CN113469419A - Commercial building power consumption prediction method and system - Google Patents
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Abstract
The invention relates to a method and a system for predicting commercial building power consumption, wherein the method comprises the following steps: predicting the electricity consumption of the commercial building by utilizing a second regression prediction model; wherein, the obtaining process of the second regression prediction model comprises the following steps: collecting historical electricity utilization data of commercial building electrical appliances, and preprocessing the historical electricity utilization data; and according to the historical power utilization data, a first regression prediction model is established through regression analysis, and insignificant independent variables in the first regression prediction model are removed through a removing step to obtain a second regression prediction model. Compared with the prior art, the method has the advantages of less calculated amount, simple operation and high accuracy, and can visually observe the influence of each variable on the predicted value of the power consumption.
Description
Technical Field
The invention relates to a power consumption prediction technology, in particular to a method and a system for predicting the power consumption of commercial buildings.
Background
At present, city business circles develop towards integration, and often comprise multiple functions such as retail sale, office, hotel, catering, entertainment and the like, and all parts have interdependence and mutual benefit active relations, so that a multifunctional, high-efficiency, complex and uniform integrated body is gradually formed. Large commercial buildings are a primary representative of commercial development,
the method is very important for predicting the electricity consumption of the commercial buildings and researching the influence of each influence factor on the electricity consumption, the electricity consumption prediction can provide basis for electricity price rules, electricity infrastructure construction and electricity utilization scheduling of the commercial buildings, the research on the influence of each influence factor on the electricity consumption can provide basis for electricity monitoring and scheduling and city planning, and the method can provide guidance for behaviors of people such as business district selection, trip planning, traffic, consumption and the like and eliminate various potential safety hazards.
At present, the deep learning technology is generally adopted to predict the electric quantity, the deep learning technology is rapidly advanced in recent years, the deep learning technology carries out hierarchical nonlinear representation on input, the deep network has strong representation capability, the multi-task deep learning has a space for deployment, however, in recent years, the electric power data volume shows explosive growth, the electronic equipment is increased rapidly, the electricity consumption is greatly improved due to the increase of the electricity demand of people, the electricity consumption of different commercial circles is greatly different due to different market structures, geographical positions, main and business services, landscape conditions and people flow density, the deep learning technology is common in performance when processing massive data such as the electricity consumption, a large number of training samples are needed to achieve the expected prediction precision, and the time consumption is long, meanwhile, the influence degree of each influence factor on the electricity consumption cannot be visually observed, and inconvenience is brought to analysis of the influence factors on the electricity consumption.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method and a system for predicting the electricity consumption of commercial buildings, which have the advantages of less calculated amount, simple operation and high accuracy and can visually observe the influence of respective variables on the predicted value of the electricity consumption.
The purpose of the invention can be realized by the following technical scheme:
a method for predicting electricity consumption of commercial buildings comprises the following steps:
predicting the electricity consumption of the commercial building by utilizing a second regression prediction model;
wherein, the obtaining process of the second regression prediction model comprises the following steps:
collecting historical electricity utilization data of commercial building electrical appliances, and preprocessing the historical electricity utilization data;
according to historical power utilization data, a first regression prediction model is established through regression analysis, and unremarkable independent variables in the first regression prediction model are removed through a removing step to obtain a second regression prediction model;
the calculation formula of the first regression prediction model is as follows:
wherein y is the predicted value of the total electricity consumption of the commercial building, beta0Is a constant term, epsilon is a random error, betaiIs the ith independent variable influence coefficient, xiThe decentralized independent variable data;
the calculation formula of the second regression prediction model is as follows:
wherein, x 'and beta' are the removed independent variable data and the corresponding independent variable influence coefficient.
Further, the pretreatment process comprises the following steps:
and deleting data points which deviate from the average value by more than three times of the standard deviation for the historical electricity consumption data of each type of electrical appliance, and correcting the initial missing data points and the deleted data points through linear interpolation.
Further, the independent variables include temperature, wind direction, wind speed, air pressure, humidity, day of rest, weekend and hours.
Further, the process of the independent variable data decentralization comprises the following steps:
and setting a reference value for each type of independent variable, and subtracting the corresponding reference value from the independent variable data to obtain centralized independent variable data.
Further, the removing step comprises:
and calculating the P value of each independent variable, judging whether the P value of the independent variable is less than 0.05, if so, judging that the independent variable is obvious, otherwise, judging that the independent variable is not obvious, and rejecting the independent variable.
The P value is the probability of the observed sample and more extreme cases occurring if the original hypothesis is true, representing the minimum level of significance to reject the original hypothesis, and is a method for determining whether the original hypothesis should be rejected, typically with a statistical difference of P < 0.05.
A commercial building power usage prediction system comprising:
the data acquisition module is used for acquiring historical electricity utilization data of commercial building electrical appliances and carrying out preprocessing;
the data processing module is used for establishing a first regression prediction model through regression analysis according to historical power utilization data;
the model correction module is used for eliminating the unremarkable independent variables in the first regression prediction model through the eliminating step to obtain a second regression prediction model;
the electric quantity prediction module is used for predicting the electricity consumption of the commercial building by utilizing the second regression prediction model;
the calculation formula of the first regression prediction model is as follows:
wherein y is the predicted value of the total electricity consumption of the commercial building, beta0Is a constant term, epsilon is a random error, betaiIs the ith independent variable influence coefficient, xiThe decentralized independent variable data;
the calculation formula of the second regression prediction model is as follows:
wherein, x 'and beta' are the removed independent variable data and the corresponding independent variable influence coefficient.
Further, the pretreatment process comprises the following steps:
for the historical electricity consumption data of each type of electric appliance, the data acquisition module deletes data points which deviate from the average value by more than three times of the standard deviation, and corrects the initial missing data points and the deleted data points through linear interpolation.
Further, the independent variables include temperature, wind direction, wind speed, air pressure, humidity, day of rest, weekend and hours.
Further, the process of the independent variable data decentralization comprises the following steps:
the data processing module sets a reference value for each type of independent variable, and subtracts the corresponding reference value from the independent variable data to obtain centralized independent variable data.
Further, the removing step comprises:
and the model correction module calculates the P value of each independent variable, judges whether the P value of the independent variable is less than 0.05 or not, judges that the independent variable is obvious if the P value of the independent variable is less than 0.05, and judges that the independent variable is not obvious if the P value of the independent variable is not less than 0.05 and rejects the independent variable.
The P value is the probability of the observed sample and more extreme cases occurring if the original hypothesis is true, representing the minimum level of significance to reject the original hypothesis, and is a method for determining whether the original hypothesis should be rejected, typically with a statistical difference of P < 0.05.
Compared with the prior art, the invention has the following beneficial effects:
(1) the invention preprocesses the historical electricity consumption data of each type of electrical appliance, eliminates abnormal data and completes the missing part. Secondly, performing center-like processing on data such as climate, buildings, population mobility, time and the like, establishing a first regression prediction model on the basis, and removing insignificant independent variables in the first regression prediction model to obtain a second regression prediction model, calculating the predicted value of the electricity consumption of the commercial building by using the second regression prediction model, and having less calculation amount, simple operation and high accuracy, meanwhile, the influence of each variable on the predicted value of the power consumption can be visually observed, the power consumption curves of the power consumption, the elimination time and the date factors of the climate factors are respectively analyzed and eliminated, and further quantitative evidence is provided for the mode and the influence of the external environment on the power consumption, therefore, better demand understanding and more timely power demand scheduling and allocation are realized, a foundation is laid for more accurately analyzing the relation between power consumption and economic activities and production conditions, and a new cut-in is provided for exploring the influence of various factors and the like on the power consumption behaviors of commercial buildings;
(2) the second regression prediction model brings temperature, humidity, wind speed, wind direction, air pressure, rest days, weekends and hours into the discussion range, and based on the environment and time variables, the second regression prediction model calculates and analyzes the change of the building power consumption caused by the change of economic activities and production conditions, so that the reliability is high;
(3) for the historical electricity consumption data of each type of electrical appliance, the data acquisition module deletes data points deviating from the average value by more than three times of the standard deviation, and corrects the initial missing data points and the deleted data points through linear interpolation, so that the accuracy of prediction is improved;
(4) the method calculates the P value of each independent variable, judges whether the P value of the independent variable is less than 0.05 or not, judges that the independent variable is obvious if the P value of the independent variable is less than 0.05, and judges that the independent variable is not obvious if the P value of the independent variable is not less than 0.05 or eliminates the independent variable, thereby improving the accuracy of prediction.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1
A method for predicting electricity consumption of commercial buildings, as shown in fig. 1, comprising:
1) collecting historical electricity utilization data of commercial building electrical appliances, and preprocessing the historical electricity utilization data;
2) according to historical power utilization data, a first regression prediction model is established through regression analysis, and unremarkable independent variables in the first regression prediction model are removed through a removing step to obtain a second regression prediction model;
3) predicting the electricity consumption of the commercial building by utilizing a second regression prediction model;
the calculation formula of the first regression prediction model is as follows:
wherein y is the predicted value of the total electricity consumption of the commercial building, beta0Is a constant term, epsilon is a random error, betaiIs the ith independent variable influence coefficient, xiThe decentralized independent variable data;
the calculation formula of the second regression prediction model is as follows:
wherein, x 'and beta' are the removed independent variable data and the corresponding independent variable influence coefficient.
In big data analysis, regression analysis is a predictive modeling technique that studies the relationship between dependent and independent variables. This technique is commonly used in predictive analysis, time series modeling, and to discover causal relationships between variables, and is referred to as multiple linear regression analysis if two or more independent variables are included in the regression analysis and there is a linear correlation between the independent variables. The present embodiment employs multiple linear regression analysis to extract power usage changes under the influence of economic activity and production conditions.
The pretreatment process comprises the following steps:
and deleting data points which deviate from the average value by more than three times of the standard deviation for the historical electricity consumption data of each type of electrical appliance, and correcting the initial missing data points and the deleted data points through linear interpolation.
The independent variables comprise temperature, wind direction, wind speed, air pressure, humidity, resting day, weekend and hours, and the independent variable data decentralization process comprises the following steps:
setting a reference value for each type of independent variable, and subtracting the corresponding reference value from the independent variable data to obtain centralized independent variable data;
the air temperature is 20 ℃ as a standard, the wind direction is 140 ℃ as a standard, the wind speed is 2km/h as a standard, the air pressure is standard atmospheric pressure, and the humidity is 75% as a standard.
The removing step comprises the following steps:
and calculating the P value of each independent variable, judging whether the P value of the independent variable is less than 0.05, if so, judging that the independent variable is obvious, otherwise, judging that the independent variable is not obvious, and rejecting the independent variable.
The P value is the probability that the observed sample and more extreme cases will occur if the original hypothesis is true, representing the minimum level of significance for rejecting the original hypothesis, and one method for determining whether the original hypothesis should be rejected is to have a statistical difference, typically P < 0.05.
Example 2
A commercial building power usage prediction system comprising:
the data acquisition module is used for acquiring historical electricity utilization data of commercial building electrical appliances and carrying out preprocessing;
the data processing module is used for establishing a first regression prediction model through regression analysis according to historical power utilization data;
the model correction module is used for eliminating the unremarkable independent variables in the first regression prediction model through the eliminating step to obtain a second regression prediction model;
the electric quantity prediction module is used for predicting the electricity consumption of the commercial building by utilizing the second regression prediction model;
the calculation formula of the first regression prediction model is as follows:
wherein y is the predicted value of the total electricity consumption of the commercial building, beta0Is a constant term, epsilon is a random error, betaiIs the ith independent variable influence coefficient, xiThe decentralized independent variable data;
the calculation formula of the second regression prediction model is as follows:
wherein, x 'and beta' are the removed independent variable data and the corresponding independent variable influence coefficient.
The pretreatment process comprises the following steps:
for the historical electricity consumption data of each type of electric appliance, the data acquisition module deletes data points which deviate from the average value by more than three times of the standard deviation, and corrects the initial missing data points and the deleted data points through linear interpolation.
The independent variable data decentralization process comprises the following steps:
the data processing module sets a reference value for each type of independent variable, and subtracts the corresponding reference value from the independent variable data to obtain centralized independent variable data;
the independent variables comprise temperature, wind direction, wind speed, air pressure, humidity, resting day, weekend and hours, wherein the air temperature is based on 20 ℃, the wind direction is based on 140 ℃, the wind speed is based on 2km/h, the air pressure is based on standard atmospheric pressure, and the humidity is based on 75%.
The removing step comprises the following steps:
and the model correction module calculates the P value of each independent variable, judges whether the P value of the independent variable is less than 0.05 or not, judges the independent variable to be obvious if the P value of the independent variable is less than 0.05, and judges the independent variable to be not obvious if the P value of the independent variable is not less than 0.05 and rejects the independent variable.
The embodiment 1 and the embodiment 2 provide a method and a system for predicting the electricity consumption of commercial buildings, wherein the method comprises the steps of preprocessing historical electricity consumption data of each type of electrical appliances, removing abnormal data and supplementing missing parts. Secondly, performing similar centralized processing on data such as climate, buildings, population movement, time and the like, establishing a first regression prediction model on the basis, removing insignificant independent variables in the first regression prediction model to obtain a second regression prediction model, and calculating a predicted value of the electricity consumption of the commercial buildings by using the second regression prediction model;
the second regression prediction model brings temperature, humidity, wind speed, wind direction, air pressure, rest days, weekends and hours into discussion range, building power consumption changes caused by changes of economic activities and production conditions are calculated and analyzed through the second regression prediction model based on the environment and time variables, the calculated amount is small, the operation difficulty is low, a foundation is laid for more accurately analyzing the relation between power consumption and the economic activities and the production conditions, a new cut-in is provided for exploring the influences of various climatic factors, time and date factors and the like on the power consumption behaviors of commercial buildings, power consumption curves of the climatic factors, the time and date factors are respectively analyzed and eliminated, quantitative evidence is further provided for the mode and the influence of the external environment on the power consumption, and accordingly better demand understanding and more timely power demand scheduling and allocation are achieved.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A method for predicting electricity consumption of commercial buildings is characterized by comprising the following steps:
predicting the electricity consumption of the commercial building by utilizing a second regression prediction model;
wherein, the obtaining process of the second regression prediction model comprises the following steps:
collecting historical electricity utilization data of commercial building electrical appliances, and preprocessing the historical electricity utilization data;
according to historical power utilization data, a first regression prediction model is established through regression analysis, and unremarkable independent variables in the first regression prediction model are removed through a removing step to obtain a second regression prediction model;
the calculation formula of the first regression prediction model is as follows:
wherein y is the predicted value of the total electricity consumption of the commercial building, beta0Is a constant term, epsilon is a random error, betaiIs the ith independent variable influence coefficient, xiThe decentralized independent variable data;
the calculation formula of the second regression prediction model is as follows:
wherein, x 'and beta' are the removed independent variable data and the corresponding independent variable influence coefficient.
2. A method as claimed in claim 1, wherein said preprocessing step comprises:
and deleting data points which deviate from the average value by more than three times of the standard deviation for the historical electricity consumption data of each type of electrical appliance, and correcting the initial missing data points and the deleted data points through linear interpolation.
3. A method as claimed in claim 1, wherein said independent variables include temperature, wind direction, wind speed, barometric pressure, humidity, day of rest, weekend and hours.
4. A method as claimed in claim 3, wherein said process of de-centralizing the independent variable data comprises:
and setting a reference value for each type of independent variable, and subtracting the corresponding reference value from the independent variable data to obtain centralized independent variable data.
5. The method as claimed in claim 1, wherein the step of eliminating comprises:
and calculating the P value of each independent variable, judging whether the P value of the independent variable is less than 0.05, if so, judging that the independent variable is obvious, otherwise, judging that the independent variable is not obvious, and rejecting the independent variable.
6. A system for predicting electricity usage for commercial buildings, comprising:
the data acquisition module is used for acquiring historical electricity utilization data of commercial building electrical appliances and carrying out preprocessing;
the data processing module is used for establishing a first regression prediction model through regression analysis according to historical power utilization data;
the model correction module is used for eliminating the unremarkable independent variables in the first regression prediction model through the eliminating step to obtain a second regression prediction model;
the electric quantity prediction module is used for predicting the electricity consumption of the commercial building by utilizing the second regression prediction model;
the calculation formula of the first regression prediction model is as follows:
wherein y is the predicted value of the total electricity consumption of the commercial building, beta0Is a constant term, epsilon is a random error, betaiIs the ith independent variable influence coefficient, xiThe decentralized independent variable data;
the calculation formula of the second regression prediction model is as follows:
wherein, x 'and beta' are the removed independent variable data and the corresponding independent variable influence coefficient.
7. A commercial building power consumption prediction system as claimed in claim 6 wherein said preprocessing includes:
for the historical electricity consumption data of each type of electric appliance, the data acquisition module deletes data points which deviate from the average value by more than three times of the standard deviation, and corrects the initial missing data points and the deleted data points through linear interpolation.
8. A commercial building power consumption prediction system as claimed in claim 6 in which the independent variables include temperature, wind direction, wind speed, barometric pressure, humidity, day of rest, weekend and hours.
9. A commercial building power usage prediction system as claimed in claim 8, wherein said process of de-centralizing the independent variable data includes:
the data processing module sets a reference value for each type of independent variable, and subtracts the corresponding reference value from the independent variable data to obtain centralized independent variable data.
10. A commercial building power consumption prediction system as claimed in claim 6 wherein said culling step comprises:
and the model correction module calculates the P value of each independent variable, judges whether the P value of the independent variable is less than 0.05 or not, judges that the independent variable is obvious if the P value of the independent variable is less than 0.05, and judges that the independent variable is not obvious if the P value of the independent variable is not less than 0.05 and rejects the independent variable.
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| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Su Yun Inventor after: Zhao Yingying Inventor after: Zhang Feifei Inventor after: Ruan Jingxian Inventor after: Jin Yanfei Inventor after: Shi Zhengyu Inventor after: Tian Yingjie Inventor after: Zhu Zheng Inventor after: Xie Wei Inventor after: Qu Haini Inventor after: Xi Zenghui Inventor after: Guo Naiwang Inventor after: Li Fan Inventor after: Wu Yi Inventor before: Su Yun Inventor before: Zhao Yingying Inventor before: Zhang Feifei Inventor before: Ruan Jingxian Inventor before: Jin Yanfei Inventor before: Shi Zhengyu Inventor before: Tian Yingjie Inventor before: Zhu Zheng Inventor before: Xie Wei Inventor before: Qu Haini Inventor before: Xi Zenghui Inventor before: Guo Naiwang Inventor before: Li Fan Inventor before: Wu Yi |