely deployed through a building (discussed below in connection with figure 15). Temperature sensors are lower power and less computationally expensive to process the generated data than for camera systems. As a consequence, and as noted above, this means that the processing for the sensor data may be performed by a low cost, local (embedded) processor. A further advantage relative to the use of cameras is that there is no privacy issue as no personally identifiable data is generated. This latter point is particularly relevant for public buildings, for instance, offices and hospitals.

[00145] Where occupancy is inferred from sensor data, occupancy patterns overtime may be established. This occupancy information may be used to predict future occupancy patterns, and predicted future occupancy may itself be used as a control input, for instance to turn on a building heating system in advance of a time of day or day of the week when it is normally expected that the building will be occupied. This may be relatively simplistic, such as establishing times and days when based on historical occupancy data it is more probable than not that the building will be occupied. In some examples of the present invention historical occupancy information built up over a period of weeks or months may be processed using a machine learning algorithm to make predictions of future occupancy. The output predictions may be validated using subsequent live occupancy information, which may be used to improve the algorithm.

[00146] Turning now to figure 15, this illustrates a plan view of a home 1501 in which a sensing system according to an embodiment of the present invention is deployed with a sensor in each room 1502. Specifically, figure 15 illustrates a building, particularly a home 1501, having four rooms 1502, each room 1502 having a sensor 1503. Sensor 1503 may correspond to sensor 101 described above. It may include an embedded computing device 102 performing some or all of the processing to infer room states and events or this may be performed by one or more remotely located computing devices (not illustrated). Inferred rooms states and events (wheresoever processed) may be communicated to a building system controller (not illustrated) for controlling a building system (not illustrated). Each room 1502 opens onto a corridor 1506 through a door 1505. Corridor 1506 may also include a sensor 1503. Each room 1502 further has a window 1504 which opens to outside of the building 1501.

[00147] In accordance with embodiments of the present invention the sensor 1503 in each room 1502 is configured to determine room states and events specifically for that room. This may include independently determining the number of occupants of each room 1502 and whether doors 1505 and windows 1504 are open. Advantageously, this allows a building system to be controlled on a per room basis. As an example, for a building heating system with a radiator or other heating appliance located in each room, each radiator may be controlled to provide an appropriate ambient temperature for each room according to the number of occupants (which may include turning off the heating in that room if the room is determined to be empty). Similarly, ventilation, cooling, lighting and security systems can be controlled on a per room basis according to occupancy information.

[00148] Figure 16 is a flowchart illustrating a method of controlling a building system using multiple sensors as deployed in separate rooms according to an embodiment of the present invention. At step 1602 for each sensor in each room sensor data is received (for instance at an embedded processor) and at step 1602 the sensor data is analysed to determined data regions. At step 1603 room states and events may be inferred individually for each room. At step 1604 this information is used to control a building system separately for each room. This building system control may be centralised. That is, the room states and events for each room may be processed or received at a central location - a building system controller - and used to control building system components separately for each room. Alternatively, some or all of the processing may be performed separately for each room. For instance, a processor embedded with a sensor 1503 in a given room may infer room state or event information and directly use that inferred information to control a building system component such as a heating appliance in that room without recourse to a central building system controller.

[00149] It will be understood that where a room is equipped with a sensor 1503 then occupancy information may be determined in respect of that specific room based upon the inferred room states or events. The building system may therefore be controlled based upon the determined occupancy information for each room. This may be controlling the building system separately for each room based on the determined occupancy forthat room, or controlling the building system for the entire building according to the aggregate occupancy information (or a combination of both).

[00150] As discussed above, embodiments of the present invention are able to infer events occurring within a room including whether a door or window is open or opened. According to a further embodiment, a door or window such as one or more of the doors 1505 and windows 1504 shown in figure 15 may also be equipped with sensors to determine whether they are open or closed. This may further be used as part of the control of a building system as it may give greater confidence in the inference of other events such as a person entering a room (by excluding the opening of a door or window when interpreting a detected change in temperature).

[00151] As for the embodiment described above in connection with figure 14, where multiple sensors are provided in separate rooms then occupancy information may be built up overtime for each room and used to predict future occupancy patterns. This allows the building system to be controlled based on predicted future occupancy.

[00152] It is known for conventional building control systems to take account of building occupancy determined through different means. For instance, it is known for the control of building heating systems to use mobile phone data for devices associated with the occupants to determine through geofencing whether that person is in the building, and use this to turn the heating on or off appropriately. Advantageously, embodiments of the present invention provide for a significant increase in the precision of locating occupants by allowing them to be determined to be in particular rooms within the building. This allows building systems to be more precisely controlled, on a per room basis, which can have positive benefits for the building occupants in terms of comfort, safety and cost of operating building systems.

[00153] According to a further embodiment of the present invention one or more sensor 1503 within a room 1502 may comprise a thermal sensor configured to detect heat sources as they pass through a sensor field of view. This allows movement of people through a building to be determined and further used to identify the rooms where people are located as part of controlling a building system. A sensor may also be configured to provide a measure of a distance to a thermal source within the field of view. This may further allow heat sources to be differentiated, for instance in the event that fixed heating appliances or sources of heat or heat loss (for instance, a window that may be opened) can be readily identified based on their known locations.

[00154] It will be appreciated that examples of the present invention can be realized in the form of hardware, software or a combination of hardware and software. Any such software may be stored in the form of volatile or non-volatile storage, for example a storage device like a ROM, whether erasable or rewritable or not, or in the form of memory, for example RAM, memory chips, device or integrated circuits or on an optically or magnetically readable medium, for example a CD, DVD, magnetic disk or magnetic tape or the like. It will be appreciated that the storage devices and storage media are examples of machine-readable storage that are suitable for storing a program or programs comprising instructions that, when executed, implement examples of the present invention.

[00155] Accordingly, examples provide a program comprising code for implementing apparatus or a method as claimed in any one of the claims of this specification and a machine-readable storage storing such a program. Still further, such programs may be conveyed electronically via any medium, for example a communication signal carried over a wired or wireless connection and examples suitably encompass the same.

[00156] Throughout this specification, the words “comprise” and “contain” and variations of them mean “including but not limited to”, and they are not intended to (and do not) exclude other components, integers or steps. Throughout this specification, the singular encompasses the plural unless the context otherwise requires. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise. Throughout this specification, the term “about” is used to provide flexibility to a range endpoint by providing that a given value may be “a little above” or “a little below” the endpoint. The degree of flexibility of this term can be dictated by the particular variable and can be determined based on experience and the associated description herein.

[00157] Features, integers or characteristics described in conjunction with a particular aspect or example of the invention are to be understood to be applicable to any other aspect or example described herein unless incompatible therewith. All of the features disclosed in this specification, and / or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive. The invention is not restricted to the details of any foregoing examples. The invention extends to any novel feature or combination of features disclosed in this specification. It will also be appreciated that, throughout this specification, language in the general form of “X for Y” (where Y is some action, activity or step and X is some means for carrying out that action, activity or step) encompasses means X adapted or arranged specifically, but not exclusively, to do Y.

[00158] Each feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features.

[00159] The reader’s attention is directed to all papers and documents which are filed concurrently with or previous to this specification in connection with this application and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference.

Claims

1. A method of controlling a system within a building, the method comprising: receiving sensor data from an environment sensor within a room in a building;5 identifying stable regions and change regions within the received sensor data;andinferring at least one of a room state or an event occurring within the room based on the identification;wherein the method further comprises:10 controlling a building system based upon the inferred room state or event; orsupplying information to a building control system for controlling the buildingsystem based upon the inferred room state or event; andwherein the environment sensor comprises one or more of:a temperature sensor;15 a pressure sensor;a particulate matter sensor;a humidity sensor; ora gas sensor.20 2. A method according to any one of the preceding claims, wherein controlling abuilding system comprises:generating a control signal to control at least one component within a building system;or generating an output signal indicative of a building system state.

253. A method according to claim 2, wherein the control signal instructs at least one component of a building system to turn on or off; to adjust a set parameter; or to change a mode of operation; orthe supplied information indicates to the building control system that at least one30 component of a building system should be turned on or off; have a set parameter adjusted; or have a mode of operation changed.

4. A method according to any one of the preceding claims, wherein the building 35 system comprises one or more selected systems or a combination of systems from a group including:a building ventilation system;a building heating system;a building air conditioning system;a building security system;a building lighting system; or5 a building mechanical control system.

5. A method according to claim 4, wherein controlling a building system based upon the inferred room state or event comprises controlling one or more of:a ventilation fan;10 a ventilation valve;a heating control, output device or valve;an air conditioning control, output device or valve;a light;a security alarm, security light or security output signal to a remote device or15 monitoring service; ora building mechanical actuator.

6. A method according to claim 4 or claim 5, wherein where the environment sensor is a temperature sensor the method further comprises uses the sensed temperature as a20 thermostat input for a building heating system.

7. A method according to anyone of the preceding claims, wherein where the environment sensor is a temperature sensor, an inferred room space state comprises a state in which the number of heat sources and heat sinks within the room remains static.

258. A method according to any one of the preceding claims, wherein where the environment sensor is a temperature sensor, an inferred event that has occurred within the room comprises one of:a heat source entering or being activated in the room; or30 a loss of heat being triggered in the room.

9. A method according to claim 8, wherein a heat source entering or being activatedin the room comprises:an artificial heating system being activated within the room; or35 a person or animal entering the room.

10. A method according to claim 8 or claim 9, wherein a loss of heat being triggered in the room comprises:an artificial cooling system being activated in the room;an artificial heating system being deactivated within the room;5 an aperture, for instance a door or window, being opened allowing warm air tovent from the room; ora person or animal leaving the room.

11. A method according to any one of the preceding claims, further comprising:10 determining occupancy information in respect of the room based upon the inferredroom state or event; andcontrolling the building system based upon the determined occupancy information.

12. A method according to any one of the preceding claims, further comprising:15 receiving sensor data from a plurality of environment sensors located within aplurality of rooms in a building;identifying stable regions and change regions within the received sensor data in respect of each room;inferring at least one of a room state for each respective room or an event20 occurring within each respective room based on the identification; andcontrolling a building system based upon the inferred room states or events in respect of each room.

13. A method according to claim 12, further comprising:25 determining occupancy information in respect of each room based upon theinferred room states or events; andcontrolling the building system based upon the determined occupancy information for each room.30 14. A method according to claim 12 or claim 13, wherein controlling the systemcomprises controlling the building system differently in respect of at least two rooms based upon the inferred room states or events in respect of those rooms.

15. A method according to any one of claims 12 to 14, further comprising:35 receiving data indicative of whether a door or a window opening onto at least oneroom with an environment sensor is open;wherein the control of a building system is further based on the received data.

16. A method according to any one of claims 11 to 15, further comprising predicting future occupancy of a room within a building based on determined occupancy and controlling the building system based on predicted future occupancy.

517. A method according to any one of the preceding claims, wherein identifying stable regions and change regions within the received sensor data comprises identifying within the received sensor data at least one:stable region indicative of a period of time within which the received data does not 10 vary by more than a magnitude threshold;trend region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient less than a gradient threshold; orrapid change region indicative of a period of time within which the received data trends upwards or downwards with a sensor data gradient greater than or 15 equal to the gradient threshold.

18. A method according to claim 17, wherein prior to the step of identification the method further comprises filtering the sensor data.20 19. A method according to claim 18, wherein the filtering comprising noise reductionand removal of fluctuations not indicative of change within the room.

20. A method according to claim 18 or claim 19, wherein the filtering comprises passing the sensor data through a weighted window processing filter.2521. A method according to anyone of claims 17 to 20, wherein prior to the step of identification the method further comprises curve fitting the sensor data or filtered sensor data to a polynomial curve.30 22. A method according to claim 21, wherein the sensor data or filtered sensor data isfitted to a polynomial curve with an order of two or more.

23. A method according to any one of claims 17 to 22, wherein identifying a stable region, trend region or rapid change region comprises processing the sensor data, filtered 35 sensor data or curve fitted sensor data using a region weighting mathematical expression.

24. A method according to any one of claims 17 to 23, wherein, for at least one identified stable region, inferring a room state comprises:identifying a sensor data magnitude or an average sensor data magnitude within the identified stable region;5 identifying a sensor data magnitude band containing the sensor data magnitudeor average magnitude; andinferring a room state based on the identified sensor data magnitude band;wherein the inference of a room state is based on a prior calibration process of measuring sensor data magnitudes for a plurality of known room states and determining10 sensor data magnitude bands forthose known room states.CO25. A method according to claim 24, wherein for at least one trend region the method further comprises:inferring that no event has occurred within the room state or that the room15 remains within a previously identified room state based on a prior calibration process of measuring sensor data magnitudes for a plurality of known room states and determining expected data trends or drifts in sensor data for those known room states over time.

26. A method according to any one of claims 17 to 25, wherein for at least one20 identified rapid change region inferring an event occurring within the room comprises: identifying a sensor data gradient or an average sensor data gradient within the identified rapid change region;identifying a sensor data gradient band containing the sensor data gradient or average gradient; and25 inferring that an event that has occurred within the room based on the identifiedsensor data gradient band;wherein the inference of an event that has occurred within the room is based on aprior calibration process of measuring sensor data gradients for a plurality of known events within the room and determining sensor data gradient bands forthose known events within30 the room.

27. A method according to claim 26, wherein inferring an event that has occurred within the room is further based upon an identified sequence of two or more different rapid change regions.

28. A method according to claim 27, wherein inferring an event that has occurred within the room is further based upon an identified sequence of two or more different rapid change regions having opposite polarity sensor data gradients.5 29. A method according to claim 28, wherein inferring an event that has occurredwithin the room is further based upon an identified sequence of one or more stable regions or trend regions and one or more rapid change regions.

30. A computer-readable storage medium having computer-readable program code10 stored therein that, in response to execution by a processor, causes the processor to perform the method of any one of the preceding claims.

31. An apparatus comprising a processor and a memory storing executable instructions that, in response to execution by the processor, cause the apparatus to 15 perform the method of any one of claims 1 to 29.2032. A control system for controlling a system within a building, the control system comprising:an environment sensor configured to generate sensor data indicative of an environment parameter within a room in a building; anda computing device configured to:receive sensor data from the environment sensor;identify stable regions and change regions within the received sensor data; and25 infer at least one of a room state or an event occurring within the roombased on the identification;wherein the computing device is further configured to:control a building system based upon the inferred room state or event; or supply information to a building control system for controlling the building30 system based upon the inferred room state or event; andwherein the environment sensor comprises one or more of:a temperature sensor;a pressure sensor;a particulate matter sensor;35 a humidity sensor; ora gas sensor.

33. A control system according to claim 32, further comprising:a plurality of environment sensors, each environment sensor being located within a separate room within a building and generating sensor data indicative of an environment parameter and providing the sensor data to the computing device;5 wherein the computing device is further configured to:identify stable regions and change regions within the received sensor data in respect of each room;infer at least one of a room state for each respective room or an event occurring within each respective room based on the identification; and10 control a building system based upon the inferred room states or events inrespect of each room.

34. A control system according to claim 33, wherein a separate computing device is associated with each environment sensor and configured to perform at least part of the 15 processing to infer at least one of a room state or an event occurring within the room.CO202535. A control system according to claim 34, further comprising a building system controller configured to:receive data from a computing device associated with each environment sensor, the data being indicative of:an environment parameter;identified stable regions and change regions within the sensor data;or inferred room states or events occurring within the room; andcontrol a building system based upon the inferred room states or events in respect of each room.

36. A control system according to any one of claims 32 to 35, wherein the computing device or the building system controller is further configured to perform the method of any one of claims 2 to 29.

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