Energy consumption warning procedure, energy consumption warning system and platform

Abstract

Energy consumption warning procedure, the procedure comprehensive: Measuring location-specific energy consumption values ​​(121) over a specific period of time at a sensor (170) that is deployed at a location of a monitored site; Decomposition of the location-specific energy consumption values ​​(121) according to a first characteristic, wherein the energy consumption values ​​(121) are decomposed by performing a date-based decomposition type; Decomposition of the location-specific energy consumption values ​​(121) according to a second characteristic comprising: Decomposition of the energy consumption values ​​(121) by performing a seasonal decomposition into the components: trend, season, and remainder, to identify changes in energy consumption that are due to seasonal effects, and to identify changes that are due to a general trend; Obtaining an initial decomposed energy consumption value (123) based on the first characteristic and based on the residual component to identify any change in energy consumption that is due to unintentional energy consumption; Determining a corresponding first reference value based on the decomposed values ​​(123); Comparing the first, decomposed energy consumption value (123) with the determined first reference value; Outputting a message to notify a user of unintended energy consumption if the first decomposed energy consumption value (123) and the determined first reference value are different.

Description

TECHNICAL AREA

[0001] The present invention relates to energy consumption measurement systems. In particular, the present invention relates to an energy consumption warning method, an energy consumption warning system, and a platform. BACKGROUND

[0002] In conventional energy distribution networks, the energy consumption of a location is typically measured at a central supply point, for example, an electricity meter installed between a utility's supply line and a first distribution board of a given standard, such as a single building or a self-contained part of a building, like an apartment or the like. This allows the total electrical energy consumed at that specific location to be measured, regardless of the location's electrical distribution system.

[0003] Typically, the energy consumption measured at such a central supply point is used by the utility company for billing purposes. Therefore, at the end of a billing period, such as a month or year, the utility company usually prepares a consumption statement based on the total measured consumption and provides it to the site manager or owner. Based on this consumption statement, the site manager or owner can then determine whether they have remained within their desired energy budget or whether it has been exceeded.

[0004] For billing purposes, such a conventional approach is sufficient. However, in times of high energy prices and a focus on energy efficiency, the data availability in such a conventional scheme is insufficient to maintain control over how energy is actually consumed within a given location, and also to estimate at any given time whether given energy targets are being met.

[0005] In addition to metering devices installed at a central supply point, individual metering devices are also known. For example, an individual metering device can be plugged into a wall socket and supply energy to a single electrical appliance, such as a device. Such energy metering devices allow the measurement of the energy consumption of a specific appliance at a specific location. However, this data is only available locally at the individual metering device. Therefore, the use of such metering devices, at least at locations with a relatively large number of electrical appliances and other electrical consumers, is both expensive and time-consuming if a building manager or owner wants to obtain a reasonable, complete picture of the energy consumption of the site being monitored.

[0006] Document US 2015 / 0261963A1 concerns a procedure that includes receiving time-series data from a source. The data are received for one or more instances. The procedure further includes detecting the sensitivity content in the time-series data. The sensitivity content indicates the presence of an anomaly. Detection includes determining a kurtosis value corresponding to the time-series data. Detection further includes comparing the kurtosis value to a reference value. Detection further includes processing the data using a first filtering agent or a second filtering agent. The first filtering agent is used if the data distribution of the time-series data is either a platykurtic distribution or a mesokurtic distribution. The second filtering agent is used if the data distribution of the time-series data is a leptokurtic distribution.

[0007] Accordingly, there is a need for better systems and procedures for monitoring energy consumption at a specific location.

[0008] In particular, there is a need for systems and procedures to monitor energy consumption at a specific location and to notify a user of unusual energy consumption. SUMMARY

[0009] According to one embodiment of the present invention, an energy consumption warning method is provided. The method comprises measuring location-specific energy consumption values ​​over a specific period at a sensor deployed at a location within a monitored site, and decomposing the location-specific energy consumption values ​​according to a first characteristic, wherein the energy consumption values ​​are decomposed by performing a date-based decomposition. The method further comprises decomposing the location-specific energy consumption values ​​according to a second characteristic, comprising: decomposing the energy consumption values ​​by performing a seasonal decomposition into the components: trend, season, and remainder, in order to identify changes in energy consumption based on seasonal effects, changes based on a general trend, and obtaining a first decomposed energy consumption value.A corresponding first reference value is determined based on the decomposed values. The first decomposed energy consumption value is compared with the determined first reference value. A message to notify the user of the unintended energy consumption is issued if the first decomposed energy consumption value and the determined first reference value differ.

[0010] According to a further embodiment of the present invention, an energy consumption warning system is described. The energy consumption warning system comprises a sensor that is deployed at a location within a monitored site. The sensor is configured to provide location-specific energy consumption values ​​over a specific period. The system includes a data decomposition device comprising a processor and a memory that stores instructions to be executed in the processor, wherein the instructions are configured to decompose the location-specific energy consumption values ​​according to a first characteristic and according to a second characteristic.

[0011] The decomposition of values ​​according to the first characteristic includes decomposition of values ​​by performing a date-based decomposition type, wherein the instructions configured to decompose energy consumption values ​​according to the second characteristic include instructions configured to decompose energy consumption values ​​by means of a seasonal decomposition into the components: trend, season, and remainder, changes in energy consumption based on seasonal effects, in order to identify changes based on a general trend. The system has an alert device that includes a processor and memory that stores instructions for execution in the processor, wherein the instructions are configured to compare an initial energy consumption value of the location-specific energy consumption values ​​with the corresponding initial reference value.The first, decomposed energy consumption value is based on the first characteristic and on the residual component to identify a change in energy consumption that is due to unintended energy consumption and to output a message to notify a user of the unintended energy consumption if the first energy consumption value and the determined corresponding first reference value differ from each other.

[0012] According to a further embodiment of the present invention, a cloud-based energy consumption warning platform is disclosed. The cloud-based energy consumption warning platform comprises a data decomposition device, which includes a processor and a memory containing instructions for execution in the processor, wherein the instructions are configured to decompose location-specific energy consumption values ​​according to a first characteristic and according to a second characteristic.The decomposition of values ​​according to the first characteristic includes decomposition of values ​​by performing a date-based decomposition type, with instructions configured to decompose energy consumption values ​​according to the second characteristic. Instructions configured to decompose energy consumption values ​​using a seasonal decomposition into the components: trend, season, and remainder. This decomposition identifies changes in energy consumption based on seasonal effects and changes based on a general trend. The location-specific energy consumption values ​​are associated with a location within a monitored site and are provided to the cloud-based energy consumption alert platform via at least one data network.The platform features a warning device comprising a processor and memory that stores instructions for execution. These instructions are configured to compare an initial energy consumption value, based on the location-specific energy consumption values, with a corresponding initial reference value. The initial, decomposed energy consumption value, based on the first characteristic and the residual component, is used to identify any changes in energy consumption resulting from unintended energy use. A message is then issued to notify the user of such unintended energy consumption if the initial energy consumption value and the specified, corresponding initial reference value differ.

[0013] The various embodiments of the invention described above enable the implementation of an energy consumption warning system that reliably notifies a user of unusual energy consumption. In this way, a malfunction of a unit can be detected quickly and reliably. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Various embodiments of the present invention are described with reference to the accompanying drawings. Fig. Figure 1 shows a schematic representation of an energy consumption warning system according to an embodiment of the invention. Fig. Figure 2 shows a flowchart of an energy consumption warning procedure according to an embodiment of the invention. DETAILED DESCRIPTION OF EXAMPLE EXECUTION FORMS

[0015] In various embodiments, the present invention relates to an energy consumption warning method that can notify a user of unusual energy consumption. The embodiments of the present invention further relate to an energy consumption warning system and a cloud-based energy consumption warning platform that can notify a user of unusual energy consumption.

[0016] Fig. Figure 1 shows an energy consumption warning system 100 according to an embodiment of the invention. The system 100 comprises a warning platform 110 and a measuring system 150, which is connected to the platform via a first gateway 112 and a second gateway 152 of a data network 180, such as the Internet.

[0017] The measuring system 150 is deployed at a location to be monitored, for example, a single building or a group of buildings. In the illustrated example, electrical energy is supplied to the location by a utility company 190 at a central supply point 192. For example, the location may be connected to an energy distribution network of the utility company 190 via a consumption metering device 154, such as a smart meter. However, in an alternative embodiment, energy may be supplied to the monitored location by multiple providers, multiple supply points, and / or multiple energy carriers.

[0018] Within the monitored site, the energy supplied by utility 190 is distributed through several distribution boards (not shown). Typically, the energy supplied to any specific endpoint within the monitored site is provided via at least one distribution board and protected by at least one circuit breaker. In the Fig. For the sake of simplicity, only three circuit breakers, 160a to 160c, are shown in the illustrated embodiment. However, it should be noted that the monitored site may contain several tens, hundreds, or even thousands of distribution boards and circuit breakers.

[0019] In the described embodiment, each of the circuit breakers 160a to 160c is assigned a corresponding sensor 170a to 170c. The sensors 170 are placed on the circuit breakers 160 to monitor the energy consumption of the corresponding circuits 162a to 162c, which each lead to electrical loads 164a to 164c. In another embodiment, the sensors 170 can be assigned to individual devices, groups of circuit breakers, distribution boards, or any other independent part of the power distribution network within the site to be monitored. Such sensors and the data they collect are hereinafter referred to as granular-level sensors and granular-level energy consumption values, respectively. The energy consumption values ​​include consumption values ​​for electrical energy and / or gas and / or oil and / or hot water and / or steam.

[0020] The sensors 170, and optionally the smart metering device 154, are connected via a local area network 156 (LAN). In this way, location-specific energy consumption values ​​for the individual consumers 164 can be collected at a granular level and made available to the warning platform 110 via the gateway 152, the data network 180 and the gateway 112.

[0021] It is pointed out that the present invention is not limited to the specific application in Fig. The disclosed measurement system 150 is limited. For the purposes of the present invention, it is sufficient to provide relatively fine-grained energy consumption values ​​at a granular level for further analysis as described below. Such data can be obtained by advanced data analysis of data provided by one or a few sensors assigned to larger parts of a monitored site, rather than by a large number of sensors assigned to individual circuits or energy-consuming devices.

[0022] The warning platform 110 has a data decomposition device 120 and a warning device 130.

[0023] The data decomposition device 120 decomposes energy consumption values ​​121 ( Fig. 2), which are provided by the sensors 170. For example, the energy consumption values ​​121 are time series of historical consumption data. In particular, the data decomposition device 120 decomposes the energy consumption values ​​121 in two directions, a vertical and a horizontal direction. Accordingly, the data decomposition device 120 decomposes the energy consumption values ​​121 according to a first characteristic and according to a second characteristic. According to further embodiments, two separate data decomposition devices are provided, wherein a first data decomposition device is configured to decompose the energy consumption values ​​121 into a plurality of different data sets according to the first characteristic, and a second data decomposition device is configured to decompose the energy consumption values ​​121 into a plurality of different data sets according to the second characteristic.

[0024] Vertical decomposition is performed to account for seasonal effects, particularly daily seasonal variations or daily patterns. For example, the energy consumption of an air conditioner is higher during the day than at night. Conversely, the energy consumption of a heating system may be higher at night than during the day.

[0025] According to one embodiment of the invention, seasonal decomposition is performed using the Seasonal-Trend Decomposition (STL) method based on Loess (e.g., as described in the article "STL: A Seasonal-Trend Decomposition Procedure on Loess" by RB Cleveland, WS Cleveland, JE McRae, and I. Terpenning, published in the Journal of Official Statistics, Volume 6, Issue 1, pages 3 to 63, 1990) or seasonal decomposition using the moving average (e.g., as described in FE Grubbs (1969), Procedures for detecting outlying observations in samples. Technometrics, Volume 11, Issue 1, pages 1 to 21). Of course, other decomposition methods that enable seasonal decomposition of energy consumption values ​​can be used.

[0026] When seasonal trend decomposition is used, the energy consumption values ​​121 are decomposed into three components: seasonal, trend, and residual. For the comparison performed by the warning device 130, described later, only the residual component is used. Accordingly, seasonal and trend effects are excluded. Therefore, changes in energy consumption due to seasonal effects and changes due to a general trend can be excluded.

[0027] Horizontal decomposition is used to separate energy consumption values ​​according to different types of days. For example, the values ​​are decomposed into the components weekday, weekend, and holiday. This allows for the consideration of differences in energy consumption on different types of days. For example, an office's energy consumption may be higher on a weekday than on a weekend.

[0028] The warning device 130 compares the decomposed values ​​provided by the data decomposition device 120 with one or more corresponding reference values. For example, the warning device 130 performs a Grubbs outlier test (GESD). The GESD test is described, for example, by B. Rosner in the article "Percentage Points for a generalized ESD many-outlier procedure," published in Technometrics, 25th edition, issue 2, pages 165 to 172, 1983.

[0029] The decomposed values ​​of the remaining component are studentized, for example, as follows, where a data set y = {x _ 1, ..., x _ N} is given by: Gj=maxi|xi−x¯|8

[0030] A studentized value or a set of studentized values ​​is / are compared to the reference value. For example, a decomposed and studentized current energy consumption value is compared to the reference value, which is based on historical energy consumption data. _ N can be the most recent value.

[0031] For example, the reference value is calculated using the following formula: λj=(N−j)tp,N−j−1(N−j−1+tp,N−j−12)(N−j+1) where: p=1−α2(N−j+1) and t _ {h,k} specifies the h-percentage point function of the t-distribution with k degrees of freedom. α ∈ (0,1) is the significance level of the data collection.

[0032] If the studentized value is greater than the reference value, Gj>λj The studentized value is considered an outlier, based on an unusual energy consumption of a corresponding electrical consumer.

[0033] The warning platform 110 includes a user interface device 140. The user interface device 140 is used to output a message, for example, a warning message. For example, the message is displayed within the warning platform 110 or can be provided to an external system for further processing, such as an email account or a web interface. For example, the notification is visualized and displayed on the user interface device 140, which is implemented, for example, as a display of the warning platform 110. According to further embodiments, the user is notified via a user interface device 140 that is part of a portable device such as a smartphone. For example, the notification can be sent by automated notification systems, such as email or text messages.

[0034] Optionally, the warning platform 110 can also include a storage device 134 for storing the location-specific energy consumption values ​​provided by the sensors 170 and / or the decomposed values, and for the reference values. Additionally or alternatively, the storage device 134 can also be used to store the warning messages. For example, the storage device 134 is a storage device of a cloud service.

[0035] According to the described embodiment, the warning device 130 is implemented as a cloud-based web application. Additionally or alternatively, the data decomposition device 120 is implemented as a cloud-based web application.

[0036] The data decomposition device 120 and / or the warning device 130 are connected to the measuring system 140 via the data network 180. The measuring system 150 is installed at a specific location to be monitored, and the warning platform 110 may be implemented outside of that specific location.

[0037] Fig. Figure 2 shows a flowchart of an exemplary procedure for operating the energy consumption warning system 110, which is located in Fig. 1 is shown.

[0038] According to the procedure, in a first step, 201 location-specific energy consumption values ​​121 are obtained using the sensors 170. Specifically, the consumption values ​​are obtained for a concrete period, for example, 6 months. The period should be long enough to provide sufficient information about seasonal effects. For example, the energy consumption values ​​121 obtained in step 201 are made available to the warning platform 110 via one or more data networks.

[0039] In an optional step 202, the energy consumption values ​​121 are smoothed, e.g. by a Gaussian filter.

[0040] In step 203, a horizontal decomposition of the energy consumption values ​​121 is performed. This horizontal decomposition separates the series of energy consumption values ​​121 into the decomposed values ​​122a, 122b, and 122c according to the type of day. For example, the type of day could be a weekday, a weekend, or a holiday. According to further embodiments, there are more than three different types of decomposed values ​​after step 203, or fewer than three types of decomposed values.

[0041] In a further step 204, a vertical decomposition of the decomposed values ​​122a, 122b, and 122c is performed. In this vertical decomposition, the series of decomposed values ​​122 is separated into three components: the seasonal component, the trend component, and the residual component. Accordingly, after step 204, there are decomposed values ​​123a to 123i. Due to the vertical decomposition, it is possible to remove seasonal and trend effects from the decomposed values ​​122. Only the residual component, specifically decomposed values ​​123c, 123f, and 123i, are processed further.

[0042] In step 205, the decomposed values ​​123c, 123f, and 123i are compared with the reference value. For example, the Grubbs outlier test (ESD) is performed for the decomposed values ​​123c, 123f, and 123i.

[0043] In a further step 206, it is determined whether at least one of the decomposed values ​​123c, 123f, and 123i is larger than the reference value. If a decomposed value is larger than the reference value, it is considered an outlier, and a notification is provided in step 207. For example, the Grubbs test (ESD, generalized extreme studentized deviate) is used to detect outliers.

[0044] For example, let's assume a data set of size 100, with a significance level of 5%, in the first iteration, p=1−0.052(100−1+1)=0.99975 and t0.099976,100−1+1=3.600812 and λj=(100−1)×3.600812(100−1−1−3.6008122)(100−1+1)=3.353992

[0045] If the condition Gj>λj If fulfilled, x will be _ i is marked as an outlier. For further processing, x can be used. _ i is removed from the series x.

[0046] The updated series undergoes another iteration until G j > λ j if the condition is not met. Then all outliers were recorded.

[0047] With the described embodiment, it is possible to monitor the energy consumption of the electrical device 164 and to detect any unusual behavior of any of the electrical devices 164. Unusual behavior of the facility equipment is detected. Furthermore, preventive maintenance is possible. For example, if there is a fault in one of the electrical devices 164 that leads to unusual energy consumption, the facility management of the monitored location can be notified. In addition, the building management can be notified if one of the electrical devices 164 is switched off but should be running. Therefore, even unforeseen incidents such as machine or equipment failure can be monitored, and the building management can be notified quickly.Accordingly, building management can immediately offer emergency support and troubleshooting, restoring normal operation. Building management is informed of the actual energy consumption of specific electrical equipment (164). Therefore, it is not necessary to rely on a system reporting function or a manual report from another user. This significantly reduces the duration of the problem. With the energy consumption warning system (100), building management can be alerted to any anomalies in the equipment or machinery by analyzing their energy consumption patterns. For example, if consumption suddenly drops significantly and approaches zero, this could be caused by a malfunction of the unit.

[0048] Vertical and horizontal decomposition are used to capture, interpret, and analyze energy consumption patterns. This allows for the handling of long-term behavioral changes identified in the trend component. For example, the trend component captures that an air conditioner's energy consumption is higher in summer than in winter. Conversely, a heating system's energy consumption may be higher in winter than in summer. Furthermore, behavioral changes caused by the effects of holidays or weekends can be handled through horizontal decomposition. Seasonality can also be taken into account. False alerts are not triggered by the vertical decomposition due to seasonal effects and the impact of trends. Flooding or masking of problem cases is prevented according to the characteristics of the GESD test.

[0049] A user receives an early warning of any unusual behavior that could potentially lead to the complete failure of a facility or machine. This can prevent serious malfunctions. The user receives immediate active alerts and notifications as soon as something fails, based on the detection of anomalies. The user can then respond immediately to address the problem. By determining energy consumption values ​​at a granular level, the user knows exactly where the problem lies or which consumer is generating the unusual energy consumption. Energy consumption data at the circuit breaker level is used to capture the consumption pattern for each individual energy consumer, rather than using overall consumption data for the monitored site.The described procedure, system and platform are accurate and immediately indicate which consumer 164 has an abnormal energy consumption.

Claims

[1] Energy consumption warning procedure, the procedure encompassing: Measuring location-specific energy consumption values ​​(121) over a specific period of time at a sensor (170) that is deployed at a location of a monitored site; Decomposition of the location-specific energy consumption values ​​(121) according to a first characteristic, wherein the energy consumption values ​​(121) are decomposed by performing a date-based decomposition type; Decomposition of the location-specific energy consumption values ​​(121) according to a second characteristic comprising: Decomposition of the energy consumption values ​​(121) by performing a seasonal decomposition into the components: trend, season, and remainder, to identify changes in energy consumption that are due to seasonal effects, and to identify changes that are due to a general trend; Obtaining an initial decomposed energy consumption value (123) based on the first characteristic and based on the residual component to identify any change in energy consumption that is due to unintentional energy consumption; Determining a corresponding first reference value based on the decomposed values ​​(123); Comparing the first, decomposed energy consumption value (123) with the determined first reference value; Outputting a message to notify a user of unintended energy consumption if the first decomposed energy consumption value (123) and the determined first reference value are different. [2] The method of claim 1, further comprising: Studentizing at least one value of the remainder; Comparing the studentized value with the reference value; and Notify the user if the studentized value is greater than the determined first reference value. [3] Method according to claim 1, further comprising: Determining the first reference value based on the values ​​of the rest. [4] Method according to claim 1, wherein performing the date-based decomposition method comprises: Decomposing the values ​​into their components: weekday, holiday, and weekend. [5] The method of claim 1, further comprising: Obtaining a series of energy consumption values ​​from the location-specific energy consumption values ​​(121); Comparing the series with the determined, corresponding first reference value; and Notify a user if the series and the determined first reference value are different. [6] Energy consumption warning system (100), comprising: a sensor (170) that is deployed at a location of a monitored site, wherein the sensor (170) is configured to provide location-specific energy consumption values ​​(121) over a specific period of time; a disassembly device (120) comprising a processor and a memory, the instructions for execution are stored in the processor, wherein the instructions are configured to decompose the location-specific energy consumption values ​​(121) according to a first characteristic and according to a second characteristic, wherein the decomposition of the energy consumption values ​​(121) according to the first characteristic includes a decomposition of the energy consumption values ​​(121) by performing a date-based decomposition type, wherein the instructions configured to decompose the energy consumption values ​​(121) according to the second characteristic include instructions configured to decompose the energy consumption values ​​(121) by means of a seasonal decomposition into the components: trend, season, and remainder, changes in energy consumption that are based on seasonal effects in order to identify changes that are based on a general trend; and a warning device (130) comprising a processor and a memory which stores instructions to be executed in the processor, wherein the instructions are configured to compare a first decomposed energy consumption value (123) of the location-specific energy consumption values ​​(121) with a corresponding first reference value based on the decomposed values, wherein the first, decomposed energy consumption value (123) based on the first characteristic and on the residual component to identify a change in energy consumption that is due to unintended energy consumption, and to output a message to notify a user of the unintended energy consumption if the first decomposed energy consumption value (123) and the determined first reference value are different. [7] System according to claim 6, wherein the sensor (170) is placed on or near a corresponding energy-consuming device (164) of the monitored location, wherein the sensor (170) is configured to provide device-specific energy consumption values. [8] System according to claim 6, wherein the system is configured to monitor the electrical energy consumption of the monitored location and the sensor (170) is placed on or near a circuit breaker in connection with a corresponding electrical circuit of the monitored location, wherein the sensor (170) is configured to provide circuit-specific energy consumption values. [9] System according to claim 6, wherein the sensor, the disassembly device (120) and the warning device (130) are interconnected via at least one data network (180). [10] System according to claim 6, wherein the disassembly device (120) is implemented as a cloud-based network application. [11] System according to claim 6, wherein the warning device is implemented as a cloud-based network application. [12] Cloud-based energy consumption warning platform (110), comprising: A data decomposition device (120) comprising a processor and a memory that stores instructions to be executed in the processor, wherein the instructions are configured to decompose location-specific energy consumption values ​​(121) according to a first characteristic and according to a second characteristic, wherein the decomposition of the energy consumption values ​​(121) according to the first characteristic includes decomposition of the energy consumption values ​​(121) by performing a date-based decomposition type, wherein the instructions configured to decompose the energy consumption values ​​(121) according to the second characteristic include instructions configured to decompose the energy consumption values ​​(121) by means of a seasonal decomposition into the components: trend, season, and remainder, in order to identify changes in energy consumption that are based on seasonal effects and changes that are based on a general trend.wherein the location-specific energy consumption values ​​are related to a location of a monitored site and are provided to the cloud-based energy consumption alert platform (110) via at least one data network (180); and, a warning device (130) comprising a processor and memory which stores instructions for execution in the processor, wherein the instructions are configured to compare a first decomposed energy consumption value (123) of the location-specific energy consumption values ​​(121) with a corresponding first reference value based on the decomposed values, wherein the first, decomposed energy consumption value (123) based on the first characteristic and on the residual component to identify a change in energy consumption that is due to unintentional energy consumption, and to output a message to notify a user of the unintentional energy consumption if the first decomposed energy consumption value (123) and the determined corresponding first reference value are different from each other. [13] Cloud-based energy consumption warning platform according to claim 12, further comprising: a web-based user interface module (140) that is configured to notify the user when the first decomposed energy consumption value and the determined first reference value differ from each other. [14] Cloud-based energy consumption warning platform according to claim 13, wherein the web-based user interface module (140) comprises an interactive website. [15] Cloud-based energy consumption warning platform according to claim 13, wherein the web-based user interface module (140) comprises an app for a mobile device. [16] Cloud-based energy consumption warning platform according to claim 13, wherein the web-based user interface module (140) comprises a web service. [17] Cloud-based energy consumption warning platform according to claim 13, wherein the web-based user interface module (140) includes an automated notification service.

Images