CN110276480A - A kind of analyzing and predicting method and system for building energy consumption management - Google Patents
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Abstract
The present invention relates to technical field of energy management more particularly to a kind of analyzing and predicting methods and system for building energy consumption management, the difference is that, step includes: S1, obtains the effective measured data of each City Building by equipment monitoring system；S2, the acquisition that by data acquisition module the Expenditure Levels of building energy are carried out with data information；S3, high-performance calculation and massive store by the big data resource realization under cloud environment to multi-energy data information；S4, building energy historical data is analyzed, and energy consumption prediction is carried out according to energy consumption real time data, predict the energy consumption situation of next phase.The present invention can predict the Expenditure Levels of building energy, reduce energy waste, while optimizing the energy consumption in building operational process.
Description
Technical Field
The invention relates to the technical field of energy management, in particular to an analysis and prediction method and system for building energy consumption management.
Background
With the development of the building industry in China, the number of buildings in cities is increasing day by day, and the styles and styles of the buildings are diversified more and more. The building floor not only relieves the problems of population growth and insufficient supply of construction land, but also serves as an important index for measuring the development level of a city. The building is in a large amount of emergence, and the operation process is also accompanied with the consumption of various energy sources. At present, the waste of energy consumption resources of buildings is very severe, and the waste mainly comprises irrational building planning and inappropriate equipment installation.
In view of the above, to overcome the above technical defects, it is an urgent problem in the art to provide an analysis and prediction method and system for building energy consumption management.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an analysis and prediction method and system for building energy consumption management, which can predict the consumption condition of building energy, reduce energy waste and optimize energy consumption in the operation process of a building.
In order to solve the technical problems, the technical scheme of the invention is as follows: an analysis and prediction method for building energy consumption management is characterized by comprising the following steps:
s1, acquiring effective measured data of each urban building through the equipment monitoring system;
s2, acquiring data information of the consumption condition of the building energy through a data acquisition module;
s3, realizing high-performance calculation and large-capacity storage of energy data information through big data resources in a cloud environment;
and S4, analyzing the historical building energy data, predicting energy consumption according to the real-time energy consumption data, and predicting the energy consumption condition of the next period.
According to the above technical solution, in step S4, the energy consumption prediction process is as follows:
A. selecting historical data of the energy consumption sample, performing data preprocessing preparation on the historical data, and constructing a training sample set and a test sample set;
B. selecting proper Support Vector Regression (SVR) types, kernel functions and related parameters;
C. establishing a target function by using a training sample, and searching an optimal classification surface;
D. and constructing a prediction model according to the obtained parameters, and predicting the predicted value at the future time by using the test sample.
According to the technical scheme, in the step D, the construction steps of the prediction model are as follows:
d1, determination of energy consumption data input and output: selecting a training sample set and a test sample set by using a Support Vector Machine (SVM) model, selecting original data stored in a database, and constructing input and output of a Support Vector Regression (SVR);
d2, preprocessing energy data: carrying out normalization processing on the original data;
d3, selection of Support Vector Regression (SVR) model parameters: selecting proper parameter values according to requirements to train sample data;
d4, determination of Support Vector Regression (SVR) prediction model function: and obtaining a final f (x) function expression.
According to the technical scheme, in the step D1, the data of the sample is derived from the data acquired by the monitoring platform in real time, including data of date, building height, area and electric energy consumption.
According to the technical scheme, in the step D4, various building energy consumption influence factors x are assumed1,x2,…,xnAccording to the basic idea of support vector machine model prediction, the expression form of mapping to a high-dimensional feature space is as follows:the regression function expression of the SVR is
Wherein,representing parameters to be identified in the model by w and b as a nonlinear mapping function;
based on the principle of minimizing the structural risk, the parameters to be identified in the formula (1) are processed according to the following formula:
wherein R (w) is empirical risk, | | w | | | messaging2For confidence risk, C (e)i) Is a loss function;
according to the SVR principle, the solution of the above equation (2) is essentially the optimization of equation (3):
wherein C in the formula is a penalty parameter, ξi，ξi *Is a relaxation variable, and epsilon is a regression function precision parameter;
according to the Mercer condition, a Gaussian radial basis kernel function is selected as a kernel function of the model, and the kernel function is as follows:
for the solution, equation (3) is usually converted into a dual problem, and a support vector regression function can be obtained, that is:
then, the above equation (4) is directly substituted into equation (5), and equivalent transformation is performed to obtain:
in practice, solving the regression function f (x) of the above formula is due to solving aiAnd ai *。
According to the technical scheme, the solution of the regression function f (x) is processed in a normalization mode, so that the convergence rate of the algorithm can be increased, and the prediction accuracy is improved, as follows:
wherein x isi(i-0, 1, …, n) is index data of each item, xmax，xminRespectively representing the maximum value and the minimum value in each index data.
According to the technical scheme, in the step A, the energy consumption prediction conforms to a time sequence rule, and the historical energy consumption data arranged according to time is assumed to be { x }t-n-m+1,xt-n-m+2,…,xtAnd then the constructed sample set can be expressed as:
wherein, X is the input of sample data, Y is the output matrix, m and n respectively represent the input order and the number of training samples.
An analytical prediction system for building energy consumption management, which is different in that it comprises:
the data source is used for acquiring effective measured data of each urban building;
the data acquisition module is used for acquiring the energy supply information and the operation information of the building energy;
the cloud storage module is used for carrying out high-performance calculation and large-capacity storage on the building energy information by fully utilizing large data resources concentrated by the cloud server;
and the energy analysis module is used for determining the energy consumption condition of the building through statistics of the building energy consumption data.
According to the technical scheme, the cloud storage module adopts big data resources in a cloud environment to realize high-performance calculation and large-capacity storage of energy data information.
According to the technical scheme, the energy analysis module processes historical building energy data and predicts the energy consumption condition of the next period according to the energy consumption real-time data.
Compared with the prior art, the invention has the following beneficial characteristics:
on one hand, the cloud storage module is adopted to process the building energy information, so that the problems of large data volume, complex structure, long time consumption and the like in operation are solved, high-performance computing and large-capacity storage under a cloud computing system architecture are realized, and good interaction is provided between a user and a system; on the other hand, in the energy analysis module, historical building energy data are analyzed through a computer data analysis technology, and the energy consumption condition of the next period is predicted according to the energy consumption real-time data. The building energy consumption management scheme provided by the invention can predict the consumption condition of building energy, reduce energy waste and optimize energy consumption in the building operation process.
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FIG. 1 is a schematic flow chart of an embodiment of the present invention;
fig. 2 is a schematic diagram of an energy consumption prediction process according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Many aspects of the invention are better understood with reference to the following drawings. The components in the drawings are not necessarily to scale. Instead, emphasis is placed upon clearly illustrating the components of the present invention. Moreover, in the several views of the drawings, like reference numerals designate corresponding parts.
The word "exemplary" or "illustrative" as used herein means serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" or "illustrative" is not necessarily to be construed as preferred or advantageous over other embodiments. All of the embodiments described below are exemplary embodiments provided to enable persons skilled in the art to make and use the examples of the disclosure and are not intended to limit the scope of the disclosure, which is defined by the claims. In other instances, well-known features and methods are described in detail so as not to obscure the invention. For purposes of the description herein, the terms "upper," "lower," "left," "right," "front," "rear," "vertical," "horizontal," and derivatives thereof shall relate to the invention as oriented in fig. 1. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification are simply exemplary embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.
Referring to fig. 1 and 2, an analysis and prediction method for building energy consumption management according to an embodiment of the present invention is different in that the method includes the following steps:
s1, acquiring effective measured data of each urban building through the equipment monitoring system;
s2, acquiring data information of the consumption condition of the building energy through a data acquisition module;
s3, realizing high-performance calculation and large-capacity storage of energy data information through big data resources in a cloud environment;
and S4, analyzing the historical building energy data, predicting energy consumption according to the real-time energy consumption data, and predicting the energy consumption condition of the next period.
Specifically, in step S4, the flow of energy consumption prediction is as follows:
A. selecting historical data of the energy consumption sample, performing data preprocessing preparation on the historical data, and constructing a training sample set and a test sample set;
B. selecting proper Support Vector Regression (SVR) types, kernel functions and related parameters;
C. establishing a target function by using a training sample, and searching an optimal classification surface;
D. and constructing a prediction model according to the obtained parameters, and predicting the predicted value at the future time by using the test sample.
Specifically, in the step D, the construction step of the prediction model is:
d1, determination of energy consumption data input and output: selecting a training sample set and a test sample set by using a Support Vector Machine (SVM) model, selecting original data stored in a database, and constructing input and output of a Support Vector Regression (SVR);
d2, preprocessing energy data: the method comprises the following steps of (1) carrying out normalization processing on original data before SVM prediction due to the fact that standards of indexes of the original data are different and the original data have large difference in numerical value;
d3, selection of Support Vector Regression (SVR) model parameters: selecting proper parameter values according to requirements to train sample data;
d4, determination of Support Vector Regression (SVR) prediction model function: and obtaining a final f (x) function expression.
Once the energy input of the building over time and the height and area of the building are determined, the model can be used to predict the energy consumption of the building over time (e.g., days, months, years) at the height and area corresponding to the energy input.
Specifically, in step D1, the data of the sample is obtained from the data acquired by the monitoring platform in real time, including date, building height, area, and power consumption data.
Specifically, in the step D4, various building energy consumption influencing factors x are assumed1,x2,…,xnAccording to the basic idea of support vector machine model prediction, the expression form of mapping to a high-dimensional feature space is as follows:
the regression function expression of the SVR is
Wherein,representing parameters to be identified in the model by w and b as a nonlinear mapping function;
based on the principle of minimizing the structural risk, the parameters to be identified in the formula (1) are processed according to the following formula:
wherein R (w) is empirical risk, | | w|2For confidence risk, C (e)i) Is a loss function;
according to the SVR principle, the solution of the above equation (2) is essentially the optimization of equation (3):
wherein C in the formula is a penalty parameter, ξi，ξi *Is a relaxation variable, and epsilon is a regression function precision parameter;
according to the Mercer condition, a Gaussian radial basis kernel function is selected as a kernel function of the model, and the kernel function is as follows:
for the solution, equation (3) is usually converted into a dual problem, and a support vector regression function can be obtained, that is:
then, the above equation (4) is directly substituted into equation (5), and equivalent transformation is performed to obtain:
in practice, solving the regression function f (x) of the above formula is due to solving aiAnd ai *。
Preferably, the solution of the regression function f (x) is processed in a normalization manner to accelerate the convergence rate of the algorithm and improve the accuracy of the prediction, as follows:
wherein x isi(i-0, 1, …, n) is index data of each item, xmax，xminRespectively representing the maximum value and the minimum value in each index data.
Specifically, in the step a, the energy consumption prediction conforms to a time series rule, and the historical energy consumption data arranged according to time is assumed to be { x }t-n-m+1,xt-n-m+2,…,xtAnd then the constructed sample set can be expressed as:
wherein, X is the input of sample data, Y is the output matrix, m and n respectively represent the input order and the number of training samples.
The prediction of building energy consumption is usually influenced by many factors, such as the temperature (the consumption of electricity is relatively high in summer and winter), the height and the occupied area of a building, and the like; in addition, the prediction of the building energy consumption data conforms to the time series rule, so the method can be adopted to process historical data.
An analytical prediction system for building energy consumption management, which is different in that it comprises:
the data source is used for acquiring effective measured data of each urban building;
the data acquisition module is used for acquiring the energy supply information and the operation information of the building energy;
the cloud storage module is used for carrying out high-performance calculation and large-capacity storage on the building energy information by fully utilizing large data resources concentrated by the cloud server;
and the energy analysis module is used for determining the energy consumption condition of the building through statistics of the building energy consumption data.
Specifically, the cloud storage module adopts big data resources in a cloud environment to realize high-performance computing and large-capacity storage of energy data information.
Specifically, the energy analysis module processes historical building energy data and predicts the energy consumption condition of the next period according to the energy consumption real-time data.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. An analysis and prediction method for building energy consumption management is characterized by comprising the following steps:
s1, acquiring effective measured data of each urban building through the equipment monitoring system;
s2, acquiring data information of the consumption condition of the building energy through a data acquisition module;
s3, realizing high-performance calculation and large-capacity storage of energy data information through big data resources in a cloud environment;
and S4, analyzing the historical building energy data, predicting energy consumption according to the real-time energy consumption data, and predicting the energy consumption condition of the next period.
2. The analytical prediction method for building energy consumption management as set forth in claim 1, characterized in that: in step S4, the energy consumption prediction process is as follows:
A. selecting historical data of the energy consumption sample, performing data preprocessing preparation on the historical data, and constructing a training sample set and a test sample set;
B. selecting proper Support Vector Regression (SVR) types, kernel functions and related parameters;
C. establishing a target function by using a training sample, and searching an optimal classification surface;
D. and constructing a prediction model according to the obtained parameters, and predicting the predicted value at the future time by using the test sample.
3. The analytical prediction method for building energy consumption management as set forth in claim 2, characterized in that: in the step D, the construction steps of the prediction model are as follows:
d1, determination of energy consumption data input and output: selecting a training sample set and a test sample set by using a Support Vector Machine (SVM) model, selecting original data stored in a database, and constructing input and output of a Support Vector Regression (SVR);
d2, preprocessing energy data: carrying out normalization processing on the original data;
d3, selection of Support Vector Regression (SVR) model parameters: selecting proper parameter values according to requirements to train sample data;
d4, determination of Support Vector Regression (SVR) prediction model function: and obtaining a final f (x) function expression.
4. The analytical prediction method for building energy consumption management as set forth in claim 3, characterized in that: in step D1, the data of the sample is derived from the data acquired by the monitoring platform in real time, including date, building height, area, and power consumption data.
5. The analytical prediction method for building energy consumption management as set forth in claim 3, characterized in that: in the step D4, various building energy consumption influencing factors x are assumed1,x2,…,xnAccording to the basic idea of support vector machine model prediction, the expression form of mapping to a high-dimensional feature space is as follows:the regression function expression of the SVR is
Wherein,representing parameters to be identified in the model by w and b as a nonlinear mapping function;
based on the principle of minimizing the structural risk, the parameters to be identified in the formula (1) are processed according to the following formula:
wherein R (w) is empirical risk, | | w | | | messaging2For confidence risk, C (e)i) Is a loss function;
according to the SVR principle, the solution of the above equation (2) is essentially the optimization of equation (3):
wherein C in the formula is a penalty parameter, ξi，ξi *Is a relaxation variable, and epsilon is a regression function precision parameter;
according to the Mercer condition, a Gaussian radial basis kernel function is selected as a kernel function of the model, and the kernel function is as follows:
for the solution, equation (3) is usually converted into a dual problem, and a support vector regression function can be obtained, that is:
then, the above equation (4) is directly substituted into equation (5), and equivalent transformation is performed to obtain:
in practice, solving the regression function f (x) of the above formula is due to solving aiAnd ai *。
6. The analytical prediction method for building energy consumption management as set forth in claim 5, characterized in that: the solution of the regression function f (x) is processed in a normalization mode to accelerate the convergence rate of the algorithm and improve the accuracy of prediction, and the method comprises the following steps:
wherein x isi(i-0, 1, …, n) is index data of each item, xmax，xminRespectively representing the maximum value and the minimum value in each index data.
7. According to claim 2The analysis and prediction method for building energy consumption management is characterized by comprising the following steps: in the step A, the energy consumption prediction conforms to a time series rule, and the historical energy consumption data arranged according to time is assumed to be { x }t-n-m+1,xt-n-m+2,…,xtAnd then the constructed sample set can be expressed as:
wherein, X is the input of sample data, Y is the output matrix, m and n respectively represent the input order and the number of training samples.
8. An analytical prediction system for building energy consumption management, comprising:
the data source is used for acquiring effective measured data of each urban building;
the data acquisition module is used for acquiring the energy supply information and the operation information of the building energy;
the cloud storage module is used for carrying out high-performance calculation and large-capacity storage on the building energy information by fully utilizing large data resources concentrated by the cloud server;
and the energy analysis module is used for determining the energy consumption condition of the building through statistics of the building energy consumption data.
9. The analytical prediction system for building energy consumption management of claim 8, wherein: the cloud storage module adopts big data resources in a cloud environment to realize high-performance computing and large-capacity storage of energy data information.
10. The analytical prediction system for building energy consumption management of claim 8, wherein: the energy analysis module processes historical building energy data and predicts the energy consumption condition of the next period according to the energy consumption real-time data.
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| CN114518723A (en) * | 2022-01-04 | 2022-05-20 | 山东正晨科技股份有限公司 | Energy consumption monitoring system and method for intelligent building data |
| CN114518723B (en) * | 2022-01-04 | 2024-04-16 | 山东正晨科技股份有限公司 | Energy consumption monitoring system and method for intelligent building data |
| CN117043794A (en) * | 2022-07-01 | 2023-11-10 | 嘉兴尚坤科技有限公司 | Building energy consumption prediction method and system based on multiple linear regression and cluster analysis |
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