CN114970357A - Energy-saving effect evaluation method, system, device and storage medium - Google Patents
Energy-saving effect evaluation method, system, device and storage medium Download PDFInfo
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Abstract
The disclosure relates to the technical field of energy conservation and environmental protection, and provides an energy-saving effect evaluation method, system, device and storage medium. The method comprises the following steps: acquiring data of a first time period and a second time period, wherein the data comprises weather factor data, work factor data, personnel factor data and energy consumption data; establishing an energy consumption factor correlation model by using a preset regression model based on the acquired first time period data; inputting the influence factor data of the second time period into the energy consumption factor correlation model, and calculating to obtain an energy consumption reference value of the second time period; and calculating a difference value between the energy consumption reference value and the energy consumption actual value in the second time period, wherein the difference value is energy saving amount, and evaluating the energy saving effect based on the energy saving amount. The method and the device can comprehensively and accurately obtain the model parameters, improve the accuracy and efficiency of energy consumption evaluation, and have high reliability of the evaluation result.
Description
Technical Field
The present disclosure relates to the field of energy saving and environmental protection technologies, and in particular, to a method, a system, an apparatus, and a storage medium for evaluating energy saving effect.
Background
In the total energy consumption of a building, the energy consumption of a heating/cooling system occupies a great proportion, and various energy-saving measures need to be taken for the heating/cooling system due to pursuit of economy and environmental protection.
From the implementation of energy-saving measures, there can be a general classification into two. The first type is an energy-saving measure based on equipment modification, and mainly comprises modification, replacement, upgrading and the like of the existing equipment, for example, a fixed-frequency water pump is replaced by a variable-frequency water pump. The second type is an energy-saving measure based on an equipment control algorithm, and achieves the aim of saving energy by regulating and controlling the running state of equipment on the premise of not physically modifying the existing equipment, for example, dynamically regulating and controlling the set value of the water outlet temperature of a water chilling unit according to the weather condition. In practical operation, the energy-saving modification of the heating/cooling system may include the above two measures. How effective the energy-saving measure is the most important aspect for judging whether the measure is successful or not, so that the energy-saving effect needs to be evaluated quantifiably. The existing energy-saving effect evaluation methods can be roughly divided into three categories. The first type is energy-saving measurement and calculation based on equipment mechanism, the second type is energy-saving measurement and calculation based on numerical simulation, and the third type is evaluation through the actual energy consumption difference of the heating/refrigerating system before and after modification.
In the prior art, only the equipment mechanism of the modified equipment is considered, the evaluation condition is simple, and the factor of inconsistency of the environmental states of the heating/refrigerating system before and after modification is ignored.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide an energy saving effect evaluation method, system, apparatus, and computer program readable storage medium, so as to solve the problems in the prior art that the evaluation effect is poor, the energy saving effect cannot be considered and evaluated from the overall energy consumption change, the evaluation effect is not accurate enough, the consideration factors are not comprehensive, and the like.
In a first aspect of the embodiments of the present disclosure, a method for evaluating an energy saving effect is provided, including the following steps:
step 1: taking one or more time periods before energy-saving measures are taken as a first time period, taking one or more time periods after energy-saving measures are taken as a second time period, and acquiring data of the first time period and the second time period, wherein the data comprises influence factor data and energy consumption data;
step 2: establishing an energy consumption factor correlation model by using a preset regression model based on the acquired first time period data;
and step 3: inputting the influence factor data of the second time period into the energy consumption factor correlation model, and calculating to obtain an energy consumption reference value of the second time period;
and 4, step 4: and calculating a difference value between the energy consumption reference value and the energy consumption actual value in the second time period, wherein the difference value is energy saving amount, and evaluating the energy saving effect based on the energy saving amount.
In a second aspect of the disclosed embodiments, there is provided a system comprising: comprising a memory, a processor and a computer program stored in the memory and executable on the processor, which processor realizes the steps of the above-mentioned method when executing the computer program.
In a third aspect of the disclosed embodiments, there is provided an apparatus, comprising: comprising a memory, a processor and a computer program stored in the memory and executable on the processor, which processor realizes the steps of the above-mentioned method when executing the computer program.
In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, which stores a computer program, which when executed by a processor, implements the steps of the above-mentioned method.
Compared with the prior art, the embodiment of the disclosure has the following beneficial effects: the method comprises the steps of comprehensively obtaining influence factor data and energy consumption data of a reference period, establishing an energy consumption factor correlation model by adopting a regression model, comprehensively and accurately obtaining model parameters, improving the accuracy of energy consumption evaluation, inputting the influence factor data of the evaluation period into the energy consumption factor correlation model, calculating to obtain an energy consumption reference value of the evaluation period, calculating a difference value between the energy consumption reference value of the evaluation period and an energy consumption actual value on the basis, outputting the reference value in real time according to actual conditions of an objective environment, improving the efficiency, obtaining an evaluation result with high reliability, calculating the energy saving effect from the whole system level rather than the equipment level, and generating quantifiable evaluation.
Drawings
To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive efforts.
FIG. 1 is a scenario diagram of an application scenario of an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of an energy saving effect evaluation method provided by the embodiment of the disclosure;
fig. 3 is a schematic flowchart of an energy saving effect evaluation method for a heating/cooling system energy saving measure according to an embodiment of the present disclosure;
fig. 4 is a schematic flowchart of an input and output method of a neural network with two hidden layers and a single output layer according to an embodiment of the present disclosure;
FIG. 5 is a schematic block diagram of a system provided by embodiments of the present disclosure;
Detailed Description
Aiming at the first type of energy-saving effect evaluation method in the prior art, namely energy-saving effect measurement and calculation based on equipment mechanism, the method only considers the equipment mechanism of the modified equipment, firstly ignores the difference between the equipment mechanism theory and the actual operation condition, secondly ignores the heating/refrigerating system as a whole, and has certain coupling relevance among the equipment, the modification of the equipment can cause influence on the operation condition of other equipment, and the method is not suitable only for considering the energy consumption change of the modified equipment.
For the second type of energy-saving effect evaluation method in the prior art, namely, measurement and calculation based on numerical simulation, the numerical simulation cannot completely restore the actual operation condition of the heating/refrigerating system, so the numerical simulation usually simulates the operation of the heating/refrigerating system under a simplified condition, and the simulated energy-saving effect does not have good reliability and recognition.
Aiming at the third energy-saving effect evaluation method in the prior art, namely, the evaluation is carried out through the actual energy consumption difference before and after the modification, and the method ignores the inconsistency of the environmental states of the heating/refrigerating system before and after the modification. Taking a comprehensive business body as an example, if the energy consumption difference between one month after modification and one month before modification is taken as the evaluation of the energy saving effect, the consistency of the variables such as weather conditions, holiday numbers, cooling area and the like of two months as comparison cannot be ensured, so that the evaluation result is inaccurate. For example, in general, the heating/cooling energy consumption in 9 months is usually lower than that in 8 months, and when such variables are superimposed, the effect of energy saving measures becomes unmanageable.
The embodiments of the present invention can solve the related problems in the prior art, and refer to the following description specifically.
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.
An energy saving effect evaluation method and apparatus according to an embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a scene schematic diagram of an application scenario of an embodiment of the present disclosure. The application scenario may include terminal devices 1, 2, and 3, a server 4, a network 5, a heating system 6, a cooling system 7, and other energy saving systems 8.
The terminal devices 1, 2, and 3 may be hardware or software. When the terminal devices 1, 2 and 3 are hardware, they may be various electronic devices having a display screen and supporting communication with the server 4, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like; when the terminal devices 1, 2, and 3 are software, they may be installed in the electronic device as described above. The terminal devices 1, 2 and 3 may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not limited by the embodiments of the present disclosure. Further, the terminal devices 1, 2, and 3 may have various applications installed thereon, such as a data processing application, an instant messaging tool, social platform software, a search-type application, a shopping-type application, and the like.
The server 4 may be a server providing various services, for example, a backend server receiving a request sent by a terminal device establishing a communication connection with the server, and the backend server may receive and analyze the request sent by the terminal device and generate a processing result. The server 4 may be one server, may also be a server cluster composed of a plurality of servers, or may also be a cloud computing service center, which is not limited in this disclosure.
The server 4 may be hardware or software. When the server 4 is hardware, it may be various electronic devices that provide various services to the terminal devices 1, 2, and 3. When the server 4 is software, it may be a plurality of software or software modules providing various services for the terminal devices 1, 2, and 3, or may be a single software or software module providing various services for the terminal devices 1, 2, and 3, which is not limited by the embodiment of the present disclosure.
The network 5 may be a wired network connected by a coaxial cable, a twisted pair and an optical fiber, or may be a wireless network that can interconnect various Communication devices without wiring, for example, Bluetooth (Bluetooth), Near Field Communication (NFC), Infrared (Infrared), and the like, which is not limited in the embodiment of the present disclosure.
A user can establish a communication connection with the server 4 via the network 5 through the terminal devices 1, 2, and 3 to receive or transmit information or the like.
It should be noted that the specific types, numbers and combinations of the terminal devices 1, 2 and 3, the server 4 and the network 5 may be adjusted according to the actual requirements of the application scenarios, and the embodiment of the present disclosure does not limit this.
The heating system 6, the cooling system 7 and the other energy saving system 8 can be connected with any end of the terminal equipment 1, 2, 3, the server 4 and the network 5 so as to carry out communication and data transmission.
Fig. 2 is a schematic flow chart of an energy saving effect evaluation method provided by the embodiment of the disclosure. The energy saving effect evaluation method of fig. 2 may be performed by the terminal device or the server of fig. 1. As shown in fig. 2, the energy saving effect evaluation method includes:
s201, acquiring data of a reference period and an evaluation period, wherein the data comprises influence factor data and energy consumption data;
s202, establishing an energy consumption factor correlation model by utilizing a preset regression model based on the acquired datum period data;
s203, inputting the influence factor data of the evaluation period into the energy consumption factor correlation model, and calculating to obtain an energy consumption reference value of the evaluation period;
and S204, calculating a difference value between the energy consumption reference value and the energy consumption actual value in the evaluation period, wherein the difference value is energy saving amount, and evaluating the energy saving effect based on the energy saving amount.
One or more time periods before the energy-saving measures are taken as a reference period, and one or more time periods after the energy-saving measures are taken as an evaluation period.
The method steps are denoted by "SXXX", XXX is a three-digit consecutively numbered number, such as S201, S202, S203, and S204 of the method steps, it should be understood that the order of execution of the steps does not mean the order of execution, and the execution order of the processes should be determined by the functions and internal logics of the processes, and should not constitute any limitation to the implementation processes of the disclosed embodiments. The method steps in other embodiments are understood in the same manner as in this embodiment, and will not be described again.
Specifically, an energy consumption factor correlation model is obtained through a regression model, a mathematical model is adopted to establish correlation analysis between the influence factors and the energy consumption data, accurate energy consumption data can be obtained on the premise that the influence factor data are obtained, and a data base can be established for accurately evaluating the energy-saving effect.
According to the technical scheme provided by the embodiment of the disclosure, the accuracy of energy consumption evaluation is improved by comprehensively acquiring the influence factor data and the energy consumption data of the reference period, the reference value can be output in real time according to the actual conditions of the objective environment, the evaluation result with high reliability is obtained, the energy-saving efficiency is calculated from the whole system level rather than the equipment level, and the quantifiable evaluation is generated.
In some embodiments, in the energy saving effect evaluation method, the influencing factor data includes: weather factors, work factors, personnel factor data, etc.
Fig. 3 is a schematic flow chart of a method for evaluating an energy saving effect of a heating/cooling system according to an embodiment of the present disclosure. The heating/cooling system energy saving effect evaluation method of fig. 3 may be performed by the terminal device or the server of fig. 1. As shown in fig. 3, the method for evaluating the energy saving effect of the heating/cooling system includes:
s301, acquiring data of a reference period and an evaluation period of the heating/refrigerating system, wherein the acquired data of the influencing factors comprise: weather factors, work factors and personnel factor data, wherein the work factors comprise work area, holiday days and time period factors, and the personnel factors comprise personnel number and personnel traffic;
s302, establishing an energy consumption factor correlation model by utilizing a preset regression model based on the acquired datum period data;
s303, inputting the influence factor data of the evaluation period into the energy consumption factor correlation model, and calculating to obtain an energy consumption reference value of the evaluation period;
and S304, calculating a difference value between the energy consumption reference value and the energy consumption actual value in the evaluation period, wherein the difference value is energy saving amount, and evaluating the energy saving effect based on the energy saving amount.
One or more time periods before the heating/cooling system takes the energy-saving measures are taken as a reference period, and one or more time periods after the heating/cooling system takes the energy-saving measures are taken as an evaluation period.
Specifically, the influencing factors of the heating/cooling system are related to the power of the working system, energy-saving working parameters and the like, are also related to external environment factors, namely weather factors, of the working system, are also related to indoor environment factors, particularly personnel factors, of the working of the heating/cooling system, and the heating/cooling system completes the heating/cooling function through coordination and linkage of the external weather factors and the indoor personnel factors, so that the data of the influencing factors are further limited to include: weather factors, work factors, personnel factor data, etc.
According to the technical scheme provided by the embodiment of the disclosure, the energy-saving effect is calculated from the whole system level rather than the equipment level through the acquisition and processing of the weather factors, the working factors and the personnel factor data, and the quantifiable evaluation is generated.
In some embodiments, in the energy saving effect evaluation method, the weather factors include daytime temperature, nighttime temperature, daytime humidity, nighttime humidity, daytime wind speed, nighttime wind speed, days of precipitation, and the like, and the work factors include cooling area, days of holidays, time period factors: depending on the granularity of the calculation time, including the month or the week of the year, etc., the human factors include the number of people, the human traffic, etc.
Specifically, the weather factors, the working factors and the personnel factors which have the largest influence on the heating/refrigerating system are selected, and the influence factors which influence the energy-saving effect of the heating/refrigerating system can be obtained in principle, so that the dependent variable of the influence factors can be comprehensively and accurately obtained, the accurate research of a model can be guaranteed, and the evaluation efficiency of the whole system can be improved.
In some embodiments, in the energy saving effect evaluation method, the selection criteria of the reference period are: the moment of implementing the energy-saving measures is taken as a boundary, and before implementing the energy-saving measures, three times or more integral multiples of a complete heating/refrigerating cycle is taken as a reference period.
According to the technical scheme provided by the embodiment of the disclosure, a selection mode that three times or more integral multiples of a complete heating/refrigerating cycle are used as a reference period can provide enough data length, and stable and reliable data output can be realized on the basis of enough stable time cycle multiples.
In some embodiments, the energy saving effect evaluation method performs data processing on the influence factor data and the energy consumption data in the reference period and the evaluation period respectively, the data processing mainly includes abnormal value elimination, missing value completion, feature extraction and the like, and the feature values of the data which are not represented in a vector form are mapped into probability distribution and can be mapped into standard normal distribution with a mean value of 0 and a standard deviation of 1.
Specifically, data processing and feature vector form data processing can be prepared for data input into a regression model, the data format requirements of the regression model are met, processed data in non-feature vector and feature vector forms can be directly input into a linear regression equation, parameter values of coefficients are further obtained, and energy consumption reference values of a reference period and an evaluation period are obtained on the basis of the regression equation model.
According to the technical scheme provided by the embodiment of the disclosure, the verification and determination of the related data can be realized through a traditional linear regression model, the implementation of the scheme is easy, the application is wide, further, the high-precision processing of the data can be realized through the normalization processing of the standard normal distribution, and the reliability is improved.
In some embodiments, in the energy saving effect evaluation method, the processed data in the form of feature vectors may be input into a decision tree, a support vector machine, a machine learning model, a deep learning model, and the like.
According to the technical scheme provided by the embodiment of the disclosure, the technical effect of the invention can be realized through selection of various models, and the implementation is flexible and easy to popularize.
In some embodiments, the energy-saving effect evaluation method includes processing data to obtain a weather factor vector x 1 Work factor vector x 2 And a personnel factor vector x 3 Splicing to obtain a comprehensive characteristic vector
Wherein d is 0 The sum of the number of feature dimensions contained for the three types of influencing factors,
representing the number of dimensions as d 0 Of (c) is determined.
In some embodiments, the energy-saving effect evaluation method inputs the feature vectors of each factor data into two or more layers of fully-connected neural networks, and the types of activation functions added to the neural networks are as follows: relu, Leaky relu, Parametric relu, relu6, sigmoid, tanh, Swish, Hard-Swish, H-Swish and the like, and the activation functions of the hidden layer neural network and the output layer neural network are the same or different, so as to obtain the coded feature vector.
Specifically, through the multi-layer arrangement of various activation functions and neural networks, the activation functions of the hidden layer neural network and the output layer neural network are the same or different, the flexible arrangement of the selection of the number of layers of the neural network, the activation functions, the hidden layer and the output layer activation functions can be realized, the structure and the working characteristics of the neural network are utilized to the maximum extent, and the optimization learning of the neural network can be realized.
In some embodiments, the energy saving effect evaluation method adds a dropout layer after a fully connected layer in the neural network model to prevent overfitting.
In some embodiments, the energy-saving effect evaluation method includes inputting a feature vector into a fully-connected neural network including two hidden layers, and outputting the encoded feature vector at the hidden layer of the fully-connected neural network of the first layer
Outputting the coded feature vector at the hidden layer of the second layer full-connection neural network
Relu is selected as an activation function, and the formula is as follows:
wherein, d 1 Representing the number of hidden output layer units of the first layer of fully-connected neural network,
vector space representing the hidden output layer of the first fully-connected neural network, d 2 Representing the number of hidden output layer units of the second layer of fully-connected neural network,
a vector space representing the hidden output layer of the second layer of the fully-connected neural network,
and
respectively representing a weight matrix and a bias vector of two hidden layers of the fully-connected neural network, wherein relu is an activation function.
In some embodiments, the feature vectors are combined
Inputting the single-layer full-connection neural network, selecting relu as an activation function, and obtaining the energy consumption reference value of the heating/refrigerating system in the current time period
Wherein R represents the vector space of the single-layer output layer of the fully-connected neural network,
and
are respectively provided withRepresenting the weight vector and bias value of the single output layer, relu being the activation function.
And (3) a model training process: training to obtain an energy consumption factor correlation model by minimizing the following loss function:
where k denotes the number of training samples, y i Representing the true energy consumption value of the ith sample,
and representing the energy consumption reference value calculated by the model of the ith sample.
Fig. 4 is a schematic flow chart of a method for outputting two hidden layers and a single layer of a neural network according to an embodiment of the present disclosure, in which
And
respectively representing the weight matrix and the offset vector of two hidden layers of the fully-connected neural network,
and
and respectively representing a weight vector and a bias value of a single-layer output layer of the fully-connected neural network, wherein relu is an activation function. The left diagram shows a mode of inputting three influencing factor vectors into a neural network, wherein the weather factor vector obtained after data processing is x 1 The work factor vector obtained after data processing is x 2 And the personnel factor vector obtained after data processing is x 3 (ii) a The right diagram shows that a comprehensive characteristic vector x obtained by splicing three influence factor vectors is input into a neural network and output to obtain
Specifically, machine learning is completed in a neural network mode with two hidden layers and a single output layer, and the purpose of the invention can be realized by a small number of neural network layers.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
Fig. 5 is a schematic diagram of a system 500 provided by an embodiment of the present disclosure. As shown in fig. 5, the system 500 of this embodiment includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and operable on the processor 501. The steps in the various method embodiments described above are implemented when the processor 501 executes the computer program 503. Alternatively, the processor 501 implements the functions of the respective modules/units in the above-described respective apparatus embodiments when executing the computer program 503.
Illustratively, the computer program 503 may be partitioned into one or more modules/units, which are stored in the memory 502 and executed by the processor 501 to accomplish the present disclosure. One or more of the modules/units may be a series of computer program instruction segments capable of performing certain functions that describe the execution of the computer program 503 in the system 500.
The system 500 may be an electronic device such as a desktop computer, a notebook, a palm top computer, and a cloud server. The system 500 may include, but is not limited to, a processor 501 and a memory 502. Those skilled in the art will appreciate that fig. 5 is merely an example of system 500 and is not intended to limit system 500 and may include more or fewer components than those shown, or some of the components may be combined, or different components, e.g., the system may also include input-output devices, network access devices, buses, etc.
The Processor 501 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 502 may be an internal storage unit of the system 500, such as a hard disk or a memory of the system 500. The memory 502 may also be an external storage device of the system 500, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the system 500. Further, memory 502 may also include both internal and external storage units of system 500. The memory 502 is used for storing computer programs and other programs and data required by the electronic device. The memory 502 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/system and method may be implemented in other ways. For example, the above-described apparatus/system embodiments are merely illustrative, and for example, a division of modules or units is merely a logical division, and an actual implementation may have another division, multiple units or components may be combined or integrated with another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method in the above embodiments, and may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the above methods and embodiments. The computer program may comprise computer program code which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain suitable additions or additions that may be required in accordance with legislative and patent practices within the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals or telecommunications signals in accordance with legislative and patent practices.
The above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present disclosure, and are intended to be included within the scope of the present disclosure.
Claims (10)
1. An energy saving effect evaluation method is characterized by comprising the following steps:
taking one or more time periods before energy-saving measures are taken as a first time period, taking one or more time periods after energy-saving measures are taken as a second time period, and acquiring data corresponding to the first time period and the second time period respectively, wherein the data comprises influence factor data and energy consumption data;
establishing an energy consumption factor association model by utilizing a preset regression model based on the data of the first time period, wherein the energy consumption factor association model is used for representing the influence relation between the influence factor data and an energy consumption reference value;
inputting the influence factor data of the second time period into the energy consumption factor correlation model, and calculating to obtain an energy consumption reference value of the second time period;
and calculating the difference value between the energy consumption reference value of the second time period and the energy consumption actual value of the second time period, taking the difference value as energy saving amount, and evaluating the energy saving effect based on the energy saving amount.
2. The energy saving effect evaluation method according to claim 1, wherein the acquiring data corresponding to the first time period and the second time period, respectively, comprises:
evaluating the energy saving effect of a system, and acquiring influence factor data and energy consumption data corresponding to the system, wherein the influence factor data comprises weather factors, working factors and personnel factor data, the working factors comprise working area, holiday days and time period factors, and the personnel factors comprise personnel number and personnel throughput; the system is a heating system or a refrigerating system.
3. The energy saving effect evaluation method according to claim 1, wherein after the acquiring of the data corresponding to the first time period and the second time period, respectively, the method further comprises:
and respectively carrying out data processing on the influence factor data and the energy consumption data of the first time period and the second time period, wherein the data processing comprises abnormal value elimination, missing value completion and characteristic extraction.
4. The energy-saving effect evaluation method according to claim 3, wherein after the data processing is performed on the influencing factor data and the energy consumption data of the first time period and the second time period, the eigenvalue of the data expressed in a non-vector form is mapped to a standard normal distribution having a mean value of 0 and a standard deviation of 1.
5. The energy-saving effect evaluation method according to claim 4, wherein the weather factor vector x obtained after the data processing is 1 Work factor vector x 2 And a personnel factor vector x 3 Splicing to obtain a comprehensive characteristic vector
Wherein d is 0 The sum of the number of feature dimensions contained for the three types of influencing factors,
representing a number of feature dimensions as d 0 Of (c) is determined.
6. The energy-saving effect evaluation method according to claim 4 or 5, wherein in the process of establishing the energy consumption factor correlation model, activation functions of the hidden layer neural network and the output layer neural network are respectively and independently selected, and the types of the activation functions include: relu, Leaky relu, Parametric relu, relu6, sigmoid, tanh, Swish, Hard-Swish and H-Swish, and feature vectors of influencing factor data of a first time period are input into two or more layers of neural networks to obtain output feature vectors of each layer after coding.
7. The energy-saving effect evaluation method according to claim 6, wherein the eigenvectors of the first-period influencing factor data are input into a fully-connected neural network of two hidden layers and a single-layer output layer, and the encoded eigenvectors are obtained by output from the first hidden layer
Obtaining coded feature vector at the output of the second layer hidden layer
Obtaining coded characteristic vector, namely system energy consumption reference value at single-layer output layer
And selecting relu as an activation function of the hidden layer and the output layer, wherein the formula is as follows:
wherein d is 1 Representing the number of hidden output layer units of the first layer of fully-connected neural network,
vector space representing the hidden output layer of the first fully-connected neural network, d 2 Representing the number of hidden output layer units of the second layer of fully-connected neural network,
a vector space representing the hidden output layer of the second layer of the fully-connected neural network,
and
respectively representing the weight matrix and the offset vector of two hidden layers of the fully-connected neural network,
and
and respectively representing the weight vector and the offset value of the single-layer output layer, wherein R represents the vector space of the single-layer output layer of the fully-connected neural network, and relu is an activation function.
8. An energy-saving effect evaluation system, characterized by comprising:
the energy saving control system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is configured to take one or more time periods before energy saving measures are taken as a first time period, take one or more time periods after energy saving measures are taken as a second time period, and acquire data corresponding to the first time period and the second time period respectively, wherein the data comprise influence factor data and energy consumption data;
the establishing module is configured to establish an energy consumption factor association model by using a preset regression model based on the data of the first time period, wherein the energy consumption factor association model is used for representing the influence relation between the influence factor data and an energy consumption reference value;
the calculation module is configured to input the influence factor data of the second time period into the energy consumption factor correlation model, and calculate an energy consumption reference value of the second time period;
and the evaluation module is configured to calculate a difference value between the energy consumption reference value of the second time period and the energy consumption actual value of the second time period, take the difference value as energy saving amount, and evaluate the energy saving effect based on the energy saving amount.
9. An apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 7 when executing the program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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| CN116528270B (en) * | 2023-06-27 | 2023-10-03 | 杭州电瓦特科技有限公司 | Base station energy saving potential evaluation method, device, equipment and storage medium |
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