Business building energy consumption monitoring method, program product, electronic equipment and system

Abstract

The invention belongs to the technical field of energy consumption monitoring, and provides a business building energy consumption monitoring method, a program product, electronic equipment and a system. The energy consumption monitoring method comprises the following steps: acquiring an energy consumption characteristic factor set and actual energy consumption of each energy consumption monitoring area; inputting the energy consumption characteristic factor set into a corresponding energy consumption prediction model to obtain predicted energy consumption; calculating an energy consumption abnormal coefficient based on the actual energy consumption and the predicted energy consumption of each energy consumption monitoring area; generating a color coding value of each energy consumption monitoring area based on the energy consumption abnormal coefficient; and performing color rendering on the corresponding model components in the commercial building digital twin model according to the color coding values to obtain a digital twin energy consumption thermodynamic diagram model and output an energy consumption thermodynamic display diagram. The invention further provides a computer program product, electronic equipment and a business building energy consumption monitoring system. The energy consumption abnormal condition is accurately fed back by using the energy consumption abnormal coefficient, and the energy consumption abnormal degree can be visually checked through the energy consumption thermal display diagram.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption monitoring, and in particular to a commercial building energy consumption monitoring method, program product, electronic equipment and system. Background Art

[0002] With the rapid development of industrialization and urbanization, energy consumption has increased significantly, and energy conservation and environmental protection are becoming increasingly important. Energy management in commercial buildings is also receiving increasing attention. Commercial buildings are buildings that provide space, facilities, and supporting services for business and office activities. They are characterized by high population density and zoned energy consumption.

[0003] In related technologies, commercial buildings have established energy consumption management platforms that use daily year-over-year (YoY) data to determine whether a building's energy consumption is abnormal. However, energy usage is dynamic, influenced by factors such as personnel mobility, equipment usage fluctuations, and seasonal temperature variations. Relying solely on daily year-over-year data to determine whether energy consumption is abnormal is not very accurate, and cannot pinpoint specific areas of abnormal energy consumption in commercial buildings, leading to numerous misjudgments and wasted human resources. Summary of the Invention

[0004] The present application aims to at least solve the technical problems existing in the related technologies and provide a commercial building energy consumption monitoring method, program product, electronic equipment and system.

[0005] In the first aspect, the present application provides a method for monitoring energy consumption in a commercial building, comprising: obtaining an energy consumption characteristic factor set and actual energy consumption of each energy consumption monitoring zone in the commercial building; wherein, there are multiple energy consumption monitoring zones in the commercial building; the energy consumption characteristic factor set of each energy consumption monitoring zone includes outdoor temperature, outdoor light intensity, and at least two of the six energy consumption characteristic factors of the number of people in the energy consumption monitoring zone, the number of energy-consuming equipment turned on, the continuous operation time of the energy-consuming equipment, and the operating temperature of the energy-consuming equipment; inputting the energy consumption characteristic factor set of each energy consumption monitoring zone into the energy consumption prediction model corresponding to each energy consumption monitoring zone to obtain the predicted energy consumption of each energy consumption monitoring zone; calculating the energy consumption anomaly coefficient of each energy consumption monitoring zone based on the actual energy consumption and predicted energy consumption of each energy consumption monitoring zone; generating a color coding value for each energy consumption monitoring zone based on the energy consumption anomaly coefficient of each energy consumption monitoring zone; in a pre-constructed digital twin model of the commercial building, color rendering is performed on the model components corresponding to each energy consumption monitoring zone according to the color coding value of each energy consumption monitoring zone to obtain a digital twin energy consumption heat map model; and outputting an energy consumption heat display map based on the digital twin energy consumption heat map model.

[0006] In a second aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of a commercial building energy consumption monitoring method described in the first aspect.

[0007] In a third aspect, the present application provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a commercial building energy consumption monitoring method as described in the first aspect of the present invention.

[0008] In a fourth aspect, the present application provides a commercial building energy consumption monitoring system, comprising: a commercial building energy consumption monitoring system, characterized in that it comprises: monitoring equipment, including a human detection module for obtaining the number of people in each energy consumption monitoring area in the commercial building, and a light sensor and a temperature sensor located outside the commercial building; energy-consuming equipment in each energy consumption monitoring area; metering equipment, used to monitor the working information of the energy-consuming equipment in each energy consumption monitoring area, the working information including the continuous operation time of the energy-consuming equipment, the operating temperature of the energy-consuming equipment and the actual energy consumption of the electrical equipment; a data acquisition module, used to collect output data of the monitoring equipment and the metering equipment; a building energy consumption anomaly analysis module, connected to the data acquisition module, to execute the steps of a commercial building energy consumption monitoring method described in the first aspect; an intelligent control module, used to display the energy consumption thermal display diagram obtained by the building energy consumption anomaly analysis module.

[0009] The beneficial technical effects of the present application are as follows: the energy consumption characteristic factor set of each energy consumption monitoring area is input into the corresponding energy consumption prediction model to obtain the predicted energy consumption, the energy consumption anomaly coefficient is calculated based on the actual energy consumption and predicted energy consumption of each energy consumption monitoring area, the color coding value of the energy consumption monitoring area is generated based on the energy consumption anomaly coefficient, the model components corresponding to the energy consumption monitoring area in the digital twin model of the commercial building are rendered using the color coding value to obtain a digital twin energy consumption thermal map model, and an energy consumption thermal display map is output. The energy consumption anomaly coefficient is calculated based on the actual energy consumption and predicted energy consumption of each energy consumption monitoring area. Compared with the traditional day-to-day energy consumption year-on-year method, it can more accurately feedback the energy consumption anomaly of each energy consumption monitoring area and reduce misjudgments; and the energy consumption thermal display map can intuitively check the degree of energy consumption anomaly in each energy consumption monitoring area, helping managers to locate energy consumption anomaly areas more quickly and improve management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flow chart of a commercial building energy consumption monitoring method in a preferred embodiment of the present invention;

[0011] Figure 2 It is a detailed data flow diagram of a commercial building energy consumption monitoring method in a preferred embodiment of the present invention;

[0012] Figure 3is a flow chart of a commercial building energy consumption monitoring method in an example of the present invention;

[0013] Figure 4 This is a schematic structural diagram of an electronic device in a preferred embodiment of the present invention;

[0014] Figure 5 This is an architectural diagram of a commercial building energy consumption monitoring system in a preferred embodiment of the present invention;

[0015] Figure 6 It is a schematic diagram of an energy consumption and thermal display diagram in an example of the present invention. DETAILED DESCRIPTION

[0016] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0017] In the description of the present invention, it should be understood that the terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0018] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal communication between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0019] The execution subject of a commercial building energy consumption monitoring method provided by the present invention includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, a commercial building energy consumption monitoring method can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] The present invention provides a commercial building energy consumption monitoring method. In a preferred embodiment, Figure 1 Shown, including:

[0021] Step S1: Obtaining a set of energy consumption characteristic factors and actual energy consumption for each energy consumption monitoring zone within a commercial building; wherein the commercial building has multiple energy consumption monitoring zones; the set of energy consumption characteristic factors for each energy consumption monitoring zone includes at least two of six energy consumption characteristic factors: outdoor temperature, outdoor light intensity, and the number of people within the energy consumption monitoring zone, the number of energy-consuming devices turned on, the continuous operation time of the energy-consuming devices, and the operating temperature of the energy-consuming devices;

[0022] Step S2: Inputting the energy consumption characteristic factor set of each energy consumption monitoring area into the energy consumption prediction model corresponding to each energy consumption monitoring area to obtain the predicted energy consumption of each energy consumption monitoring area; the predicted energy consumption represents the normal energy consumption predicted value of the energy consumption monitoring area without unreasonable energy consumption, and is based on its historical normal actual energy consumption prediction;

[0023] Step S3, calculating the energy consumption anomaly coefficient of each energy consumption monitoring area based on the actual energy consumption and predicted energy consumption of each energy consumption monitoring area;

[0024] Step S4, generating a color coding value for each energy consumption monitoring area based on the energy consumption anomaly coefficient of each energy consumption monitoring area;

[0025] Step S5: In the pre-built digital twin model of the commercial building, the model components corresponding to each energy consumption monitoring area are color-rendered according to the color coding value of each energy consumption monitoring area to obtain a digital twin energy consumption heat map model. The digital twin model of the commercial building is preferably, but not limited to, generated using the existing Revit, EasyV, Shanhaijing visualization, or Dycharts digital twin visualization platform. Each energy consumption monitoring area has a unique corresponding model component in the digital twin model, and the corresponding model component is composed of virtual components such as floors, walls, and doors that actually exist in the energy consumption monitoring area.

[0026] Step S6: Output an energy consumption thermal display diagram based on the digital twin energy consumption thermal diagram model. Specifically, the energy consumption thermal display diagram is an image of the digital twin energy consumption thermal diagram model showing as many energy consumption monitoring areas as possible. Figure 6 An energy consumption thermal display diagram in an example is shown. In the energy consumption thermal display diagram, the energy consumption abnormal coefficient of the warm color is greater than that of the cold color. The energy consumption abnormal coefficient of the red area is Figure 6 The energy consumption anomaly coefficient is displayed as different colors on the energy consumption thermal display diagram to reflect different levels of energy consumption anomaly. The digital twin energy consumption thermal display model links the energy consumption thermal display diagram with the 3D digital twin model, facilitating review and judgment by responsible personnel.

[0027] In order to better understand the technical solution of this application, Figure 2 A detailed diagram showing the data flow of the commercial building energy consumption monitoring method.

[0028] In this embodiment, the energy consumption monitoring area is an area in a commercial building where actual energy consumption can be independently monitored, preferably but not limited to a room or an office area of a company or the scope of each customized energy consumption monitoring area.

[0029] In this embodiment, the execution subject may start executing the above steps S1 to S6 when receiving the energy consumption monitoring instruction, or the execution subject may execute the above steps S1 to S6 regularly within the set monitoring time period, or the execution subject may cyclically execute steps S1 to S6 within the set monitoring time period.

[0030] In this embodiment, in the energy consumption characteristic factor set, the number of energy-consuming devices turned on represents the number of energy-consuming devices turned on in the energy consumption monitoring area; the continuous operation time of the energy-consuming devices represents the median or average operation time of all energy-consuming devices that have been running continuously in the energy consumption monitoring area to the current time. The operating temperature of the energy-consuming device can be obtained by collecting it through the temperature sensor built into the energy-consuming device or by collecting it through the temperature sensor deployed at the hot point of the shell of the energy-consuming device (the higher or highest point of the shell temperature). Specifically, the operating temperature of the energy-consuming device in the energy consumption monitoring area represents the median or average or maximum value of the operating temperature of all turned-on energy-consuming devices in the energy consumption monitoring area. The energy consumption characteristic factors in the energy consumption characteristic factor set can be selected based on experience, or can be selected using the principal component analysis method combined with the degree of contribution of the energy consumption characteristic factors to the prediction accuracy of the energy consumption prediction model.

[0031] In this embodiment, in order to exclude outliers and facilitate data processing, preferably, before the energy consumption characteristic factor set of each energy consumption monitoring area is input into the energy consumption prediction model corresponding to each energy consumption monitoring area in step S2, the energy consumption characteristic factor set is also pre-processed. The pre-processing preferably includes, but is not limited to: outlier identification and normalization processing. Outlier identification can specifically be to compare each energy consumption characteristic factor with its corresponding reference value interval. If the energy consumption characteristic factor falls within the corresponding reference value interval, the energy consumption characteristic factor is considered normal. If the energy consumption characteristic factor does not fall within the corresponding reference value interval, the energy consumption characteristic factor is considered abnormal. When there are abnormal energy consumption characteristic factors, the current energy consumption characteristic factor set is discarded, and the energy consumption characteristic factor set is re-collected for outlier identification. If the energy consumption characteristic factor is found to be abnormal for a preset number of consecutive times, it is reported to the manager terminal or management platform. Normalization processing refers to de-dimensionalizing each energy consumption characteristic factor and then performing numerical normalization processing, which can be normalized to the numerical interval [0, 1].

[0032] In this embodiment, in step S2, the energy consumption prediction model can be an existing weak regression learner. All energy consumption characteristic factors in the energy consumption characteristic factor set are used as input data of the weak regression learner, and the predicted energy consumption is used as the prediction data of the weak regression learner. The historical actual energy consumption corresponding to the collection time of the energy consumption characteristic factor set is used as the true value. The weak regression learner is trained by the historical energy consumption (normal energy consumption, non-irrational energy consumption) of the energy consumption monitoring area and the historical energy consumption characteristic factor set. The trained weak regression learner is used as the energy consumption prediction model. The weak regression learner is preferably, but not limited to, a decision tree regression model, a lasso regression model, a ridge regression model, or a support vector regression model.

[0033] In this embodiment, in step S3, the energy consumption anomaly coefficient of each energy consumption monitoring zone can be calculated by calculating the difference between the actual energy consumption and the predicted energy consumption of each energy consumption monitoring zone, linearly mapping the difference to a preset numerical interval, and using the mapping value of the difference in the preset numerical interval as the energy consumption anomaly coefficient of each energy consumption monitoring zone.

[0034] In a preferred embodiment, the present invention provides a commercial building energy consumption monitoring method, further comprising:

[0035] In the digital twin model or digital twin energy consumption heat map model, the actual energy consumption and predicted energy consumption of each energy consumption monitoring area are marked on the model component corresponding to each energy consumption monitoring area. This allows the digital twin model or digital twin energy consumption heat map model to display the energy consumption data corresponding to each energy consumption monitoring area for easy viewing.

[0036] In a preferred embodiment, in order to ensure the prediction reliability, stability and calculation simplicity of the energy consumption prediction model, the energy consumption prediction model of each energy consumption monitoring area is a multiple linear regression model established by the least squares method.

[0037] In this embodiment, the process of constructing the energy consumption prediction model for each energy consumption monitoring area includes:

[0038] Step a1: Determine the independent and dependent variables for the multivariate linear regression model. The independent variables include outdoor temperature, outdoor light intensity, and at least two of six energy consumption characteristic factors: the number of people in the energy consumption monitoring area, the number of energy-consuming devices turned on, the duration of continuous operation of energy-consuming devices, and the operating temperature of energy-consuming devices. Preferably, the independent variables include these six energy consumption characteristic factors. The dependent variable is the actual energy consumption of the energy consumption monitoring area during normal energy use. The values of the independent and dependent variables at multiple historical time points in the energy consumption monitoring area are collected to form a training dataset.

[0039] Step a2, construct a multiple linear regression equation based on the number of independent variables:

[0040] y=β1x1+…+β p x p +ε

[0041] Where y is the dependent variable, i.e., the predicted energy consumption of the energy consumption monitoring area; x1,...,x p represent p independent variables, p≥2; ε is the intercept term; β1,...,β p are p regression coefficients.

[0042] In step a3, statistical software (such as SPSS, R, Python, etc.) is used to determine the values of each regression coefficient and intercept term based on the training data set and the multiple linear regression equation, and a multiple linear regression model is established.

[0043] In this embodiment, a training data set within a time period before the current time may be regularly collected to update the energy consumption prediction model of each energy consumption monitoring area.

[0044] In this embodiment, commercial buildings are generally used for office work, and energy consumption varies significantly throughout the day. Therefore, to obtain more accurate energy consumption forecasts, it is further preferred that the energy consumption characteristic factor set for each energy consumption monitoring area also include energy consumption forecast time. The energy consumption forecast time can be expressed in the form of 0-24 hours. In the training data set of the energy consumption forecast model, the energy consumption forecast time can be the historical collection time of actual energy consumption.

[0045] In a preferred embodiment, in order to more objectively evaluate the energy consumption anomaly of the energy consumption monitoring area, the energy consumption anomaly coefficient is the energy consumption anomaly ratio. In step S3, the step of calculating the energy consumption anomaly coefficient of each energy consumption monitoring area based on the actual energy consumption and predicted energy consumption of each energy consumption monitoring area includes method one or method two.

[0046] Method 1: When the predicted energy consumption of each energy consumption monitoring area is not equal to 0, the ratio of the actual energy consumption of each energy consumption monitoring area to the predicted energy consumption is used as the energy consumption abnormality ratio of each energy consumption monitoring area.

[0047] Method 2: When the predicted energy consumption of each energy consumption monitoring area is not equal to 0, calculate the difference between the actual energy consumption and the predicted energy consumption of each energy consumption monitoring area, and use the ratio of the difference to the predicted energy consumption as the energy consumption abnormality ratio of each energy consumption monitoring area.

[0048] In the above-mentioned method 1 and method 2, when the predicted energy consumption of each energy consumption monitoring area is equal to 0, if the actual energy consumption is also 0, no processing is performed. If the actual energy consumption is greater than 0 and less than the preset energy consumption threshold, the current actual energy consumption and the energy consumption characteristic factor set are used as a set of historical training data to train and update the energy consumption prediction model of the energy consumption monitoring area. If the actual energy consumption is greater than or equal to the preset energy consumption threshold, indicating that there is a major abnormality in the energy consumption monitoring area that should have no energy, the energy consumption abnormality coefficient of the energy consumption monitoring area is configured to the maximum energy consumption abnormality coefficient currently calculated, and reported to the management personnel for verification so that the management personnel can quickly come to verify and avoid misjudgment. Exemplarily, the preset energy consumption threshold is 0.8 to 1 times the maximum energy consumption allowed for safe energy use in the energy consumption monitoring area.

[0049] In a preferred embodiment, in order to adaptively generate the color coding value of the energy consumption monitoring zone based on the energy consumption anomaly ratio of all currently acquired energy consumption monitoring zones, the present application establishes an energy consumption anomaly ratio standard, which can adaptively adjust the color coding value. The step of generating the color coding value of each energy consumption monitoring zone based on the energy consumption anomaly coefficient of each energy consumption monitoring zone in step S4, the implementation process based on the energy consumption anomaly ratio standard includes:

[0050] Step b1: Generate an energy consumption anomaly ratio interval based on the energy consumption anomaly ratios of multiple energy consumption monitoring areas within the commercial building. After calculating the energy consumption anomaly ratio at each time and space point (each energy consumption monitoring area), obtain the minimum value r{min} and the maximum value r{max} of all energy consumption anomaly ratios.

[0051] Step b2: Divide the energy consumption abnormality ratio interval into N non-overlapping sub-intervals, where N is a positive integer. The N non-overlapping sub-intervals can divide the energy consumption abnormality ratio interval equally or unequally. For example, if the energy consumption abnormality ratio interval is divided into sub-intervals from sparse to dense, the sub-intervals closer to the minimum energy consumption abnormality ratio will have a longer length, while the sub-intervals closer to the maximum energy consumption abnormality ratio will have a shorter length. This will make the color of the energy consumption monitoring area with larger energy consumption abnormalities more eye-catching and easier to observe.

[0052] Set N levels corresponding to N subintervals one by one;

[0053] Set N color coding values corresponding one by one to N levels.

[0054] Step b3, obtain the level corresponding to the sub - interval into which the energy consumption anomaly ratio of each energy consumption monitoring area falls, and use the color coding value corresponding to the obtained level as the color coding value of each energy consumption monitoring area.

[0055] In an example of this embodiment, N is 5. The energy consumption anomaly ratio interval (r{min}, r{max}) is equally divided into 5 sub - intervals, each sub - interval corresponds to a level, and different colors (color coding values) are used to represent anomalies of different severity levels. The formula for dividing the sub - intervals is:

[0056] The lower limit of the first - level sub - interval is (L1 = r{min}), and the upper limit is (U1 = r{min}+(r{max}-r{min}) / 5;

[0057] The lower limit of the second - level sub - interval is (L2 = r{min}+(r{max}-r{min}) / 5), and the upper limit is (U2 = r{min}+2*(r{max}-r{min}) / 5);

[0058] The lower limit of the third - level sub - interval is (L3 = r{min}+2*(r{max}-r{min}) / 5), and the upper limit is (U3 = r{min}+3*(r{max}-r{min}) / 5);

[0059] The lower limit of the fourth - level sub - interval (L4 = r{min}+3*(r{max}-r{min}) / 5), and the upper limit is (U4 = r{min}+4*(r{max}-r{min}) / 5);

[0060] The lower limit of the fifth - level sub - interval is (L5 = r{min}+4*(r{max}-r{min}) / 5), and the upper limit is (U5 = r{max}).

[0061] The color coding setting rule is:

[0062] When the energy consumption anomaly ratio belongs to the first - level sub - interval (L1≤r<U1), the corresponding color is green, indicating that the energy consumption situation is normal.

[0063] When the energy consumption anomaly ratio belongs to the second - level sub - interval (U1≤r<U2), the corresponding color is yellow, indicating that there is a mild energy consumption anomaly and attention is needed.

[0064] When the energy consumption anomaly ratio belongs to the third - level sub - interval (U2≤r<U3), the corresponding color is orange, indicating that there is a moderate energy consumption anomaly and further inspection and analysis are needed.

[0065] When the energy consumption anomaly ratio belongs to the 4th level sub - interval (U3 ≤ r < U4), the corresponding color is red, indicating that there is a serious energy consumption anomaly, and measures should be taken promptly for adjustment.

[0066] When the energy consumption anomaly ratio belongs to the 5th level sub - interval (U4 ≤ r ≤ U5), the corresponding color is purple, indicating that the energy consumption anomaly situation is extremely serious, and there may be equipment failures or other major problems.

[0067] In a preferred implementation, in order to highlight the differences in the energy consumption anomaly coefficients of each energy consumption monitoring area, facilitate relevant personnel to observe the specific distribution, and achieve more accurate decision - making. When the energy consumption anomaly coefficient is the energy consumption anomaly ratio, in step S4, the step of generating the color - coding value for each energy consumption monitoring area based on the energy consumption anomaly coefficient of each energy consumption monitoring area includes:

[0068] If the energy consumption anomaly ratio of an energy consumption monitoring area is less than or equal to the ratio threshold, then assign the color - coding value of this energy consumption monitoring area as the RGB component (0, 0, 255), which is blue. For method one, the ratio threshold is 1, and for method two, the ratio threshold is 0;

[0069] If the energy consumption anomaly ratio of the energy consumption monitoring area is greater than the ratio threshold, then assign the color - coding value of the energy consumption monitoring area as the RGB component (255, G i , 0). When the energy consumption anomaly ratio is obtained by method one, the G - channel component G i of the i - th energy consumption monitoring area = f(1 - e 1+vi ); when the energy consumption anomaly ratio is obtained by method two, the G - channel component G i of the i - th energy consumption monitoring area = f(1 - e vi ). Where, i represents the energy consumption monitoring area index and is a positive integer; v i represents the energy consumption anomaly ratio of the i - th energy consumption monitoring area; the function f(B) represents linearly mapping the variable B to the numerical interval [0, 255], B = (1 - e 1+vi ) or (1 - e vi ), f(1 - e 1 +vi ) or f(1 - e vi ) are both mapped to the numerical interval [0, 255] through the linear mapping function f(B). Among them, f(1 - e 1+vmax ) or f(1 - e vmax ) are both mapped to 0 through the function f(B), and f(1 - e 1+vτ ) or f(1 - e vτ ) are both mapped to 255 through the function f(B). v τ represents the ratio threshold, and v max represents the maximum value of the energy consumption anomaly ratios of all energy consumption monitoring areas.

[0070] In this embodiment, through the above color coding step, the color coding value of the energy consumption abnormality ratio less than or equal to the ratio threshold is set to RGB component (0, 0, 255), which is blue, indicating no abnormality; the color coding value corresponding to the energy consumption abnormality ratio greater than the ratio threshold is coded between yellow and red, and the monotonic increasing and nonlinear increasing properties of the exponential function are used. As the value approaches v max The more sensitive the color change, the closer it is to red, the more it highlights energy consumption monitoring areas with higher risks, more easily attracting the attention of relevant personnel, and making it easier to intuitively see the differences between energy consumption monitoring areas. The maximum energy consumption abnormality ratio is coded in red, and when the energy consumption abnormality ratio reaches the ratio threshold, it is coded in yellow. The color coding is adaptive to the energy consumption abnormality ratio calculated during each energy consumption monitoring.

[0071] Figure 3 A flowchart showing a commercial building energy consumption monitoring method in an example is shown, including:

[0072] A1: Real-time data collection and processing. Energy consumption data is collected at a regular frequency for each point in time and space (energy consumption monitoring area) within a commercial building. This data includes actual energy consumption values and their corresponding energy consumption characteristic factor sets. These energy consumption characteristic factor sets include various relevant characteristic data that influence actual energy consumption, such as equipment operating time, operating temperature, number of users, number of devices powered on, and outdoor temperature. The collected data is cleaned and preprocessed to remove outliers and missing values. This energy consumption characteristic factor set is then constructed and stored in an energy consumption database in accordance with relevant standards and specifications.

[0073] A2: Based on energy consumption data, a multivariate linear regression model is established for the building's energy consumption characteristics at each time and space point. This model is continuously trained to produce predicted energy consumption values (i.e., reasonable energy usage values for that time and space). The predicted energy consumption values may change based on the continuous input of the energy consumption characteristic dataset.

[0074] A3: Compare the energy consumption forecast value with the actual energy consumption value at each time to obtain the energy consumption anomaly ratio at each time and space, establish an energy consumption anomaly ratio database, and store the energy consumption anomaly ratio in the database.

[0075] A4: Establish an adaptive adjustment algorithm for the energy consumption anomaly ratio standard. The energy consumption anomaly ratio in the database can be automatically divided into 5 levels according to the actual energy consumption usage, and represented by different colors to reflect anomalies of different severity.

[0076] A5: Build a 3D digital twin model to map the physical space to the virtual space and bind it with energy consumption data;

[0077] A6: Reflect the standard value of the energy consumption abnormality ratio on the energy consumption heat map, and bind the heat map with the digital twin model to obtain the digital twin energy consumption heat map model, and output the energy consumption heat display map based on the digital twin energy consumption heat map model, that is, the optimal heat map.

[0078] This application also discloses a commercial building energy consumption monitoring system, such as Figure 5 As shown, in a preferred embodiment, the system includes:

[0079] The monitoring equipment includes a human detection module for detecting the number of people within each energy consumption monitoring zone in a commercial building, as well as a light sensor and a temperature sensor located outside the commercial building. The human detection module preferably includes, but is not limited to, a camera installed in each energy consumption monitoring zone and an image processing module. The camera captures images of the energy consumption monitoring zone, and the image processing module performs human target recognition on the images captured by the camera and counts the number of recognized targets. The image processing module preferably includes, but is not limited to, an existing object detection network or instance segmentation network, such as the YOLO series.

[0080] Energy-consuming equipment in each energy consumption monitoring area. Energy-consuming equipment preferably includes, but is not limited to, smart air conditioning equipment, smart lighting equipment, and smart screens.

[0081] Metering equipment is used to monitor the operating information of energy-consuming equipment within each energy consumption monitoring zone. This information includes the equipment's continuous operating time, operating temperature, and actual energy consumption. Metering equipment may include smart meters in each energy consumption monitoring zone, energy meters that detect operating information on each energy-consuming equipment, and temperature sensors attached to the housing.

[0082] The data collection module collects output data from monitoring and metering devices. It uses the IoT module, such as MQTT, or a physical model-based approach to collect energy consumption data from multiple energy consumption monitoring areas in commercial buildings. The collected data is preprocessed to obtain historical energy consumption and a set of historical energy consumption characteristic factors for each energy consumption monitoring area.

[0083] The building energy consumption anomaly analysis module is connected to the data acquisition module to execute the steps of the above-mentioned commercial building energy consumption monitoring method.

[0084] The intelligent control module is used to display the energy consumption thermal display diagram obtained by the building energy consumption anomaly analysis module.

[0085] In this embodiment, if Figure 3 As shown, the system also includes:

[0086] The energy consumption database of the Internet of Things platform is used to store the data collected by the data acquisition module.

[0087] The regression analysis module is connected to the energy consumption database of the Internet of Things platform and generates an energy consumption prediction model corresponding to each energy consumption monitoring area based on the historical energy consumption and historical energy consumption characteristic factor set of each energy consumption monitoring area.

[0088] This method uses correlation calculations to analyze various factors that influence unreasonable energy consumption, finds a reasonable predicted energy consumption value, and then compares it with the actual energy consumption value to obtain an outlier comparison standard. Compared with the traditional method of presenting heat map using only daily or year-on-year values, it provides more accurate feedback on energy consumption anomalies. At the same time, by binding the heat map to the digital twin model and applying it to commercial buildings, the degree of energy consumption anomalies in each area can be intuitively viewed through the color of the energy consumption heat map, helping managers to locate areas of energy consumption anomalies more quickly.

[0089] The present invention also discloses a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned commercial building energy consumption monitoring method provided by the present invention. The computer program product should be understood as a software product that mainly implements its solution through a computer program, such as a program product integrated in the cloud or a software library.

[0090] The present invention also discloses an electronic device. In one embodiment, the electronic device includes at least one processor; and a memory connected to the at least one processor; wherein,

[0091] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute a commercial building energy consumption monitoring method provided by the present invention.

[0092] like Figure 4 FIG2 is a schematic diagram of the structure of an electronic device for implementing a commercial building energy consumption monitoring method according to an embodiment of the present invention. The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for implementing a commercial building energy consumption monitoring method.

[0093] In some embodiments, the processor 10 may be composed of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes or executes programs or modules stored in the memory 11 (for example, executing a commercial building energy consumption monitoring method) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0094] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of a commercial building energy consumption monitoring method program, but can also be used to temporarily store data that has been output or is to be output.

[0095] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0096] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, for displaying information processed in the electronic device and for displaying a visual user interface.

[0097] Figure 4 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 4 The structure shown does not limit the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0098] For example, although not shown, the electronic device may further include a power source (such as a battery) for powering various components. Preferably, the power source may be logically connected to at least one processor 10 via a power management device, thereby implementing functions such as charge management, discharge management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0099] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0100] Furthermore, if the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0101] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "example," "specific example," "one implementation," "a preferred implementation," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0102] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A commercial building energy consumption monitoring method, characterized in that: include: Obtain energy consumption characteristic factor sets and actual energy consumption for each energy consumption monitoring zone within a commercial building; wherein the commercial building has multiple energy consumption monitoring zones; the energy consumption characteristic factor set for each energy consumption monitoring zone includes outdoor temperature, outdoor light intensity, and at least two of six energy consumption characteristic factors: the number of people within the energy consumption monitoring zone, the number of energy-consuming devices turned on, the continuous operating time of the energy-consuming devices, and the operating temperature of the energy-consuming devices; Input the energy consumption characteristic factor set of each energy consumption monitoring area into the energy consumption prediction model corresponding to each energy consumption monitoring area to obtain the predicted energy consumption of each energy consumption monitoring area; Calculate the energy consumption anomaly coefficient of each energy consumption monitoring area based on the actual energy consumption and predicted energy consumption of each energy consumption monitoring area; Generate a color coding value for each energy consumption monitoring area based on the energy consumption anomaly coefficient of each energy consumption monitoring area; In the pre-built digital twin model of the commercial building, the model components corresponding to each energy consumption monitoring area are rendered according to the color coding value of each energy consumption monitoring area to obtain a digital twin energy consumption heat map model; Output energy consumption thermal display diagram based on digital twin energy consumption thermal diagram model.

2. A commercial building energy consumption monitoring method according to claim 1, characterized in that: The method further comprises: In the digital twin model or the digital twin energy consumption heat map model, the actual energy consumption and predicted energy consumption of each energy consumption monitoring area are marked on the model component corresponding to each energy consumption monitoring area.

3. A commercial building energy consumption monitoring method according to claim 1, characterized in that: The energy consumption prediction model for each energy consumption monitoring area is a multiple linear regression model established by the least squares method.

4. A commercial building energy consumption monitoring method according to claim 1, characterized in that: The energy consumption characteristic factor set of each energy consumption monitoring area also includes energy consumption prediction time.

5. A commercial building energy consumption monitoring method according to any one of claims 1 to 4, characterized in that: The energy consumption anomaly coefficient is an energy consumption anomaly ratio. The step of calculating the energy consumption anomaly coefficient of each energy consumption monitoring area based on the actual energy consumption and predicted energy consumption of each energy consumption monitoring area includes: When the predicted energy consumption of each energy consumption monitoring area is not equal to 0, the ratio of the actual energy consumption of each energy consumption monitoring area to the predicted energy consumption is used as the energy consumption abnormality ratio of each energy consumption monitoring area; Alternatively, when the predicted energy consumption of each energy consumption monitoring area is not equal to 0, the difference between the actual energy consumption and the predicted energy consumption of each energy consumption monitoring area is calculated, and the ratio of the difference to the predicted energy consumption is used as the energy consumption abnormality ratio of each energy consumption monitoring area.

6. A commercial building energy consumption monitoring method according to claim 5, characterized in that: The step of generating a color coding value for each energy consumption monitoring area based on the energy consumption anomaly coefficient of each energy consumption monitoring area includes: Generate energy consumption abnormality ratio intervals based on energy consumption abnormality ratios of multiple energy consumption monitoring areas in a commercial building; Divide the energy consumption abnormal ratio interval into N non-overlapping sub-intervals, where N is a positive integer; Set N levels corresponding to N subintervals one by one; Set N color coding values corresponding to N levels one by one; The level corresponding to the subinterval into which the energy consumption anomaly ratio of each energy consumption monitoring zone falls is obtained, and the color coding value corresponding to the obtained level is used as the color coding value of each energy consumption monitoring zone.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of a commercial building energy consumption monitoring method as described in any one of claims 1 to 6 are implemented.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, A memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute a commercial building energy consumption monitoring method as described in any one of claims 1 to 6.

9. A commercial building energy consumption monitoring system, characterized in that: include: Monitoring equipment, including a human detection module for obtaining the number of people in each energy consumption monitoring zone in the commercial building, and a light sensor and a temperature sensor located outside the commercial building; Energy-consuming equipment in each energy consumption monitoring area; Metering equipment is used to monitor the operating information of energy-consuming equipment in each energy consumption monitoring area. The operating information includes the continuous operation time of the energy-consuming equipment, the operating temperature of the energy-consuming equipment, and the actual energy consumption of the electrical equipment; Data acquisition module, used to collect output data of monitoring equipment and metering equipment; a building energy consumption anomaly analysis module connected to the data acquisition module, and executing the steps of a commercial building energy consumption monitoring method according to any one of claims 1 to 6; The intelligent control module is used to display the energy consumption thermal display diagram obtained by the building energy consumption anomaly analysis module.

10. A commercial building energy consumption monitoring system according to claim 9, characterized in that: Also includes: The energy consumption database of the Internet of Things platform is used to store the data collected by the data acquisition module; The regression analysis module is connected to the energy consumption database of the Internet of Things platform and generates an energy consumption prediction model corresponding to each energy consumption monitoring area based on the historical energy consumption and historical energy consumption characteristic factor set of each energy consumption monitoring area.

Images