Building energy consumption prediction method and system based on multi-factor decomposition, terminal and medium

Abstract

The invention discloses a building energy consumption prediction method and system based on multi-factor decomposition, a terminal and a medium, and relates to the technical field of building energy consumption prediction. According to the technical scheme, multiple factors influencing building energy consumption are divided into environmental conditions considered from a global perspective, energy consumption activities considered from a local perspective and behavior characteristics considered from details; under the condition that the overall trend that the building energy consumption is influenced by the environmental conditions is represented through the basic power utilization data and the power utilization variable coefficient function, the correlation influence among the energy consumption activity, behavior characteristics and the environmental conditions is mined, and accurate prediction of the building energy consumption can be achieved through limited sample data.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy consumption prediction, and more specifically, to a building energy consumption prediction method, system, terminal and medium based on multi-factor decomposition. Background Art

[0002] Building energy consumption mainly refers to the electrical energy consumed by a building during its normal operation to meet the needs of internal environmental comfort, lighting, equipment operation, etc. The forecast of building energy consumption can provide basic data for formulating energy-saving measures and power peak regulation.

[0003] There are many factors that affect building energy consumption, such as building design parameters, climate conditions, and usage behavior. Building design parameters include building shape and orientation, lighting and ventilation design, thermal insulation treatment of building envelopes, etc. The building energy consumption prediction methods recorded in the prior art mainly use prediction models based on machine learning algorithms and deep learning algorithms for prediction. Prediction models based on machine learning algorithms include support vector regression, artificial neural networks, etc., and prediction models based on deep learning algorithms include recurrent neural networks, autoencoders, generative adversarial networks, etc. The above prediction models require a large amount of labeled sample data during the training process, and when the surrounding environment of the building is difficult to replicate, the reliability of the sample data is difficult to guarantee, which easily leads to poor prediction results when the above prediction models are applied to new buildings or buildings with obvious differences. In addition, in the case of limited sample data, it is difficult for the above prediction models to dig out the correlation between different influencing factors, resulting in poor accuracy in building energy consumption prediction.

[0004] Therefore, how to study and design a building energy consumption prediction method, system, terminal and medium based on multi-factor decomposition that can overcome the above-mentioned defects is a problem that we urgently need to solve. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the purpose of the present invention is to provide a building energy consumption prediction method, system, terminal and medium based on multi-factor decomposition, which divides the multiple factors affecting building energy consumption into environmental conditions considered from a global perspective, energy consumption activities considered from a local perspective, and behavioral characteristics considered from a detailed perspective. While characterizing the overall trend of building energy consumption affected by environmental conditions through basic electricity consumption data and electricity consumption variable coefficient functions, the correlation between energy consumption activities, behavioral characteristics and environmental conditions is also explored, so that accurate prediction of building energy consumption can be achieved through limited sample data.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions: In the first aspect, a building energy consumption prediction method based on multi-factor decomposition is provided, comprising the following steps: Collect hourly and daily electricity consumption data of each target building in the target area; Grid the target area, and divide the corresponding total daily electricity consumption data according to the total building area in each grid unit to obtain the initial grid electricity consumption data of the target area at the daily level; Obtaining energy consumption activity information of the target area at the daily level, and splitting the initial grid power consumption data into first grid power consumption data with energy consumption activities and second grid power consumption data without energy consumption activities; Extract the daily electricity consumption data affected by environmental conditions in the target area from the second grid electricity consumption data, and obtain the basic electricity consumption data and electricity consumption variable coefficient function of the target area based on the fitting analysis of multiple daily electricity consumption data in a preset period; The activity electricity consumption data affected by the energy consumption activity is calculated by the difference between the first grid electricity consumption data and the basic electricity consumption data, and the activity electricity consumption function is constructed based on the activity types and electricity consumption variable coefficients of the multiple activity electricity consumption data; The estimated electricity consumption data is calculated based on the product of the basic electricity consumption data and the building area of ​​the target building, and the daily fluctuation electricity consumption data of the corresponding target building is calculated based on the difference between the daily electricity consumption data and the estimated electricity consumption data. In addition, the deterministic electricity consumption fluctuation feature set of the target building at the hourly level is determined by combining the daily fluctuation electricity consumption data of the target building and the hourly electricity consumption data of multiple days; The energy consumption of the target building is predicted at the hourly level by combining basic electricity consumption data, electricity consumption variable coefficient function, activity electricity consumption function and deterministic electricity consumption fluctuation characteristics.

[0007] Furthermore, the process of performing weighted processing on the corresponding total daily electricity consumption data according to the total building area in each grid unit is specifically as follows: Divide the standard building area by the total building area in each grid unit to obtain the weighting coefficient of each grid unit; The total daily electricity consumption data of each grid unit is multiplied by the weighted coefficient to obtain the weighted electricity consumption data of each grid unit; The initial grid electricity consumption data of the target area at the daily level is constructed based on the combination of the net power consumption data of each grid unit.

[0008] Furthermore, the calculation formula of the daily electricity consumption data is specifically as follows: ; or, as: ; in, Indicates that the target area is Daily electricity consumption data affected by environmental conditions; Indicates The grid cell in Daily ex-rights electricity consumption data; Indicates the number of grid cells in the second grid power consumption data.

[0009] Furthermore, the expressions of the basic electricity consumption data and the electricity consumption variable coefficient function of the target area are specifically as follows: ; in, Indicates that the target area is Daily electricity consumption data affected by environmental conditions; Indicates the standard building area; Indicates the basic electricity consumption data of the target area; Indicates The daily electricity consumption coefficient; Represents the electricity consumption variable coefficient function.

[0010] Furthermore, the expression of the activity power consumption function is specifically: ; in, Indicates Type of activity per day The corresponding activity electricity consumption data; represents the activity power consumption function; Indicates The daily electricity consumption coefficient.

[0011] Furthermore, the calculation formula of the deterministic power consumption fluctuation feature set is specifically: ; in, Indicates the target building In the Daily electricity consumption data for each day; Indicates the basic electricity consumption data of the target area; Indicates the target building of building area; Indicates the target building In the Daily fluctuation electricity consumption data of each day; Indicates the target building In the Random daily electricity consumption fluctuation characteristics; Indicates the target building In the Daily The power consumption fluctuation characteristics of the target building in different days are different for the same hour. The corresponding deterministic power consumption fluctuation characteristics are taken as 0. Indicates the quantity of daily electricity consumption data; Indicates the target building In the Deterministic electricity consumption fluctuation characteristics for 1 hour; Indicates the target building In the Daily The fluctuation characteristics of electricity consumption in one hour; Indicates the target building Deterministic electricity consumption fluctuation feature set at hourly level; Indicates the target building Deterministic electricity consumption fluctuation characteristics in the first hour; Indicates the target building Deterministic electricity consumption fluctuation characteristics at the 24th hour.

[0012] Furthermore, the process of realizing the hourly energy consumption prediction of the target building by combining the basic electricity consumption data, the electricity consumption variable coefficient function, the activity electricity consumption function and the deterministic electricity consumption fluctuation characteristics is specifically as follows: The basic electricity consumption data and the electricity consumption variable coefficient function are used to predict the basic electricity consumption data of the target area in the next day; Estimate the activity electricity consumption data of the target area in the next day through the activity reporting information and activity electricity consumption function; Combined with the target area's basic electricity consumption data for the next day, active electricity consumption data, and the deterministic electricity consumption fluctuation feature set of each target building, the energy consumption forecast result of the target building at the hourly level is determined.

[0013] In a second aspect, a building energy consumption prediction system based on multi-factor decomposition is provided, the system is used to implement the building energy consumption prediction method based on multi-factor decomposition as described in any one of the first aspects, including: A data collection module is used to collect hourly and daily electricity consumption data of each target building in the target area; The data weighting module is used to perform grid processing on the target area and weight-reduced processing on the corresponding total daily electricity consumption data according to the total building area in each grid unit to obtain the initial grid electricity consumption data of the target area at the daily level; A data splitting module is used to obtain the energy consumption activity information of the target area at the daily level, and split the initial grid power consumption data into the first grid power consumption data with energy consumption activity and the second grid power consumption data without energy consumption activity; An environmental analysis module is used to extract daily electricity consumption data affected by environmental conditions in the target area from the second grid electricity consumption data, and obtain basic electricity consumption data and electricity consumption variable coefficient function of the target area based on fitting analysis of multiple daily electricity consumption data in a preset period; An activity analysis module, configured to calculate the activity electricity consumption data affected by the energy consumption activity by using the difference between the first grid electricity consumption data and the basic electricity consumption data, and to construct an activity electricity consumption function based on the activity types and electricity consumption variable coefficients of the plurality of activity electricity consumption data; The fluctuation analysis module is used to calculate the estimated electricity consumption data based on the product of the basic electricity consumption data and the building area of ​​the target building, and calculate the daily fluctuation electricity consumption data of the corresponding target building based on the difference between the daily electricity consumption data and the estimated electricity consumption data, and determine the deterministic electricity consumption fluctuation feature set of the target building at the hourly level by combining the daily fluctuation electricity consumption data of the target building and the hourly electricity consumption data of multiple days; The energy consumption prediction module is used to combine basic electricity consumption data, electricity consumption variable coefficient function, activity electricity consumption function and deterministic electricity consumption fluctuation characteristics to realize the energy consumption prediction of the target building at the hourly level.

[0014] In a third aspect, a computer terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a building energy consumption prediction method based on multi-factor decomposition as described in any one of the first aspects is implemented.

[0015] In a fourth aspect, a computer-readable medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement the building energy consumption prediction method based on multi-factor decomposition as described in any one of the first aspects.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The building energy consumption prediction method based on multi-factor decomposition provided by the present invention divides the multiple factors affecting building energy consumption into environmental conditions considered from a global perspective, energy consumption activities considered from a local perspective, and behavioral characteristics considered from a detailed perspective. While characterizing the overall trend of building energy consumption affected by environmental conditions through basic electricity consumption data and electricity consumption variable coefficient functions, the correlation between energy consumption activities, behavioral characteristics and environmental conditions is also excavated, and accurate prediction of building energy consumption can be achieved through limited sample data; 2. When the present invention characterizes the overall trend of building energy consumption affected by environmental conditions through basic electricity consumption data and electricity consumption variable coefficient function, it integrates multiple influencing factors into a single factor for fitting prediction when the basic electricity consumption data remains unchanged within a certain period, thereby simplifying the difficulty of overall trend prediction; 3. When analyzing the deterministic electricity consumption fluctuation feature set, the present invention can adaptively update the deterministic electricity consumption fluctuation features according to the changes in the basic electricity consumption data. In the process of trend changes or oscillation changes in environmental conditions, it can effectively improve the prediction accuracy of building energy consumption at the hourly level. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 This is a flow chart of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary implementation modes of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0019] Example 1: Building energy consumption prediction method based on multi-factor decomposition, such as Figure 1 As shown, the following steps are included: S1: Collect hourly and daily electricity consumption data of each target building in the target area; S2: Grid the target area, and divide the corresponding total daily electricity consumption data according to the total building area in each grid unit to obtain the initial grid electricity consumption data of the target area at the daily level; S3: Acquire energy consumption activity information of the target area at the daily level, and split the initial grid power consumption data into first grid power consumption data with energy consumption activities and second grid power consumption data without energy consumption activities; S4: extracting daily electricity consumption data affected by environmental conditions in the target area from the second grid electricity consumption data, and obtaining basic electricity consumption data and electricity consumption variable coefficient function of the target area based on fitting analysis of multiple daily electricity consumption data in a preset period; S5: Calculate the activity electricity consumption data affected by the energy consumption activity by using the difference between the first grid electricity consumption data and the basic electricity consumption data, and construct an activity electricity consumption function based on the activity types and electricity consumption variable coefficients of the multiple activity electricity consumption data; S6: Estimated electricity consumption data is calculated based on the product of the basic electricity consumption data and the building area of ​​the target building, and the daily fluctuation electricity consumption data of the corresponding target building is calculated based on the difference between the daily electricity consumption data and the estimated electricity consumption data, and the deterministic electricity consumption fluctuation feature set of the target building at the hourly level is determined by combining the daily fluctuation electricity consumption data of the target building and the hourly electricity consumption data of multiple days; S7: Combine basic electricity consumption data, electricity consumption variable coefficient function, activity electricity consumption function and deterministic electricity consumption fluctuation characteristics to realize energy consumption prediction of target buildings at the hourly level.

[0020] In step S1, the division of the target area can be determined based on the number of buildings or the area, which is not limited here. Generally, the number of buildings in the target area can reach hundreds or thousands.

[0021] It should be noted that the target buildings in a target area need to be of the same type, such as all residential buildings or all commercial buildings.

[0022] The hourly electricity consumption data collected by the present invention refers to data collected in one hour, and the data of one day constitutes an hourly sequence. The daily electricity consumption data refers to data collected once a day, and the data collected multiple times in a cycle constitutes a daily sequence.

[0023] In step S2, when the target area is gridded, it can be evenly divided with the same specifications, or it can be unevenly divided according to the distribution of the target buildings, which is not limited here.

[0024] Taking into account the differences in the building areas of the corresponding target buildings in each grid unit, this will also lead to differences in electricity consumption data. To this end, in order to perform data processing in the same dimension. The present invention performs weighted processing on the corresponding total daily electricity consumption data based on the total building area in each grid unit, and the specific process is: divide the standard building area by the total building area in each grid unit to obtain the weighted coefficient of each grid unit; multiply the total daily electricity consumption data of each grid unit by the weighted coefficient to obtain the weighted electricity consumption data of each grid unit; and combine the weighted electricity consumption data of each grid unit to form the initial grid electricity consumption data of the target area at the daily level.

[0025] It should be noted that the standard building area can be the area of ​​a basic unit, that is, the standard building area is regarded as "1". The standard building area can also be other values, which are not limited here.

[0026] In step S3, energy consumption activity information refers to activities that have energy consumption behaviors and have been reported, such as concerts, commercial performances, etc. Since the distance of an activity can easily cause drastic fluctuations in power consumption data, the initial grid power consumption data needs to be split into the first grid power consumption data with energy consumption activities and the second grid power consumption data without energy consumption activities.

[0027] In step S4, although the second grid power consumption data does not include the overall data of the target area, the purpose of the present invention is to analyze the overall trend, so the average power consumption level determined by the second grid power consumption data is used to equivalently replace the average power consumption level of the target area.

[0028] As an optional implementation, the calculation formula for daily electricity consumption data is specifically: ; in, Indicates that the target area is Daily electricity consumption data affected by environmental conditions; Indicates The grid cell in Daily ex-rights electricity consumption data; Indicates the number of grid cells in the second grid power consumption data.

[0029] As another optional implementation, in addition to using the average value as the average power consumption level, the value under the minimum fluctuation condition can also be used as the average power consumption level. The calculation formula for daily power consumption data is specifically as follows: ; in, Indicates that the target area is Daily electricity consumption data affected by environmental conditions; Indicates The grid cell in Daily ex-rights electricity consumption data; Indicates the number of grid cells in the second grid power consumption data.

[0030] In this embodiment, the basic electricity consumption data of the target area and the expression of the electricity consumption variable coefficient function are specifically: ; in, Indicates that the target area is Daily electricity consumption data affected by environmental conditions; Indicates the standard building area; Indicates the basic electricity consumption data of the target area; Indicates The daily electricity consumption coefficient; Represents the electricity consumption variable coefficient function.

[0031] It should be noted that the power variable coefficient function generally adopts a nonlinear function, such as ,The specific expression of the electricity consumption variable coefficient function is determined by the historical data analysis, and the changing trend of the electricity consumption variable coefficient function is also determined by the historical data.

[0032] In step S5, the activity electricity consumption function can be constructed using linear regression, decision tree, deep learning and other methods. The independent variables of the activity electricity consumption function include activity type and electricity consumption variable coefficient, and the dependent variable is the activity electricity consumption data.

[0033] The expression of the activity power consumption function is as follows: ; in, Indicates Type of activity per day The corresponding activity electricity consumption data; represents the activity power consumption function; Indicates The daily electricity consumption coefficient.

[0034] It should be noted that the activity electricity consumption function is also a nonlinear function. The specific expression of the activity electricity consumption function is determined by historical data analysis, and its changing trend is also determined by historical data.

[0035] In step S6, the calculation formula of the deterministic power consumption fluctuation feature set is specifically: ; in, Indicates the target building In the Daily electricity consumption data for each day; Indicates the basic electricity consumption data of the target area; Indicates the target building of building area; Indicates the target building In the Daily fluctuation electricity consumption data of each day; Indicates the target building In the Random daily electricity consumption fluctuation characteristics; Indicates the target building In the Daily The power consumption fluctuation characteristics of the target building in different days are different for the same hour. The corresponding deterministic power consumption fluctuation characteristics are taken as 0. Indicates the quantity of daily electricity consumption data; Indicates the target building In the Deterministic electricity consumption fluctuation characteristics for 1 hour; Indicates the target building In the Daily The fluctuation characteristics of electricity consumption in one hour; Indicates the target building Deterministic electricity consumption fluctuation feature set at hourly level; Indicates the target building Deterministic electricity consumption fluctuation characteristics in the first hour; Indicates the target building Deterministic electricity consumption fluctuation characteristics at the 24th hour.

[0036] It should be noted that the daily fluctuation power consumption data is composed of random power consumption fluctuation characteristics and deterministic power consumption fluctuation characteristics. When dividing the deterministic power consumption fluctuation characteristics and the random power consumption fluctuation characteristics, the first Daily and You can also compare the first Daily and In order to ensure the stability of the prediction results, it is necessary to dig out as many deterministic electricity consumption fluctuation characteristics as possible, so the minimum sum of the absolute values ​​of random electricity consumption fluctuation characteristics is required as a constraint.

[0037] In step S7, the process of realizing the energy consumption prediction of the target building at the hourly level by combining the basic electricity consumption data, the electricity consumption variable coefficient function, the activity electricity consumption function and the deterministic electricity consumption fluctuation characteristics is specifically as follows: the basic electricity consumption data of the target area in the next day is predicted by using the basic electricity consumption data and the electricity consumption variable coefficient function; the activity electricity consumption data of the target area in the next day is estimated by using the activity reporting information and the activity electricity consumption function; the energy consumption prediction result of the target building at the hourly level is determined by combining the basic electricity consumption data of the target area in the next day, the activity electricity consumption data and the deterministic electricity consumption fluctuation feature set of each target building.

[0038] Specifically, the sum of the basic electricity consumption data and the active electricity consumption data of the target area in the next day can be used to calculate the overall electricity consumption of the target area in the next day. Combined with the deterministic electricity consumption fluctuation feature set of the target building at the hourly level, the energy consumption forecast result of the target building at the hourly level can be calculated.

[0039] Embodiment 2: A building energy consumption prediction system based on multi-factor decomposition, the system is used to implement the building energy consumption prediction method based on multi-factor decomposition as described in Embodiment 1, such as Figure 2 As shown, it includes a data acquisition module, a data weighting module, a data splitting module, an environment analysis module, an activity analysis module, a fluctuation analysis module and an energy consumption prediction module.

[0040] Among them, the data acquisition module is used to collect the hourly electricity consumption data and daily electricity consumption data of each target building in the target area; the data weighting module is used to grid the target area, and weight the corresponding total daily electricity consumption data according to the total building area in each grid unit, so as to obtain the initial grid electricity consumption data of the target area at the daily level; the data splitting module is used to obtain the energy consumption activity information of the target area at the daily level, and split the initial grid electricity consumption data into the first grid electricity consumption data with energy consumption activities and the second grid electricity consumption data without energy consumption activities; the environmental analysis module is used to extract the daily electricity consumption data affected by the environmental conditions in the target area from the second grid electricity consumption data, and obtain the basic electricity consumption data and electricity consumption variable coefficient function of the target area based on the fitting analysis of multiple daily electricity consumption data in a preset period. an activity analysis module, for calculating the activity electricity consumption data affected by the energy consumption activity by the difference between the first grid electricity consumption data and the basic electricity consumption data, and constructing the activity electricity consumption function according to the activity type and electricity consumption variable coefficient training of multiple activity electricity consumption data; a fluctuation analysis module, for calculating the estimated electricity consumption data according to the product of the basic electricity consumption data and the building area of ​​the target building, and calculating the daily fluctuation electricity consumption data of the corresponding target building by the difference between the daily electricity consumption data and the estimated electricity consumption data, and determining the deterministic electricity consumption fluctuation feature set of the target building at the hourly level by combining the daily fluctuation electricity consumption data of the target building and multiple daily hourly electricity consumption data; an energy consumption prediction module, for realizing the energy consumption prediction of the target building at the hourly level by combining the basic electricity consumption data, the electricity consumption variable coefficient function, the activity electricity consumption function and the deterministic electricity consumption fluctuation feature.

[0041] The present invention also records a computer terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the building energy consumption prediction method based on multi-factor decomposition as described in Example 1 is implemented.

[0042] The present invention also records a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement the building energy consumption prediction method based on multi-factor decomposition as described in Example 1.

[0043] Working principle: The present invention divides the multiple factors affecting building energy consumption into environmental conditions considered from a global perspective, energy consumption activities considered from a local perspective, and behavioral characteristics considered from a detailed perspective. When the overall trend of building energy consumption being affected by environmental conditions is characterized by basic electricity consumption data and electricity consumption variable coefficient functions, the correlation between energy consumption activities, behavioral characteristics and environmental conditions is simultaneously excavated, and accurate prediction of building energy consumption can be achieved through limited sample data; in addition, when the present invention characterizes the overall trend of building energy consumption being affected by environmental conditions by basic electricity consumption data and electricity consumption variable coefficient functions, when the basic electricity consumption data remains unchanged within a certain period, multiple influencing factors are integrated into a single factor for fitting prediction, thereby simplifying the difficulty of overall trend prediction; in addition, when analyzing the deterministic electricity consumption fluctuation feature set, the present invention can adaptively update the deterministic electricity consumption fluctuation features according to the changes in the basic electricity consumption data, and can effectively improve the prediction accuracy of building energy consumption at the hourly level during changes in environmental condition trends or oscillations.

[0044] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0045] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0046] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0047] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0048] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The building energy consumption prediction method based on multi-factor decomposition is characterized by: The following steps are involved: Collect hourly and daily electricity consumption data of each target building in the target area; Grid the target area, and divide the corresponding total daily electricity consumption data according to the total building area in each grid unit to obtain the initial grid electricity consumption data of the target area at the daily level; Obtaining energy consumption activity information of the target area at the daily level, and splitting the initial grid power consumption data into first grid power consumption data with energy consumption activities and second grid power consumption data without energy consumption activities; Extract daily electricity consumption data affected by environmental conditions in the target area from the second grid electricity consumption data, and obtain basic electricity consumption data and electricity consumption variable coefficient function of the target area based on fitting analysis of multiple daily electricity consumption data in a preset period; The activity electricity consumption data affected by the energy consumption activity is calculated by the difference between the first grid electricity consumption data and the basic electricity consumption data, and the activity electricity consumption function is constructed based on the activity types and electricity consumption variable coefficients of the multiple activity electricity consumption data; The estimated electricity consumption data is calculated based on the product of the basic electricity consumption data and the building area of ​​the target building, and the daily fluctuation electricity consumption data of the corresponding target building is calculated based on the difference between the daily electricity consumption data and the estimated electricity consumption data. In addition, the deterministic electricity consumption fluctuation feature set of the target building at the hourly level is determined by combining the daily fluctuation electricity consumption data of the target building and the hourly electricity consumption data of multiple days; The energy consumption of the target building is predicted at the hourly level by combining basic electricity consumption data, electricity consumption variable coefficient function, activity electricity consumption function and deterministic electricity consumption fluctuation characteristics.

2. The building energy consumption prediction method based on multi-factor decomposition according to claim 1 is characterized in that: The process of performing weighted processing on the corresponding total daily electricity consumption data according to the total building area in each grid unit is specifically as follows: Divide the standard building area by the total building area in each grid unit to obtain the weighting coefficient of each grid unit; The total daily electricity consumption data of each grid unit is multiplied by the weighted coefficient to obtain the weighted electricity consumption data of each grid unit; The initial grid electricity consumption data of the target area at the daily level is constructed based on the combination of the net power consumption data of each grid unit.

3. The building energy consumption prediction method based on multi-factor decomposition according to claim 1 is characterized in that: The calculation formula of the daily electricity consumption data is specifically as follows: ； or, as: ； in, Indicates that the target area is Daily electricity consumption data affected by environmental conditions; Indicates The grid cell in Daily ex-rights electricity consumption data; Indicates the number of grid cells in the second grid power consumption data.

4. The building energy consumption prediction method based on multi-factor decomposition according to claim 1 is characterized in that: The basic electricity consumption data and electricity consumption variable coefficient function of the target area are specifically expressed as follows: ； in, Indicates that the target area is Daily electricity consumption data affected by environmental conditions; Indicates the standard building area; Indicates the basic electricity consumption data of the target area; Indicates The daily electricity consumption coefficient; Represents the electricity consumption variable coefficient function.

5. The building energy consumption prediction method based on multi-factor decomposition according to claim 1 is characterized in that: The expression of the activity power consumption function is specifically: ； in, Indicates Type of activity per day The corresponding activity electricity consumption data; represents the activity power consumption function; Indicates The daily electricity consumption coefficient.

6. The building energy consumption prediction method based on multi-factor decomposition according to claim 1 is characterized in that: The calculation formula of the deterministic power consumption fluctuation feature set is specifically: ； in, Indicates the target building In the Daily electricity consumption data for each day; Indicates the basic electricity consumption data of the target area; Indicates the target building of building area; Indicates the target building In the Daily fluctuation electricity consumption data of each day; Indicates the target building In the Random daily electricity consumption fluctuation characteristics; Indicates the target building In the Daily The power consumption fluctuation characteristics of the target building in different days are different for the same hour. The corresponding deterministic power consumption fluctuation characteristics are taken as 0. Indicates the quantity of daily electricity consumption data; Indicates the target building In the Deterministic electricity consumption fluctuation characteristics of 1 hour; Indicates the target building In the Daily The fluctuation characteristics of electricity consumption in one hour; Indicates the target building A deterministic electricity consumption fluctuation feature set at the hourly level; Indicates the target building Deterministic electricity consumption fluctuation characteristics in the first hour; Indicates the target building Deterministic electricity consumption fluctuation characteristics at the 24th hour.

7. The building energy consumption prediction method based on multi-factor decomposition according to claim 1 is characterized in that: The process of realizing the hourly energy consumption prediction of the target building by combining the basic electricity consumption data, the electricity consumption variable coefficient function, the activity electricity consumption function and the deterministic electricity consumption fluctuation characteristics is specifically as follows: The basic electricity consumption data and the electricity consumption variable coefficient function are used to predict the basic electricity consumption data of the target area in the next day; Estimate the activity electricity consumption data of the target area in the next day through the activity reporting information and activity electricity consumption function; Combined with the target area's basic electricity consumption data for the next day, active electricity consumption data, and the deterministic electricity consumption fluctuation feature set of each target building, the energy consumption forecast result of the target building at the hourly level is determined.

8. Building energy consumption prediction system based on multi-factor decomposition, characterized by: The system is used to implement the building energy consumption prediction method based on multi-factor decomposition as described in any one of claims 1 to 7, comprising: A data collection module is used to collect hourly and daily electricity consumption data of each target building in the target area; The data weighting module is used to perform grid processing on the target area and weight-reduced processing on the corresponding total daily electricity consumption data according to the total building area in each grid unit to obtain the initial grid electricity consumption data of the target area at the daily level; A data splitting module is used to obtain the energy consumption activity information of the target area at the daily level, and split the initial grid power consumption data into the first grid power consumption data with energy consumption activity and the second grid power consumption data without energy consumption activity; An environmental analysis module is used to extract daily electricity consumption data affected by environmental conditions in the target area from the second grid electricity consumption data, and obtain basic electricity consumption data and electricity consumption variable coefficient function of the target area based on fitting analysis of multiple daily electricity consumption data in a preset period; An activity analysis module, configured to calculate the activity electricity consumption data affected by the energy consumption activity by using the difference between the first grid electricity consumption data and the basic electricity consumption data, and to construct an activity electricity consumption function based on the activity types and electricity consumption variable coefficients of the plurality of activity electricity consumption data; The fluctuation analysis module is used to calculate the estimated electricity consumption data based on the product of the basic electricity consumption data and the building area of ​​the target building, and calculate the daily fluctuation electricity consumption data of the corresponding target building based on the difference between the daily electricity consumption data and the estimated electricity consumption data, and determine the deterministic electricity consumption fluctuation feature set of the target building at the hourly level by combining the daily fluctuation electricity consumption data of the target building and the hourly electricity consumption data of multiple days; The energy consumption prediction module is used to combine basic electricity consumption data, electricity consumption variable coefficient function, activity electricity consumption function and deterministic electricity consumption fluctuation characteristics to realize the energy consumption prediction of the target building at the hourly level.

9. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the building energy consumption prediction method based on multi-factor decomposition as described in any one of claims 1 to 7 is implemented.

10. A computer readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the building energy consumption prediction method based on multi-factor decomposition as described in any one of claims 1 to 7.

Images