Methods relating to energy consumption of a building environment

Abstract

Faults are diagnosed in energy consuming devices in a building environment by using a set of mappings, each corresponding to a portion of the energy-consuming devices. Physical parameter data from an

Description

Technical Field The present invention relates to methods for diagnosing faults with energy-consuming devices in a building environment and to methods for determining energy efficiency in a building environment. The present invention also relates to a computer-readable storage medium and a computer program of these methods, and a building environment in which the methods are implemented. Background The operation of buildings accounts for approximately 26% of global energy consumption. In particular, heating, ventilation and air conditioning (HVAC) systems amount to from about 20% to 50% of a commercial building’s energy consumption. Therefore, the building sector has a high environmental impact and there is significant potential to deliver substantial cuts in emissions, and in particular from HVAC systems. Buildings have many devices which consume energy, for example, computers, fans, printers, refrigerators and many others. A building environment may have multiple floors, with energy-consuming devices arranged on different floors of the building environment. To optimise the energy consumption of a building environment as a whole, the energy and performance of every energy-consuming device must be known. In order to measure the energy efficiency of individual devices, separate energy meters are typically required for each of the energy-consuming devices. These are expensive, complicated to install, and time-intensive to monitor. As a result, the overall efficiency of a building environment is difficult to measure, thereby making it hard to identify when the energy efficiency of a building environment is low. Low efficiency of a building environment can arise from broken or incorrectly operating energy-consuming devices, energyconsuming devices being used when not needed, or energy-consuming devices which are overcompensating due to the energy usage of other energy-consuming devices, among other reasons. Moreover, it is hard to improve the energy efficiency of a building environment when the energy efficiency of the energy-consuming devices in it, and how the energy-consuming devices are being used, are not known. As a consequence, many building environments operate at low efficiency. This low efficiency increases the energy usage, and gives building environments a high environmental impact due to energy required. In some building environments, the building management system (BMS) can provide alerts to control and monitor the mechanical and HVAC systems. Examples of common BMS providers include Trend Controls, Schneider Electric and Siemens. Alert systems are used to notify a user if there is a fault in one or more of the energy-consuming devices in the building. This allows maintenance requests to be raised to fix the energy-consuming device. In addition, there are automated fault detection and diagnostic systems which collect data from sensors, detect one or more faults of devices by comparing data under fault and normal conditions, and then diagnose the cause of those faults. However, these are limited in their usefulness as faults are pinned to a particular energy-consuming device, and do not consider nearby or otherwise related devices. In some building environments, the energy consumption of one or more of the energy-consumption devices may be monitored by the BMS. In other building environments, a separate Energy Management System (EMS) may be installed. Examples of common EMS providers include Schneider Electric, Honeywell and ABB. It is desirable to be able to improve the fault diagnostics in building environments in order to better understand and improve the energy efficiency of building environments. Summary The present invention is defined by the independent claims, with further optional features being defined by the dependent claims. In a first aspect of the invention, there is provided a method for diagnosing faults in a building environment, the building environment having a plurality of energy-consuming devices located within the building environment, wherein a set of mappings, each mapping corresponding to at least a portion of the plurality of energy-consuming devices, is stored in a memory; wherein the method is performed by a computer processor and comprises: receiving physical parameter data from a first energy-consuming device of the plurality of energy-consuming devices; applying at least one fault diagnostic rule to the physical parameter data; and if the physical parameter data does not satisfy the at least one fault diagnostic rule: identifying from the set of mappings in the memory, a first mapping that relates to the first energy-consuming device, wherein the first mapping corresponds to a first portion of the plurality of energy-consuming devices; and sending a control message to one or more energy-consuming devices of the first portion of the plurality of energy-consuming devices, to change at least one operating parameter. By receiving physical parameter data from a first energy-consuming device and applying a fault diagnostic rule to the physical parameter data received, it can be determined whether one of the energy-consuming devices has a fault. In particular, if the physical parameter data does not satisfy the fault diagnostic rule, it is indicative that one of the devices is not operating correctly. However, physical parameter data received from a first energyconsuming device may in fact indicate a fault in a different energy-consuming device. Therefore, by using a mapping containing the first energy-consuming device, the different relationships between the first energy-consuming device and other energy-consuming devices in the building environment can be determined, and a fault identified with another energy-consuming device in the mapping. For example, energy-consuming devices which are electrically connected or are located in the same physical location may be affected by each other. Then, a control message can be sent to one of the energy-consuming devices (which may be the first energy-consuming device or another energy-consuming device in the mapping) to change its operating parameter. As a consequence, the physical parameter data of the first energy-consuming device should be changed so that it falls within the normal operating parameters. Thus, the effect of the fault of one of the energy-consuming devices can be mitigated by the method of the present invention. As a result, any deviation from the normal operation of the energy-consuming devices can be quickly identified and fixed. Optionally, the building environment comprises one or more of: a building, one or more floors of a building, and one or more portions of one or more floors of a building. Optionally, the plurality of energy-consuming devices may have different physical points from which physical parameter data is received. Optionally, the plurality of energy-consuming devices comprises devices arranged in racks. In this way, the method is able to be used for different types of building environments, including large office buildings and data centres. Optionally, the physical parameter data comprises at least one of: fan speed, pump speed, heating valve position, cooling valve position, operating hours, temperature, and whether the device is enabled. These physical parameters enable different factors of performance to be monitored for the different energy-consuming devices, as appropriate. Moreover, these different physical parameters allow for different types of energy-consuming device to be monitored. Optionally, the method may further comprise creating an alarm for one of the first portion of energy-consuming devices if the physical parameter data does not satisfy the at least one fault diagnostic rule. Optionally, the method may further comprise outputting the alarm to a user associated with the building environment. The creation of an alarm and outputting the alarm to a user allows for the identification of energy-consuming devices which may have a fault, or energy-consuming devices which may be affected by a fault in a different device. Therefore, the user is able to track and monitor the faults. Optionally, the method may further comprise receiving an acknowledgement of the alarm from the user via a user input. The acknowledgement allows the system to keep track of alarms which have been created and actually received by the user. This ensures that faults are fixed quickly and enables easy identification of any faults which have not been fixed. Optionally, the at least one fault diagnostic rule defines an expected value or a range of expected values for the physical parameter data. Optionally, applying the at least one fault diagnostic rule comprises comparing the physical parameter data received from the first energy-consuming device to the expected value or the range of expected values. If there are no faults, the physical parameter data will likely fall within an expected range. Optionally, the at least one fault diagnostic rule defines an expected rate of change or a range of expected rates of change for the physical parameter data, wherein applying the at least one fault diagnostic rule comprises comparing the physical parameter data received from the first energy-consuming device to the expected rate of change or the range of expected rates of change. However, if there is a fault in one of the set of energy-consuming devices, the operation of that device, and / or other devices within a particular mapping may change. Therefore, the physical parameter data may no longer fall within the expected ranges and the fault can be identified. Optionally, an alarm is created if the physical parameter data received from the first energyconsuming device does not match the expected value or is not within the range of expected values for a predetermined period of time. Optionally, an alarm is created if the physical parameter data received from the first energy-consuming device does not match the expected rate of change or is not within the range of expected rates of change for a predetermined period of time. Therefore, an alarm can be created if the device is not performing as expected but does not create unnecessary alarms if expected values are only not met for short periods of time. For example, a change in environment such as increased occupancy of a room for a short period of time may cause an increase in power consumption or the like. It is not always desirable for an alarm to be created based on a temporary change in conditions. Optionally, the method may further comprise diagnosing the cause of the fault. By diagnosing the cause of the fault, it can be identified which device has a fault and how the device should be fixed. This allows the device to be fixed more quickly, so that the energyconsumption of the device returns back to the expected values. Optionally, the first energy-consuming device has a fault, wherein diagnosing the cause of the fault comprises identifying the first energy-consuming device. Alternatively, the first portion comprises a second energy-consuming device, wherein the second energyconsuming device has a fault, wherein diagnosing the cause of the fault comprises identifying the second energy-consuming device. In some circumstances, physical parameter data from a first energy-consuming device may not satisfy the fault diagnostic rule but a fault may have occurred in a second energy-consuming device. Therefore, by identifying that the fault lies in the second energy-consuming device, the second energyconsuming device can quickly be fixed. Optionally, the method further comprises determining a relationship between the first energy-consuming device and the second energy-consuming device from the first mapping. Optionally, the method further comprises creating an alarm for the second energyconsuming device. Optionally, the method further comprises storing imported data in memory, the imported data corresponding to at least one of the plurality of energy-consuming devices. Optionally, the imported data is stored in memory in a structured format. Optionally, the imported data comprises at least one of: manufacturing specification data or historic data relating to at least one of the plurality of energy-consuming devices. Manufacturing specification data and historic data enable accurate fault diagnostic rules to be established for the energyconsuming devices, as the fault diagnostic rules will be based on data specific to the type (e.g. brand, model number) of energy-consuming device. This improves the accuracy of the fault diagnostic detection. Optionally, the method further comprises analysing the historic data and identifying a first trend. Analysing the historic data to identify trends enables the processor to determine the normal operating ranges for the energy-consuming devices (i.e. the operating range which is to be expected when the energy-consuming device is operating without a fault), thereby allowing for more accurate fault detection. Optionally, the method may further comprise comparing the at least one operating parameter with the first trend. Comparing the at least one operating parameter with the first trend enables the processor to determine whether the first energy-consuming device is operating in a normal range. Optionally, the manufacturing specification data is converted into a common format. This allows data from different manufacturers to be used in the same energy efficiency equations and / or to be compared to one another. Optionally, the method may further comprise changing the at least one operating parameter based on the manufacturing specification data. Changing the operating parameter(s) based on the manufacturing specification data ensures that the devices are controlled appropriately and within their operating ranges, thereby reducing the likelihood of risk of any further faults. Moreover, in some scenarios, certain sub-ranges may be selected to enhance energy efficiency. Optionally, diagnosing the cause of the fault comprises analysing physical parameter data from one of the plurality of energy consuming devices over a period of time. Optionally, the method may further comprise using the physical parameter data in a predetermined energy efficiency equation. Optionally, the method may further comprise outputting the energy efficiency of at least one of the plurality of energy-consuming devices. By outputting the energy efficiency, a user is able to track and monitor the energy efficiency of an energyconsuming device. As a result, the user may be able to identify which devices have low efficiency. This allows the user to understand why a building environment is inefficient and how to improve it. Optionally, the energy efficiency of the at least one of the plurality of energy-consuming devices is a percentage efficiency. Optionally, the processor creates an alarm if the energy efficiency of the at least one of the plurality of energy-consuming devices is below a threshold level for a predetermined period of time. If the energy efficiency of an energyconsuming device is lower than a threshold, this will affect the energy efficiency of the building environment. Therefore, an alarm will indicate to a user that there may be a fault associated with that device, or that the device is inappropriate for the task required. Optionally, the method may further comprise suggesting modifications to control algorithms used by the computer processor based on the energy efficiency of at least one of the plurality of energy-consuming devices. Optionally, the method may further comprise calculating energy consumption savings based on the modifications made to the control algorithms. Optionally, the method may further comprise modifying the sequencing of the plurality of energy-consuming devices to reduce energy consumption. Optionally, the manufacturing specification data comprises fixed parameter data. Optionally, the fixed parameter data comprises at least one of: fan power, pump power, coefficient of performance, heating capacity, cooling capacity, heating load and unit power. Further optionally, the fixed parameter data comprises heating load data and the coefficient of performance for the at least one energy-consuming device, wherein total energy input for the at least one energy-consuming device is determined by dividing the heating load data by the coefficient of performance. Optionally, the physical parameter data further comprises fan power and pump power of the at least one energy-consuming device, wherein total power consumption is determined by adding the total energy input, the fan power and the pump power. In a second aspect of the invention, there is provided a method comprising determining energy efficiency in a building environment, the building environment having a plurality of energy-consuming devices located within the building environment, wherein a set of mappings, each mapping corresponding to at least a portion of the plurality of energyconsuming devices, is stored in a memory; wherein the method is performed by a computer processor and comprises: receiving physical parameter data from at least one of the plurality of energy-consuming devices; using the physical parameter data in a predetermined energy efficiency equation; outputting the energy efficiency of one of the plurality of energyconsuming devices. By using the physical parameter data in an energy efficiency equation for an energyconsuming device, the energy efficiency of the entire building environment can be determined. The energy efficiency equations can also be used in combination with the fault detection system of the first aspect in order to monitor and fix the energy-consuming devices, to reduce any adverse effects of faults which occur. Optionally, the physical parameter data comprises an excess operating time of the at least one energy-consuming device and operational data for the at least one energy-consuming device, wherein wasted energy of the at least one energy-consuming device is determined using the excess operating time and the operational data. In a third aspect of the invention, there is provided a building environment having a plurality of energy-consuming devices located within the building environment and a computer processor, the computer processor configured to perform the method of any preceding claim. In a fourth aspect of the invention, there is provided a computer processor configured to perform the method of any of the aspects above. In a fifth aspect of the invention, there is a computer-readable storage medium comprising instructions which, when executed by a computer processor, are configured to cause the computer processor to perform the method of any of the aspects above. In a sixth aspect of the invention, there is provided a computer program with instructions which, when executed by a computer processor, are configured to cause the computer processor to perform the method of any of the aspects above. Brief Description of the Drawings Embodiments of the invention are described below, by way of example, with reference to the following drawings, in which: FIG. 1 illustrates an exemplary building environment in which the invention may be implemented. FIG. 2 is a schematic illustrating a set of energy-consuming devices, and the mapping or relationships between the different energy-consuming devices. FIG. 3 is a flowchart detailing steps of a method of the present invention for diagnosing faults in a building environment. FIGS 4A-4C illustrate exemplary user interfaces for creating a fault diagnostic rule which can be used with the method of the present invention. FIG. 5 is a schematic illustrating the mapping between the plurality of energy-consuming devices, and the communicative coupling between each energy-consuming device and a computer. FIG. 6 is a schematic illustrating the mapping between the plurality of energy-consuming devices, and the communicative coupling between each energy-consuming device and a remote computer. FIGS 7A-7E illustrate exemplary user interfaces which can be used with the method of the present invention. FIG. 8 is a flowchart detailing steps of a second method of the present invention, wherein the method is for determining energy efficiency in a building environment. FIG. 9A is an exemplary user interface illustrating energy consumption over time for a building environment 100. FIG. 9B is a graph illustrating electric energy consumption over time for a first energyconsuming device, for example, a fan coil unit. FIG. 10 depicts a computer for implementing the methods of the invention. Detailed Description Buildings serve a number of societal needs, for example, shelter, storage, living space and space for working, such as an office. Buildings, especially large high-rise apartment buildings or offices, require significant internal infrastructure to function. For example, systems that may be required include heating, cooling, power, telecommunications, water and waste systems. These systems can take up large amounts of space and require regular maintenance. Among the internal infrastructure, there is typically a plurality of energyconsuming devices that are responsible for the building to function. The term “energyconsuming device” as used herein, is any device forming part of the infrastructure of a building that consumes electrical energy. Typically, such devices are plugged into the electrical system of the building. FIG. 1 illustrates an example of a building environment 100. The term “building environment” as used herein, is a reference to a building (or at least a part of said building) and the collection of energy consuming devices within said building. A building environment 100 may comprise a plurality of floors, and each floor may be split into one or more portions. As is shown in the example in FIG. 1, the building environment 100 comprises five floors 101,102, 103, 104, 105. In this example, floor 101 is used as a server room, floors 102-104 are used for small, separated offices (i.e. portions 102A, 102B, 102C of floor 102, portions 103A, 103B and 103C of floor 103, portions 104A, 104B and 104C of floor 104), and floor 105 can be used for larger occupancy meeting or conference rooms. Within each floor (or each portion of each floor), there may be a number of energy-consuming devices, such as an HVAC (heating, ventilation and air-conditioning) system, in addition to, telecommunication devices, printers and many more. FIG. 2 illustrates an example set of energy-consuming devices 200 having three energyconsuming devices 201, 202, 203. FIG. 2 also illustrates a mapping showing the relationship between the exemplary energy-consuming devices 201, 202,203. For instance, the energyconsuming devices 201,202, 203 may all be present on the same floor 101. Energy-consuming devices which are in the same location, such as in the same room, may affect each other’s power consumption. For example, a faulty component may cause an increase in power consumption of a first energy-consuming device 201, which increases the heat output of the first energy-consuming device 201. The increased heat output may in turn cause a second energy-consuming device 202, such as an air conditioning unit to increase its fan speed to cool the room back down to the desired temperature. In other words, a fault in the first energy-consuming device 201 may cause a subsequent effect on the power consumption on the second energy-consuming device 202. There are also other relationships between energy-consuming devices 200, such as via direct electrical connections. As an example, a building 100 may comprise a chilled water system. The chilled water system may comprise a primary pump located on the second floor 102 of the building 100. The primary pump may feed a secondary pump which is located on the fourth floor 104 halfway up the building. If the primary pump is not operating properly, the secondary pump may need to operate at a higher speed to compensate and therefore the secondary pump will consume more energy. Therefore, in the example of FIG. 2 where the energy-consuming devices 201,202, 203 are on the same floor 101, a first mapping between each of the devices 201, 202, 203, as illustrated by the connecting lines 210, 211, 212, is determined. In general, each mapping logically links two or more of the plurality of energy-consuming devices 200. Such mappings are predetermined, i.e. prior to the methods of the invention commencing, and are stored in a memory. The energy-consuming devices 200 within a particular mapping may be referred to herein as the “set of energy-consuming devices”. The number of energy-consuming devices 200 in the set of energy-consuming devices 200 (i.e., in the mapping) is fewer than the number of energy-consuming devices in the plurality of energy-consuming devices 200 (i.e. in the building environment 100). In other words, the set of energy-consuming devices 200 is a portion of the plurality of energy-consuming devices 200. In other examples, the set of energy-consuming devices 200 in one mapping may have any number of energy-consuming devices 200. Additionally, any one energy-consuming device may be in multiple mappings. For instance, one particular energy-consuming device 200 may have one mapping relating to the room and one mapping relating to the floor of the building environment 100. Alternatively, one particular energy-consuming device 200 may have multiple mappings relating to different functions of the building environment 100, such as heating, cooling or power. In other examples, the energy-consuming devices 200 may be arranged in racks, and may have a mapping between energy-consuming devices 201, 202, 203 by virtue of their electrical connections and / or positions within the rack. Example energy-consuming devices 200 that may be arranged in racks include servers, network equipment, storage system, power distribution units, cooling equipment, patch panels, KVM switches, blade servers, telecommunication equipment, backup systems, audio / video equipment, and the like. There may be a set of mappings, each relating to different portions of the plurality of energyconsuming devices within the building environment 100. For example, referring back to FIG. 1, a second mapping may relate to energy-consuming devices on the floor 103. A further example is a third mapping between first and second HVAC units which are situated on different floors of the building 100. In another example, a mapping may relate to an energy-consuming device and its upstream energy-consuming devices and / or its downstream energy-consuming devices. For example, a second energy-consuming device may receive electrical power from a first energyconsuming device. The second energy-consuming device cools fluid and then supplies the cooled fluid to a third energy-consuming device. In this example, the second energyconsuming device is a downstream energy-consuming device of the first energy-consuming device. Similarly, the third energy-consuming device is a downstream energy-consuming device of the second energy-consuming device. In other words, the upstream energyconsuming device of the second energy-consuming device, i.e. the first energy-consuming device, supplies the second energy-consuming device. The second energy-consuming device then supplies its downstream energy-consuming device, the third energy-consuming device. In another example, a mapping may relate sibling energy-consuming devices. Sibling energy-consuming devices have at least one common upstream energy-consuming device. For example, a first energy-consuming device may supply power to a second energyconsuming device and a third energy-consuming device. The second energy-consuming device and the third energy-consuming device are sibling energy-consuming devices, and both are fed by the first energy-consuming device, which is the upstream energy-consuming device. The set of mappings may be stored in a memory, as further discussed herein. FIG. 3 is a flowchart detailing the steps of a method for diagnosing faults in a building environment, such as for example the building illustrated in FIG. 1. The method is performed by a computer processor which can be configured as a remote computer, or within the building itself, as discussed with respect to FIG. 5 and FIG. 6. Before the method commences, the plurality of energy-consuming devices 200 in the building are communicatively coupled with the computer processor. Such communicative coupling is discussed further with respect to FIG. 5 and FIG. 6. Then, once the energyconsuming devices 200 in the plurality of energy-consuming devices 200 are identified, data identifying the plurality of energy-consuming devices 200 are stored in memory. Such data may include various information about the energy-consuming device, such as device name or ID, device manufacturer, device type or model, device status (online, offline), location within the building environment 100, which system the device belongs to, relationships with other devices, expected data points, fan power, pump power, coefficient of performance, energy efficiency ratio and seasonal energy efficiency ratio. Additionally, the set of mappings for the plurality of energy-consuming devices are determined and stored in memory. The set of mappings are based on the physical relationships between the plurality of energy-consuming devices 200 in the building environment 100, as discussed with respect to FIG. 2. Other relationships other than location may also be mapped. Such mappings are typically determined manually based on knowledge of the building environment 100. At step 301, the method comprises receiving physical parameter data from at least one energy-consuming device, for example from those illustrated in FIG. 2. The receiving of physical parameter data may be in reply to a request from the computer processor (i.e. pulled physical parameter data) or without a request from the computer processor (i.e. pushed physical parameter data). Such receiving is preferably periodic. For instance, the physical parameter data may be received every 1 minute, 2 minutes, 5 minutes, 10 minutes, 20 minutes, 30 minutes, 60 minutes, etc. Different periods are suitable for different types of energy-consuming device. The term “physical parameter data” as used herein represents a physical attribute at or caused by an energy-consuming device. For example, physical parameter data at the energy-consuming device may comprise at least one of fan speed, pump speed, heating valve position, cooling valve position, operating hours, temperature and whether the device is enabled. Other physical parameter data may of course be used, depending on the type of energy-consuming device 200. In general, such data may be used to determine the energyefficiency or energy use of the energy-consuming device 200, as further discussed herein. Physical parameter data caused by the energy-consuming device may include temperature, power consumption, and the like. The energy-consuming devices 200 may (each) have different physical points from which physical parameter data is received. For example, if the energy-consuming device is a fan, the physical parameter data may be fan speed received from the motor and / or energy usage at the plug. Some energy-consuming devices may have communicative capabilities and therefore be able to send such data to the computer processor without further devices being present. Other energy-consuming devices, typically legacy energy-consuming devices, may not have communicative capabilities and for these types of devices, such data can be obtained by, for example, using temporary clamp meters that have communicative capabilities. At step 302, the method comprises applying at least one fault diagnostic rule to the physical parameter data. The method of the present invention may also comprise, prior to the method commencing, creating one or more fault diagnostic rules, and storing such rules in the memory. The fault diagnostic rules may define an expected value or a range of expected values for the physical parameter data. For example, a fault diagnostic rule may define a temperature range of ±10°C around a temperature setpoint (desired temperature) of 25°C. In other examples, the fault diagnostic rule may define expected ranges of fan speed, or pump speed, or may define an expected value of heating valve position. The computer processor may be configured to assign appropriate fault diagnostic rules to the energy-consuming device 200. The computer processor may also be configured to assign more than one fault diagnostic rule to the energy-consuming devices 200. For example, referring back to FIG. 2, a first fault diagnostic rule relating to pump speed and a second fault diagnostic rule relating to fan speed may both be applied to an energy-consuming device 201. The computer processor may also be configured to apply the same fault diagnostic rule to more than one energy-consuming device 200. In some embodiments, the computer processor may be configured to apply the same fault diagnostic rule to all of a type of energy-consuming device. For example, a fault diagnostic rule specifying the expected fan speed range may be applied to some or all of the energy-consuming devices 200 which have a fan. In another example, a particular temperature setpoint may be applied to a particular floor of the building environment 100, e.g. floor 101 of FIG. 1. In another embodiment, the fault diagnostic rule may define an expected rate of change of the values of the physical parameter data. An abrupt change in a parameter (i.e. a rate of change over a threshold value), for example a large decrease in temperature or a large increase in pressure, may indicate a fault. In another embodiment, the fault diagnostic rule may be established by comparing physical parameter data from the same type of energy-consuming devices 200. For example, the fault diagnostic rule may define an expected range of values for the physical parameter data from a first energy-consuming device 201 relative to the physical parameter data from a second energy-consuming device 202 (in this example, the first energy-consuming device 201 being the same type as the second energy-consuming device 202). Therefore, this may allow inconsistencies in performance between similar energy-consuming devices 200 to be identified. In another embodiment, the fault diagnostic rule may be established by comparing physical parameter data from different energy-consuming devices 200 within a building environment. The fault diagnostic rule may comprise multiple conditions, wherein each condition relates to a different energy-consuming device 200. Alternatively, multiple fault diagnostic rules may be established, wherein each fault diagnostic rule relates to a different energy-consuming device 200. For example, a first energy-consuming device 201 may provide a heating effect, whilst a second energy-consuming device 202 may provide a cooling effect, which may indicate a fault which would not be identified by considering a fault diagnostic rule relating to only the first energy-consuming device, or only the second energy-consuming device. In another embodiment, the fault diagnostic rule may be established by analysing physical parameter data from one of the plurality of energy-consuming devices 200 over a period of time to create historical data and / or a predictive model, the rule then involving comparing the physical parameter data to historical data or predictive models. Therefore, expected values or ranges for expected values for physical parameter data can be determined from the historical data and / or predictive model in order to define the fault diagnostic rule. Various methods for creating a predictive model from physical parameter data are known in the art. At step 303, the method comprises determining whether the physical parameter data satisfies the at least one fault diagnostic rule. When the rule is an expected value or a range of expected values, this determination is performed by comparing the physical parameter data to the expected value or range of expected values. If the physical parameter data falls outside of the expected value or range of values, then it does not satisfy the at least one fault diagnostic rule, and the method moves to step 304. If the physical parameter data falls within the expected value or range of expected values, then it is considered to satisfy the at least one fault diagnostic rule, then no further action is taken. Then, periodically, steps 301 to 302 will be repeated for new physical parameter data received from the energy-consuming device 200. Notably, at step 303, it is determined whether the physical parameter data satisfies at least one fault diagnostic rule. This means that the physical parameter data may be used in more than one fault diagnostic rule. For each fault diagnostic rule, a separate step 304 may be performed. If the physical parameter data received from a first energy-consuming device 201 does not satisfy the at least one fault diagnostic rule, step 304 of FIG. 3 is performed. Step 304 comprises identifying from the set of mappings in the memory, a first mapping that relates to the first energy-consuming device 201, wherein the first mapping corresponds to a first portion of the plurality of energy-consuming devices 200. For example, if the temperature received from a first energy-consuming device 201 is outside of the expected temperature range 15°C - 35°C, such as a temperature reading of 40°C, the physical parameter data has not satisfied the fault diagnostic rule. In such a case, the mapping containing the first energyconsuming device 200 is retrieved from memory. There are various methods for retrieving the appropriate mappings from memory, including performing a query for all mappings containing a particular energy-consuming device ID or the like. If the physical parameter data does not satisfy the at least one fault diagnostic rule, this may indicate a fault of one of the energy-consuming devices of the first portion of energyconsuming devices 200. In particular, if the physical parameter data from a first energyconsuming device 201 does not satisfy a first fault diagnostic rule, this may indicate a fault in the first energy-consuming device 201. It may alternatively indicate a fault in a second energy-consuming device 202, or faults in a plurality of energy-consuming devices 200. A mapping is identified which relates to the first energy-consuming device 201 and maps the relationships between the first energy-consuming device 201 and other energyconsuming devices 202, 203. For example, the identified first mapping may relate the first energy-consuming device 201 to the other energy-consuming devices on the same floor 101 or in the same room as the first energy-consuming device 201. The method may further comprise determining a relationship between the first energy-consuming device 201 and the second energy-consuming device 202 from the first mapping. In an example, a fault diagnostic rule may comprise multiple conditions. For example, a fault diagnostic rule may be assigned to a fan coil unit (FCU) which monitors for ineffective cooling. The fault diagnostic rule may require an expected range of fan speed of a discharge fan, an expected value of the cooling valve, and an expected temperature of the room. In this example, when the fault diagnostic rule is applied to the fan coil unit, there may be a fault if the fan speed and cooling valve satisfy the conditions of the fault diagnostic rule, but the room temperature is greater than two degrees of the expected temperature of the room. This may indicate that the FCU is not cooling effectively. At step 305, the method further comprises sending a control message to one or more energy-consuming devices 200 of the first portion of the plurality of energy-consuming devices 200, to change at least one operating parameter (of the one or more energyconsuming devices 200). For example, a temperature reading may indicate that a HVAC unit is cooling a room to a lower temperature than is required, particularly if that room is unoccupied. The computer processor may send a control message which changes the temperature setpoint (desired temperature) or changes the range of temperatures in which the room is not heated or cooled. In another example, there may be a control message which can allow more airflow into a space based on air quality levels. The fault diagnostic rule specifies an acceptable range of CO2 levels. If the CO2 levels do not satisfy the fault diagnostic rule, the control message may increase the airflow into the space. In other examples, the computer processor may send a control message to increase or decrease fan speed, pump speed, operating hours, and temperature, or to change the heating valve position, the cooling valve position, or change whether the unit is enabled. The processor may send the same control message to one or more energy-consuming devices 200 or may send different control messages to each energy-consuming device 201, 202, 203. If a control message which is sent to an energy-consuming device does not resolve the fault, a ticket can be raised and sent to a user for further investigation. The method may further comprise storing imported data, the imported data corresponding to at least one of the plurality of energy-consuming devices 200. The imported data may be manufacturing specification data or historic data relating to at least one of the plurality of energy-consuming devices 200. In other examples, the imported data may be predictive models relating to the performance of at least one of the plurality of energy-consuming devices 200. The operating parameter may be changed based on the manufacturing specification data. For example, a control message to change the fan speed of a first-energy consuming device 201 should not change the fan speed to a speed outside of the range that is recommended for the first-energy consuming device 201. This allows the method to be tailored to a specific building environment 100, by taking into account the specific energy-consuming devices which are used. Step 305 may further comprise diagnosing the cause of the fault. In some embodiments, step 305 may further comprise identifying the first energy-consuming device 201, i.e. identifying the first energy-consuming device 201 as having a fault. In an example, the computer processor receives physical parameter data which indicates that the fan speed of a first energy-consuming device 201 is outside of the expected range. If the processor also receives physical parameter data from other energy-consuming devices 200 that indicate that the other energy-consuming devices 200 are operating in expected ranges, there may be a fault in the first energy-consuming device 201. For example, the fault in the first-energy consuming device 201 may be a filter which needs cleaning. Additionally or alternatively, step 305 may comprise identifying an energy-consuming device in the set of energy-consuming devices 200 in the first mapping, other than the first energyconsuming device 291, as having a fault. For example, if a first fan coil unit (FCU) out of a group of multiple FCUs has an increase in thermal energy usage, whilst the other FCUs all have constant thermal energy usage, this may indicate an issue with the valve or actuator on the first FCU. The method may comprise diagnosing the cause of the fault by analysing physical parameter data from one of the plurality of energy-consuming devices 200 over a period of time. The computer processor may diagnose the cause of the fault by comparing the physical parameter data to historical data or predictive models. Diagnosing the fault may comprise identifying correlations between the fault and specific components or conditions of the first energy-consuming device 201. FIG. 4A-4C illustrates an exemplary user interface pop-up which can be utilised when creating a fault diagnostic rule. The user interface contains a plurality of fields 401-409 which are editable by a user, i.e. so that the user can input or select text. Some of the plurality of fields, such as fields 401,402 and 403 are always displayed. The ‘Groups’ field 401 allows a user to apply a fault diagnostic rule to a group of energy-consuming devices. For example, a fault diagnostic rule may be applied to all components within a system, or to all energyconsuming devices which comprise a particular component, such as a valve. The ‘Name’ field 402 allows a user to give a name to describe the fault diagnostic rule. The ‘Asset type’ field 403 allows a user to specify which energy-consuming device, or which type of energyconsuming device the fault diagnostic rule should be assigned. Other fields may be specific to a particular tab. FIG. 4A shows the user interface with the ‘Rule definition’ tab, FIG. 4B shows the user interface with the ‘Rule actions’ tab, and FIG. 4C shows the user interface with the “Related FDDs” tab. In FIG. 4A, which shows the ‘Rule Definition’ tab, the ‘Rule definition’ field 404 allows a user to provide a calculation for the fault diagnostic rule. As mentioned previously, the calculation may define an expected value or expected range of values for physical parameter data of an energy-consuming device. The ‘Trigger’ field 405 allows the user to define a duration (e.g. in seconds) for which the physical parameter data must not satisfy the fault diagnostic rule before a control message is sent or a ticket is generated. FIG. 4B illustrates the user interface, showing the ‘Rule actions’ tab instead of the ‘Rule definitions’ tab. The user may fill in the ‘Rule definitions’ tab and then fill in the ‘Rule actions’ tab. The ‘Rule actions’ field 406 allows a user to specify a control message to be sent to one or more of the plurality of energy-consuming devices 200 once a rule is triggered. The ‘Email’ field 407 is used to specify an email address of a user to be alerted about the rule being triggered. The ‘Auto Ticketing’ field 408, which takes the form of a tick box in FIG. 4B, is an option to automatically create a ticket in addition to the alarm when the rule is triggered. FIG. 4G illustrates the user interface, showing the ‘Related FDDs’ tab. The ‘Related FDDs’ tab may comprise three buttons: an ‘Add All Ancestors’, an ‘Add Immediate Ancestors’ and an ‘Add Siblings’ box. The ‘Add All Ancestors’ box allows a user to add all fault diagnostic rules for all upstream energy-consuming devices to the ‘Related FDDs’ box. The ‘Add Immediate Ancestors’ button allows a user to add all fault diagnostic rules for only directly upstream energy-consuming devices to the ‘Related FDDs’ box. The ‘Add Siblings’ button adds all fault diagnostic rules for all energy-consuming devices which downstream of the same energy-consuming devices to the ‘Related FDDs’ box. This enables the relationships between energy-consuming devices to be utilised to see patterns of the fault diagnostic rules which have not been satisfied. FIG. 5 is a schematic illustrating the mapping between the plurality of energy-consuming devices 201, 202, 203, shown as connections 210, 211, 212, and the communicative coupling between each energy-consuming device 201,202,203 and a computer 400, shown as coupling 410, 420 and 430. The plurality of energy-consuming devices are communicatively coupled to the computer 400 via a least one communication network, such as the Internet, BACnet / IP, Modbus TCP &RTU, KNX, OPC, LoraWAN, MQTT &SNMP, DALI &DALI 2. Other communication networks may be used including a local area network (LAN) and / or a wide area network (WAN). Further communication networks may be present in various types of computer 400, such as mobile devices and tablets, to cellular networks, such as 3G, 4G LTE and 5G. Although FIG. 5 shows direct connections between each of the plurality of energy-consuming devices 200 and the computer 400, the connections may also be indirect. Put another way, not all of the energy-consuming devices 201, 202 and 203 may be connected directly with computer 400, instead device 201 may be connected to device 203, which is in turn connected to computer 400. In this embodiment for implementing the invention, the computer 400 is located in the building environment 100, and comprises a computer processor and memory, as discussed with respect to FIG. 10. The method discussed with respect to FIG. 3 may be performed on computer 400. FIG. 6 is an alternative schematic illustrating the mapping between the plurality of energyconsuming devices 201, 202, 203, shown as connections 210, 211, 212, and the communicative coupling between each energy-consuming device 201, 202, 203 and a remote computer 500, shown as coupling 510, 520 and 530. The difference between FIG. 6 and FIG. 5 is that in FIG. 6 the remote computer 500 is not within the building environment 100. Instead, at least one communication network 550, preferably the Internet, is present to connect the remote computer 500. This particular set up is useful if the method discussed with respect to FIG. 3 is provided by a third party (i.e. not the building owner). It is also useful for managing the building environment 100 remotely. In this embodiment, the method of FIG. 3 is performed by remote computer 500. A combination of the embodiments of FIG. 5 and FIG. 6 is also possible. That is, there may be a computer 400 present in the building environment 100 in addition to a remote computer 500 present outside of the building environment 100. In such an embodiment, the method of FIG. 3 may be performed entirely on either computer 400 or remote computer 500. Alternatively, the method may be performed by computer 400 in conjunction with remote computer 500. In general, computer 400 offers improved security due to its physical location within the building environment 100, while remote computer 500 provides the ability to remotely control the building environment 100. FIGS 7A-7D illustrate different views of an exemplary user interface 600 which can be used with the method of the present invention. The user interface 600 may be hosted by either computer 400 of FIG. 5 or remote computer 500 of FIG. 6. FIG. 7A illustrates a ‘Building Overview’ user interface. The ‘Building Overview’ user interface provides a summary of the energy-consuming devices 200 of a building environment 100. For example, the information provided on the ‘Building Overview’ interface may include the total energy consumption of the building, the number of alarms which have been created, the number of alarms with each priority level. Other information may include the air quality of different floors of a building environment 100. The computer processor may create one or more alarms 610 for display on the user interface 600. There are various reasons that the computer processor may create an alarm 610. In some examples, the computer processor may create an alarm 610 if the physical parameter data received from the first energy-consuming device 201 does not satisfy the at least one fault diagnostic rule. In other examples, the processor may create an alarm 610 for one of the first portion of energy-consuming devices 200 if the physical parameter data does not satisfy the at least one diagnostic rule. In some examples, the processor may create an alarm 610 id the physical parameter data is not within the range of expected values for a predetermined period of time. Accordingly, the method of FIG. 3 may further comprise the computer processor outputting the alarm 610 to a user associated with the building environment 100, i.e. via the user interface 600 of the computer 400 of FIG. 5 or remote computer 500 of FIG. 6. The user may view the alarm 610, and any other alarms which have been outputted via the user interface 600. The user interface 600 may categorise the alarms 610, for example by location. In addition or alternatively, the computer processor may categorise the alarms 610 based on priority levels, and the user interface 600 may display the alarms 610 to the user based on the priority level of each alarm 610. For example, the alarms 610 in the highest priority level may be displayed above alarms 610 in lower priority levels in a list. The alarms 610 may be assigned to different users, wherein the user interface may show a user all of the outputted alarms or may only show a user the alarms which have been output to the particular user. Optionally, there may be a toggle button on the user interface 600 to toggle between showing all alarms and only alarms assigned to the user viewing the user interface. To this end, the user interface 600 may also provide functionality so that the user can log in to their user account. The user may submit an acknowledgement of the alarm, via the user interface 600, to indicate that the fault has been seen and / or addressed. For example, the user may replace a component of a first energy-consuming device 201, or reset the first energy-consuming device 201. After which, because the fault will have been resolved, the user can submit an acknowledgement of the alarm and subsequently have it removed from the user interface 600. Suitable options may be provided in the user interface 600 for the user to interact with each of the alarms. Different users may have restricted permissions and may not be able to remove any alarm that appears on the user interface 600. The user interface 600 may also be used to display the set of mappings, and each mapping in the set of mappings. Furthermore, the set of mappings, and / or any of the mappings may be adapted by the user via the user interface 600. For instance, a user may create a new mapping via a user interface when a new relationship between several energy-saving devices 200 is identified. In another example, the user may edit an existing mapping to remove one or more energy-saving devices that have been permanently removed from the building environment 100. The exemplary alarm 610 of FIG. 7B indicates that the fault diagnostic rule which is named ‘supply_fan_alarm_status_runtime_exceeded’ was not satisfied by the physical parameter data received from a first energy consuming device 200. The alarm 610 provides a priority level (‘Medium’), a time at which the fault diagnostic rule failed, and provides an option for the user to acknowledge the alarm 610. Alarms 620, 630 and 640 illustrate further alarms which may be output on other energyconsuming devices 200. FIG. 70 illustrates an exemplary user interface which can be used to view and acknowledge tickets which have been raised. As discussed previously, if a control message does not resolve the fault, a ticket is raised to be sent to a user for further investigation. The ticket 618 provides a time at which the fault diagnostic rule failed and an option to acknowledge the alarm 618. FIG. 7D illustrates an exemplary system user interface 620 for a chilled water system. The system user interface 620 illustrates the components of the chilled water system, and provides relevant information about the Chilled Water System. For example, system user interface 620 illustrates a plate heat exchanger 622, a first chiller 624 and a second chiller 626. In addition, the system user interface 630 illustrates fluid pathways 630 and indicates the direction of fluid flow with arrows 632. Furthermore, the system user interface 620 illustrates three return temperature points 628a, 628b, 628c which indicates the temperature at different locations within the Chilled Water System. For example, the return temperature point 628a outputs the measured temperature after fluid has passed through the plate heat exchanger, the return temperature point 628b outputs the measured temperature before fluid passes through the first chiller 624, and the return temperature point 628c outputs the measured temperature before fluid passes through the second chiller 626. Information is provided about each component of the chilled water system in the user interface. For the first chiller 624, the system user interface 620 provides information regarding whether the first chiller 624 is enabled, whether the first chiller 624 is running and whether there are any faults associated with the first chiller 624. Similar information is provided regarding the second chiller 626. FIG. 7E illustrates an exemplary system user interface 640 for a communications room. System user interface 640 illustrates the communications room, and the physical locations of components in the communications room. For example, the location of a first door 642a and a second door 642b, the locations of a first rack 644a and a second rack 644b are provided. In addition, measured physical parameter data is displayed for the cold aisle, the hot aisle and the gas suppression system. The displayed physical parameter data allows a user to monitor the status of the communications room, and the energy-consuming devices within the communications room. FIG. 8 is a flowchart detailing the steps of a method for determining energy efficiency in a building environment, such as the building illustrated in FIG. 1. This method can be used in conjunction with the method of FIG. 3, as further explained below. Like the method of FIG. 3, the method of FIG. 8 is performed by a computer processor which can be configured as a remote computer 500, or as computer 400 within the building environment 100 itself (as shown in FIG. 5 and FIG. 6). At step 801, the method of FIG. 8 comprises receiving physical parameter data from at least one of the plurality of energy-consuming devices. The energy-consuming devices may be the energy-consuming devices 200 illustrated in and discussed with respect to FIG 2. The physical parameter data may include at least one of fan speed, pump speed, heating valve position, operating hours and temperature. The physical parameter data may be received similarly to that described with respect to FIG. 3. In fact, the same physical parameter data may be used for FIG. 3 (i.e. for applying the diagnostic rules) as for FIG. 8 (for determining energy efficiency). At step 802, the method of FIG. 8 comprises using the physical parameter data in a predetermined energy efficiency equation. Subsequently, at step 803, the energy efficiency of at least one of the plurality of energy-consuming devices 200 is output. For example, the energy efficiency may be output to user interface 600. The output energy efficiency of the at least one energy-consuming device 201,202, 203 may be a percentage efficiency. The computer processor may receive fixed parameter data for use in the predetermined energy efficiency equation. The fixed parameter data may include at least one of fan power, pump power, coefficient of performance, heating capacity, cooling capacity, heating load and unit power. Other fixed parameter data may be used according to the types of energyconsuming devices 200 in the building environment 100. In an example, the fixed parameter data may comprise heating load data and the coefficient of performance for the at least one energy-consuming device 201, 202, 203, wherein total energy input for the at least one energy-consuming device 201, 202, 203 is determined by dividing the heating load data by the coefficient of performance. The fixed parameter data may further comprise fan power and pump power of the at least one energy-consuming device. The total energy input for the at least one energy-consuming device 201, 202, 203 may be determined by dividing the heating load data by the coefficient of performance. The total power consumption may be determined by adding the total energy input, the fan power and the pump power. This exemplary calculation can be applied to a HVAC system with a heat pump. For example, the coefficient of performance may be 3.02, the heating load may be 33 kW, the fan power may be 6.8 kW and the pump power may be 7.0 kW. These key parameters may be imported from the memory. The physical parameter data received by the computer processor may include the operating hours of the heat pump, the fan speed in the operational period and the pump speed in the operational period. In an example, the operating hours may be 50 hours, the fan speed may be 72% and the pump speed may be 66%. The total energy input is the heating load divided by the coefficient of performance of the heat pump, which equals 11.03 kW in this example. The total power consumption in this example is calculated by summing the calculated total energy input, fan power and the pump power, which equals 24.83 kW. To calculate the energy consumption for a specific period, the physical parameter data can be used to account for the operating time. The operating fan energy is calculated by multiplying the fan power by the average fan speed in the operational period, which equals 4.9 kW in this example. The operating pump energy is calculated by multiplying the pump power by the average pump speed in operational period and the operating hours of the pump, which equals 4.62 kW in this example. The energy consumption for a specific period is calculated by summing the energy input, the operating fan energy and the operating pump energy, which equals 1027.3 kWh in this example. Also disclosed herein is an example calculation for an HVAC system with a fan coil unit. The electric and thermal energy calculations are calculated separately. For electric energy consumption calculations, the electric energy used is adjusted by using the operating hours of the equipment over a specific period of time and the average fan speed. In this example, the fixed parameter data includes the fan coil unit power. The physical parameter data include the operating hours of the fan coil unit and the fan speed. In an example, the fan coil unit power may be 0.12 kW, the operating hours of the fan coil unit may be 40 hours and the fan speed may be 60%. The electrical energy consumption is calculated by multiplying the fan coil unit power, average fan speed and the operating hours of the fan coil unit, which in this example equals 2.80 kWh. For heating thermal energy consumption calculations, the heating thermal energy used is adjusted by using the operating hours of the equipment (e.g. the fan coil unit) over a specific period of time and an average position of a heating valve of the fan coil unit. In this example, the fixed parameter data may include the heating capacity of the fan coil unit. The physical parameter data may include the operating hours of the fan coil unit and the average heating valve position during the operational period. In an example, the heating capacity may be 1.4 kW, the operating hours of the fan coil unit may be 40 hours, and the average heating valve position may be 46%. The heating thermal energy consumption is calculated by multiplying the heating capacity, the average heating valve position and the operating hours of the fan coil unit, which in this example equals 25.76 kWh. For cooling thermal energy consumption calculations, the cooling thermal energy used is adjusted by using the operating hours of equipment (e.g. the fan coil unit) over a specific period of time and the average position of a cooling valve of the fan coil unit. In this example, the fixed parameter data may include the cooling capacity of the fan coil unit. The physical parameter data may include the operating hours of the fan coil unit and the average cooling valve position during the operational period. In an example, the cooling capacity may be 3.37 kW, the average cooling capacity position during the operational period may be 18% and the operating hours of the fan coil unit may be 40 hours. The cooling thermal energy consumption is calculated by multiplying the cooling capacity, the average cooling valve position and the operating hours of the fan coil unit, which in this example may be 24.26 kWh. Also disclosed herein is an example calculation for an HVAC sensor with a fan coil unit, wherein the wasted energy is calculated. To calculate the wasted energy, the operating hours of the fan coil unit over a specific period of time is used, taking into account the occupancy of the area being served, and wherein an average speed of the fan is used to adjust the energy used. The fixed parameter data may include the fan coil unit power. The physical parameter data includes the operating hours of the fan coil unit, the average fan speed, and the occupied hours of the area. Occupancy data may be received by an occupancy sensor. To calculate the operating hours whilst the room is unoccupied (i.e. unoccupied operating hours), the occupied hours of the area are subtracted from the operating hours of the coil unit. In an example, the operating hours of the fan coil unit may be 40 hours, the occupied hours of the area may be 12 hours, the fan coil unit power may be 0.12 kW and the average fan speed may be 60%. Therefore, the unoccupied hours may be 28 hours in this example. The wasted electrical energy is calculated by multiplying the fan coil unit power, the average fan speed and the unoccupied operating hours, which equals 2.02 kWh in this example. In other words, the wasted electrical energy is calculated by determining the energy used whilst the area being served is not occupied. Also disclosed herein is an example calculation for the control efficiency of temperature control of a fan coil unit. To calculate the control efficiency of a fan coil unit, it is first determined whether the actual temperature is within an acceptable range of the desired temperature. It is then determined whether the temperature is being controlled to be within the acceptable range. As an example, the acceptable range may be ± 2°C. Of course, it is envisioned that the acceptable range may be other values. The physical parameter data may include whether the fan coil unit is enabled (i.e. whether the device and / or unit is enabled), a temperature setpoint and the live temperature. The temperature setpoint represents the desired temperature. The unit enabled value may be represented as a binary variable, with 1 for enabled and 0 for disabled. The physical parameter data is evaluated relative to a condition. The condition evaluates whether the absolute difference between the setpoint and the temperature multiplied by the unit enabled value is less than or equal to 2. When the fan coil unit is enabled, and the absolute difference between the setpoint temperature and the temperature multiplied by the unit enabled value is less than or equal to 2, the condition returns a value of 1 (true). In all other situations, a value of 0 (false) is returned. The condition described above may be used to analyse the performance of the fan coil unit temperature during a certain period of time. A number, N, of different time periods are evaluated. For each time period, the physical parameter data discussed above, i.e. the live temperature and the unit enabled value are received. The condition is evaluated for each time period. The temperature control efficiency is calculated by summing the occurrences when the condition evaluates to 1 and dividing by the number of different time periods, N. FIG. 9A illustrates an exemplary energy consumption user interface which depicts the energy consumption of a building environment 100 over a time period, in this example over a month. The energy consumption user interface page also provides a calculated average control efficiency, which may be calculated using any of the methods described above, an amount by weight of carbon consumed. The energy consumption user interface page also provides a list of energy-consuming devices with each energy-consuming device’s energy usage, online hours, amount by weight of carbon consumed and approximate cost. FIG. 9B illustrates a graph of electric energy consumption over time for a first energyconsuming device 201, for example a fan coil unit. The example calculations disclosed herein may be utilised to establish a baseline performance. The methods of the present invention, i.e. either that of FIG. 3 or FIG. 8, may include continuously or regularly monitoring the energy consumption, for example the electric energy as illustrated in FIG. 9B. In other examples, the total energy, thermal heating energy, thermal cooling energy, and wasted energy may be monitored to establish a baseline performance. In FIG. 9B, by monitoring the electric energy consumption of an energy-consuming device 200 (or the plurality of energyconsuming devices 200 or the building environment 100 as a whole) and comparing it to the baseline performance, deviations from expected performance can be identified. Deviations may indicate potential faults or inefficiencies in the system. FIG. 9B illustrates that from time h to time t2, the electric energy consumption approximately equals the baseline. However, after time t2, the electric energy consumption increases rapidly, thereby indicating a change in operation of the building environment. The methods of the present invention, i.e. either that of FIG. 3 or FIG. 8, may include creating a fault diagnostic rule which defines an expected range of electric energy consumption. This fault diagnostic rule may be applied to the electric energy consumption data which is calculated from the physical parameter data in the example above. If the electric energy consumption data does not satisfy the electric energy consumption fault diagnostic rule, this may indicate a fault in the first energy-consuming device 201, or a fault in a second energyconsuming device 202. By continuously monitoring energy consumption and other physical parameters, the method of the present invention can detect faults at an early stage, allowing for prompt corrective actions and minimising the impact on energy efficiency of the building environment 100. The efficiencies which are output, for example, by using the calculations above, may allow for the improvement of the energy efficiency of the building environment 100 as a whole. The computer processor may create an alarm if the energy efficiency of at least one of the plurality of energy-consuming devices 200 is below a threshold level for a predetermined period of time. The computer processor may suggest modifications to control algorithms used by the computer processor based on the energy efficiency of at least one of the plurality of energyconsuming devices 200. For example, the modifications may include changing the temperature setpoints, fan speed, fan power, pump speed, pump power, operating hours, unit power and temperature, or to change the heating valve position, the cooling valve position, or change whether the unit is enabled. The computer processor may send the same control message to one or more energy-consuming devices 201, 202, 203 or may send different control messages to each energy-consuming device 201, 202, 203, depending on the nature of the devices, for example if the devices are all the same type. As an example, temperature, humidity or CO2 level setpoints can be adjusted to optimise energy efficiency without compromising the comfort of occupants. In some examples, the computer processor may calculate energy consumption savings based on the modifications made to the control algorithms. For example, the energy consumption before the modifications can be compared to the energy consumption after the modifications. In some examples, the computer processor may further comprise modifying the sequencing of the plurality of energy-consuming devices 200 to reduce energy consumption. For example, the staging of chillers or boilers can be optimised, which can reduce energy consumption and wear on the equipment. Turning to FIG. 10, an example computer 400 for implementing the methods of the invention is shown. Computer 400 may be embodied as any type of computer, including a server, a desktop computer, a laptop, a tablet, a mobile device, or the like. Components of computer 400 include, but are not limited to, a processor 911, such as a central processing unit (CPU), system memory 912, and system bus 913. System bus 913 provides communicative coupling for various components of computer 400, including system memory 912 and processor 911. Example system bus architectures include parallel buses, such as Peripheral Component Interconnect (PCI) and Integrated Drive Electronics (IDE), and serial buses, such as PCI Express (PCIe) and Serial ATA (SATA). Computer 400 of FIG. 10 is a specific implementation of computer 400 of FIG. 5. Remote computer 500 of FIG. 6 has equivalent features to computer 400 of FIG. 5 as discussed herein. System memory 912 is formed of volatile and / or non-volatile memory such as read only memory (ROM) and random-access memory (RAM). ROM is typically used to store a basic input / output system (BIOS), which contains routines that boots the operating system and sets up the components of computer 400, for example at start-up. RAM is typically used to temporarily store data and / or program modules that the processor 911 is operating on. Computer 400 includes other forms of memory, including (computer readable) storage media 915, which is communicatively coupled to the processor 911 through a memory interface 914 and the system bus 913. Storage media 915 may be or may include volatile and / or non-volatile media. Storage media 915 may be or may include removable or nonremovable storage media. Examples storage media 915 technologies include: semiconductor memory, such as RAM, flash memory, solid-state drives (SSD); magnetic storage media, such as magnetic disks; and optical storage, such hard disk drives (HDD) and CD, CD-ROM, DVD and BD-ROM. Data stored in storage medium 915 may be stored according to known methods of storing information such as program modules, data structures, or other data. For instance, the mappings discussed herein may be stored in storage media 915. Other data discussed herein, such as the manufacturer specification data. May also be stored in storage media 915. Various program modules are stored on the system memory 912 and / or storage media 915, including an operating system and one or more user applications. Such user applications may cause the computer 400 to interact with the energy-consuming devices in the building environment 100 in the way discussed with respect to FIG. 3 and / or FIG. 8. Computer 400 may be present within the building environment 100 and / or may be a remote computer 500 communicatively coupled to the building environment 100 via a least one communication network, such as the Internet. Other communication networks may be used including a local area network (LAN) and / or a wide area network (WAN). Further communication networks may be present in various types of computer 400, such as mobile devices and tablets, to cellular networks, such as 3G, 4G LTE and 5G. Computer 400 establishes communication through network interface 919. Computer 400 is communicatively coupled to a display device via a graphics / video interface 916 and system bus 913. The display device may be used to present the user interface discussed herein to a user. In some instances, the display device may be an integrated display. A graphical processing unit (GPU) 926 may be used in addition to improve graphical and other types of processing. Computer 400 also includes an input peripheral interface 917 and an output peripheral interface 918 that are communicatively coupled to the system bus 913. Input peripheral interface is communicatively coupled to one or more input devices, such as a keyboard, mouse or touchscreen, for interaction between the computer 400 and the user. Output peripheral interface 918 is communicatively coupled to one or more output devices, such as a speaker. When not integrated, the communicative coupling may be wired, such as via a universal serial bus (USB) port, or wireless, such as over Bluetooth. The invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In a preferred embodiment, the invention is implemented in software. Furthermore, the invention can take the form of a computer program embodied as a computer-readable medium having computer executable code for use by or in connection with a computer. For the purposes of this description, a computer readable medium can be any tangible apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the computer. Moreover, a computer-readable medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) ora propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact diskread only memory (CD-ROM), compact disk-read / write (CD-R / W) and DVD. The flow diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of the methods of the invention. In some alternative implementations, the steps noted in the figures may occur out of the order noted in the figures. For example, two steps shown in succession may, in fact, be performed substantially concurrently, or the blocks may sometimes be performed in the reverse order, depending upon the functionality involved. It will be understood that the above description of is given by way of example only and that various modifications may be made by those skilled in the art. Although various embodiments have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the scope of this invention. The following numbered embodiments form part of the description: 1. A method for diagnosing faults in a building environment, the building environment having a plurality of energy-consuming devices located within the building environment, wherein a set of mappings, each mapping corresponding to at least a portion of the plurality of energyconsuming devices, is stored in a memory; wherein the method is performed by a computer processor and comprises: receiving physical parameter data from a first energy-consuming device of the plurality of energy-consuming devices; applying at least one fault diagnostic rule to the physical parameter data; and if the physical parameter data does not satisfy the at least one fault diagnostic rule: identifying from the set of mappings in the memory, a first mapping that relates to the first energy-consuming device, wherein the first mapping corresponds to a first portion of the plurality of energy-consuming devices; and sending a control message to one or more energy-consuming devices of the first portion of the plurality of energy-consuming devices, to change at least one operating parameter. 2. The method of embodiment 1, wherein the building environment comprises one or more of: a building, one or more floors of a building, and one or more portions of one or more floors of a building. 3. The method of embodiment 1 or embodiment 2, wherein the plurality of energy-consuming devices have different physical points from which physical parameter data is received. 4. The method of any preceding embodiment, wherein the plurality of energy-consuming devices comprises devices arranged in racks. 5. The method of embodiment 4, wherein the physical parameter data comprises at least one of: fan speed, pump speed, heating valve position, cooling valve position, operating hours, temperature, and whether the device is enabled. 6. The method of embodiment 1, further comprising changing the operating parameters of the first energy-consuming device. 7. The method of any preceding embodiment, wherein the physical parameter data not satisfying the at least one fault diagnostic rule indicates a fault of one of the energyconsuming devices of the first portion of energy-consuming devices. 8. The method of any preceding embodiment, further comprising creating an alarm for one of the first portion of energy-consuming devices if the physical parameter data does not satisfy the at least one fault diagnostic rule. 9. The method of any preceding embodiment, further comprising outputting the alarm to a user associated with the building environment. 10. The method of embodiment 9, further comprising receiving an acknowledgement of the alarm from the user via a user input. 11. The method of any preceding embodiment, wherein the at least one fault diagnostic rule defines an expected value or a range of expected values for the physical parameter data. 12. The method of embodiment 11, wherein applying the at least one fault diagnostic rule comprises comparing the physical parameter data received from the first energy-consuming device to the expected value or the range of expected values. 13. The method of embodiment 12, wherein an alarm is created if the physical parameter data received from the first energy-consuming device does not match the expected value or is not within the range of expected values for a predetermined period of time. 14. The method of embodiment 7, further comprising diagnosing the cause of the fault. 15. The method of embodiment 14, wherein the first energy-consuming device has a fault, wherein diagnosing the cause of the fault comprises identifying the first energy-consuming device. 16. The method of embodiment 14, wherein the first portion comprises a second energyconsuming device, wherein the second energy-consuming device has a fault, wherein diagnosing the cause of the fault comprises identifying the second energy-consuming device. 17. The method of embodiment 16, further comprising determining a relationship between the first energy-consuming device and the second energy-consuming device from the first mapping. 18. The method of embodiment 17, further comprising creating an alarm for the second energy-consuming device. 19. The method of any preceding embodiment, further comprising storing imported data in memory, the imported data corresponding to at least one of the plurality of energyconsuming devices. 20. The method of embodiment 19, wherein the imported data comprises at least one of: manufacturing specification data or historic data relating to at least one of the plurality of energy-consuming devices. 21. The method of embodiment 20, wherein the imported data is stored in memory in a structured format. 22. The method of embodiment 20 or embodiment 21, further comprising analysing the historic data and identifying a first trend. 23. The method of embodiment 22, further comprising comparing the at least one operating parameter with the first trend. 24. The method of any one of embodiments 20-23, wherein the manufacturing specification data is converted into a common format. 25. The method of any one of embodiments 20-24, further comprising changing the at least one operating parameter based on the manufacturing specification data. 26. The method of any one of embodiments 20-25, wherein the manufacturing specification data comprises fixed parameter data. 27. The method of embodiment 26, wherein the fixed parameter data comprises at least one of: fan power, pump power, coefficient of performance, heating capacity, cooling capacity, heating load and unit power. 28. The method of embodiment 15 or embodiment 16, wherein diagnosing the cause of the fault comprises analysing physical parameter data from one of the plurality of energy consuming devices over a period of time. 29. The method of any preceding embodiment, further comprising using the physical parameter data in a predetermined energy efficiency equation. 30. The method of embodiment 29, further comprising outputting the energy efficiency of at least one of the plurality of energy-consuming devices. 31. The method of embodiment 30, wherein the energy efficiency of the at least one of the plurality of energy-consuming devices is a percentage efficiency. 32. The method of embodiment 30 or embodiment 31, wherein the processor creates an alarm if the energy efficiency of the at least one of the plurality of energy-consuming devices is below a threshold level for a predetermined period of time. 33. The method of any one of embodiments 30-32, further comprising suggesting modifications to control algorithms used by the computer processor based on the energy efficiency of at least one of the plurality of energy-consuming devices. 34. The method of embodiment 33, further comprising calculating energy consumption savings based on the modifications made to the control algorithms. 35. The method of any one of embodiments 29-34, further comprising modifying the sequencing of the plurality of energy-consuming devices to reduce energy consumption. 36. The method of any one of embodiments 29-35, wherein the physical parameter data comprises an excess operating time of the at least one energy-consuming device and operational data for the at least one energy-consuming device, wherein wasted energy of the at least one energy-consuming device is determined using the excess operating time and the operational data. 37. The method of any one of embodiments 29-36, wherein the fixed parameter data comprises heating load data and the coefficient of performance for the at least one energyconsuming device, wherein total energy input for the at least one energy-consuming device is determined by dividing the heating load data by the coefficient of performance. 38. The method of embodiment 37, wherein the physical parameter data further comprises operating hours of the at least one energy-consuming device, wherein the fixed parameter data comprises fan power and pump power of the at least one energy-consuming device, wherein total power consumption is determined by adding the total energy input, the fan power and the pump power. 39. A method for determining energy efficiency in a building environment, the building environment having a plurality of energy-consuming devices located within the building environment, wherein a set of mappings, each mapping corresponding to at least a portion of the plurality of energy-consuming devices, is stored in a memory; wherein the method is performed by a computer processor and comprises: receiving physical parameter data from at least one of the plurality of energy-consuming devices; using the physical parameter data in a predetermined energy efficiency equation; outputting the energy efficiency of one of the plurality of energy-consuming devices. 40. A building environment having a plurality of energy-consuming devices located within the building environment and a computer processor, the computer processor configured to 5 perform the method of any preceding embodiment. 41. A computer processor configured to perform the method of any of embodimentsl to 39. 42. A computer-readable storage medium comprising instructions which, when executed by a computer processor, are configured to cause the computer processor to perform the method of any of embodiments 1 to 39. 10 43. A computer program comprising instructions which, when executed by a computer processor, are configured to cause the computer processor to perform the method of any of embodiment 1 to 39.

Claims

1. A method for diagnosing faults in a building environment, the building environment having a plurality of energy-consuming devices located within the building environment, wherein a set of mappings, each mapping corresponding to at least a portion of the plurality of energy-consuming devices, is stored in a memory, wherein the method is performed by a computer processor and comprises:receiving physical parameter data from a first energy-consuming device of the plurality of energy-consuming devices;applying at least one fault diagnostic rule to the physical parameter data; and if the physical parameter data does not satisfy the at least one fault diagnostic rule:identifying from the set of mappings in the memory, a first mapping that relates to the first energy-consuming device, wherein the first mapping corresponds to a first portion of the plurality of energy-consuming devices; andsending a control message to one or more energy-consuming devices of the first portion of the plurality of energy-consuming devices, to change at least one operating parameter.

2. The method of claim 1, wherein the building environment comprises one or more of: a building, one or more floors of a building, and one or more portions of one or more floors of a building.

3. The method of claim 1 or claim 2, wherein the plurality of energy-consuming devices have different physical points from which physical parameter data is received.

4. The method of any preceding claim, wherein the plurality of energy-consuming devices comprises devices arranged in racks.

5. The method of any preceding claim, further comprising changing the operating parameters of the first energy-consuming device.

6. The method of any preceding claim, further comprising creating an alarm for one of the first portion of energy-consuming devices if the physical parameter data does not satisfy the at least one fault diagnostic rule and outputting the alarm to a user associated with the building environment.

7. The method of claim 6, further comprising receiving an acknowledgement of the alarm from the user via a user input.

8. The method of any preceding claim, wherein the at least one fault diagnostic rule defines an expected value or a range of expected values for the physical parameter data, wherein applying the at least one fault diagnostic rule comprises comparing the physical parameter data received from the first energy-consuming device to the expected value or the range of expected values, wherein an alarm is created if the physical parameter data received from the first energy-consuming device does not match the expected value or is not within the range of expected values for a predetermined period of time.

9. The method of any preceding claim, wherein the physical parameter data not satisfying the at least one fault diagnostic rule indicates a fault of one of the energy-consuming devices of the first portion of energy-consuming devices, further comprising diagnosing the cause of the fault.

10. The method of claim 9, wherein the first energy-consuming device has a fault, wherein diagnosing the cause of the fault comprises identifying the first energy-consuming device.

11. The method of claim 9, wherein the first portion comprises a second energy-consuming device, wherein the second energy-consuming device has a fault, wherein diagnosing the cause of the fault comprises identifying the second energy-consuming device.

12. The method of claim 11, further comprising determining a relationship between the first energy-consuming device and the second energy-consuming device from the first mapping.

13. The method of claim 12, further comprising creating an alarm for the second energyconsuming device.

14. The method of any preceding claim, further comprising storing imported data in memory, the imported data corresponding to at least one of the plurality of energy-consuming devices.

15. The method of claim 14, wherein the imported data comprises at least one of: manufacturing specification data or historic data relating to at least one of the plurality of energy-consuming devices.

16. The method of claim 15, further comprising analysing the historic data and identifying a first trend and comparing the at least one operating parameter with the first trend.

17. The method of claim 15 or claim 16, further comprising changing the at least one operating parameter based on the manufacturing specification data.

18. The method of any preceding claim, further comprising using the physical parameter data in a predetermined energy efficiency equation and outputting the energy efficiency of at least one of the plurality of energy-consuming devices.

19. The method of claim 18, wherein the processor creates an alarm if the energy efficiency of the at least one of the plurality of energy-consuming devices is below a threshold level for a predetermined period of time.

20. The method of claim 18 or claim 19, further comprising suggesting modifications to control algorithms used by the computer processor based on the energy efficiency of at least one of the plurality of energy-consuming devices.

21. The method of claim 20, further comprising calculating energy consumption savings based on the modifications made to the control algorithms.

22. A building environment having a plurality of energy-consuming devices located within the building environment and a computer processor, the computer processor configured to perform the method of any preceding claim.

23. A computer processor configured to perform the method of any of claims 1 to 21.

24. A computer-readable storage medium comprising instructions which, when executed by a computer processor, are configured to cause the computer processor to perform the method of any of claims 1 to 21.

25. A computer program comprising instructions which, when executed by a computer processor, are configured to cause the computer processor to perform the method of any of claims 1 to 21.

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