CN116029562A - New energy consumption proportion contribution evaluation model training method, evaluation method and device - Google Patents
New energy consumption proportion contribution evaluation model training method, evaluation method and device Download PDFInfo
- Publication number
- CN116029562A CN116029562A CN202211469275.0A CN202211469275A CN116029562A CN 116029562 A CN116029562 A CN 116029562A CN 202211469275 A CN202211469275 A CN 202211469275A CN 116029562 A CN116029562 A CN 116029562A
- Authority
- CN
- China
- Prior art keywords
- energy consumption
- new energy
- consumption proportion
- historical
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a new energy consumption proportion contribution evaluation model training method, an evaluation method and a device, wherein the training method comprises the following steps: determining a historical new energy consumption proportion contribution score of historical day-ahead schedule data according to a target schedule meeting the annual new energy consumption proportion of the comprehensive energy base; generating a data set according to the corresponding relation among the historical day-ahead scheduling plan data, the historical day-ahead power prediction data and the historical new energy consumption proportion contribution score; training a preset deep neural network by adopting a data set to obtain a new energy consumption proportion contribution assessment model for outputting a new energy consumption proportion contribution score. According to the method and the device, the training effectiveness and reliability of the new energy consumption proportion contribution evaluation model can be effectively improved, the accuracy and effectiveness of the new energy consumption proportion contribution evaluation result can be improved, the energy consumption waste caused by centralized adjustment for reaching standards is avoided, and the rationality and reliability of energy configuration of the comprehensive energy base can be effectively improved.
Description
Technical Field
The application relates to the technical field of energy scheduling, in particular to a new energy consumption proportion contribution evaluation model training method, an evaluation method and a device.
Background
The large energy resource can collect wind, light and thermal power and send the collected wind, light and thermal power out through direct current. The operation of the large energy base needs to make a scheduling plan of wind, light and heat power in advance, so that the defects of insufficient standby capacity caused by various reasons or a large amount of waste wind and waste light caused by unreasonable configuration are avoided. One way of operation assessment of a large-scale energy base is clean energy consumption ratio, and the consumed wind power and photovoltaic electric quantity can reach a certain proportion of total electric quantity in the operation process of one year. At present, this goal is not considered in the existing scheduling plan generation method, and there is a possibility that the thermal power output ratio is intensively reduced to reach the assessment goal when approaching the assessment time node. This countermeasure has a problem of wasting energy consumption.
The existing new energy consumption proportion contribution evaluation mode depends on manual experience, is rough and cannot achieve good effects, for example, the evaluation is performed by adopting the same standard (for example, whether the new energy output reaches 50%) at different days when scheduling is planned before the day is manufactured, and the same evaluation mode is obviously inaccurate at different times due to the seasonal characteristic of wind-electricity photovoltaic.
Disclosure of Invention
In view of this, embodiments of the present application provide new energy consumption proportion contribution estimation model training methods, estimation methods, and apparatuses to eliminate or ameliorate one or more of the deficiencies of the prior art.
The first aspect of the application provides a new energy consumption proportion contribution evaluation training method, which comprises the following steps:
constructing a target scheduling plan meeting the annual new energy consumption ratio of the comprehensive energy base;
determining a historical new energy consumption proportion contribution score of historical day-ahead scheduling plan data of the comprehensive energy base according to the target scheduling plan;
generating a data set according to the corresponding relation among the historical day-ahead scheduling plan data, the historical day-ahead power prediction data corresponding to the historical day-ahead scheduling plan data and the historical new energy consumption proportion contribution score;
and training a preset deep neural network by adopting the data set to obtain a new energy consumption proportion contribution assessment model for outputting new energy consumption proportion contribution scores of the daily scheduling plan data according to the daily scheduling plan data and the corresponding daily power prediction data of the comprehensive energy base.
In some embodiments of the present application, the determining the historical new energy consumption proportion contribution score of the historical daily schedule data of the integrated energy base according to the target schedule includes:
Acquiring historical day-ahead power prediction data samples corresponding to a plurality of historical sampling time points of the comprehensive energy base respectively, wherein each historical day-ahead power prediction data sample comprises: historical day-ahead photovoltaic power prediction data and historical day-ahead wind power prediction data;
acquiring historical day-ahead schedule plan data samples of the next day corresponding to each historical day-ahead power prediction data sample, wherein each historical day-ahead schedule plan sample comprises: historical day-ahead schedule plan photovoltaic duty cycle, historical day-ahead schedule plan wind power duty cycle, and historical day-ahead schedule plan thermal power duty cycle;
and respectively determining the historical new energy consumption proportion contribution scores corresponding to the historical daily scheduling plan samples according to the new energy consumption proportion of the current day under the target scheduling plan and the new energy consumption proportion corresponding to the historical daily scheduling plan samples.
In some embodiments of the present application, before the training of the preset deep neural network using the data set, the method further includes:
constructing a loss function for representing a difference value between a new energy consumption proportion contribution score and the new energy consumption proportion contribution score output by the deep neural network;
Correspondingly, the training the preset deep neural network by adopting the data set comprises the following steps:
and training a preset deep neural network based on the data set by taking the minimum value of the loss function as a target.
In some embodiments of the present application, the historical day-ahead power prediction data and the historical day-ahead schedule plan data are both time-series data;
correspondingly, the deep neural network comprises: LSTM model.
In some embodiments of the present application, the determining, according to the new energy consumption ratio of the current day under the target schedule and the new energy consumption ratio corresponding to each of the historical day-ahead schedule samples, the contribution score of the historical new energy consumption ratio corresponding to each of the historical day-ahead schedule samples includes:
determining a historical new energy consumption proportion contribution score corresponding to each historical day-ahead scheduling plan sample based on a preset new energy consumption proportion contribution score quantification formula;
wherein, the new energy consumption proportion contribution score quantization formula includes:
in the formula (1), S i A new energy consumption proportion contribution score is represented; c (C) i Representing new energy consumption duty ratios corresponding to the historical daily schedule samples; 
And representing the new energy consumption duty ratio of the current day under the target dispatching plan.
The second aspect of the application provides a new energy consumption proportion contribution evaluation method of an integrated energy base, comprising the following steps:
acquiring front scheduling plan data and corresponding daily front power prediction data of the comprehensive energy base;
inputting the daily scheduling plan data and the corresponding daily power prediction data into a new energy consumption proportion contribution assessment model so that the new energy consumption proportion contribution assessment model outputs a new energy consumption proportion contribution score corresponding to the daily scheduling plan data;
the new energy consumption proportion contribution evaluation model is trained based on the new energy consumption proportion contribution evaluation training method in advance.
A third aspect of the present application provides a new energy consumption proportion contribution evaluation training device, including:
the target plan construction module is used for constructing a target scheduling plan meeting the annual new energy consumption ratio of the comprehensive energy base;
the scoring calculation module is used for determining historical new energy consumption proportion contribution scores of historical daily scheduling plan data of the comprehensive energy base according to the target scheduling plan;
The data set generation module is used for generating a data set according to the corresponding relation among the historical day-ahead scheduling plan data, the historical day-ahead power prediction data corresponding to the historical day-ahead scheduling plan data and the historical new energy consumption proportion contribution score;
and the model training module is used for training a preset deep neural network by adopting the data set to obtain a new energy consumption proportion contribution assessment model for outputting new energy consumption proportion contribution scores of the daily scheduling plan data according to the daily scheduling plan data of the comprehensive energy base and the corresponding daily power prediction data.
A fourth aspect of the present application provides a new energy consumption proportion contribution evaluation device of an integrated energy base, including:
the data acquisition module is used for acquiring front scheduling plan data and corresponding daily front power prediction data of the comprehensive energy base;
the model prediction module is used for inputting the daily scheduling plan data and the corresponding daily power prediction data into a new energy consumption proportion contribution evaluation model so that the new energy consumption proportion contribution evaluation model outputs a new energy consumption proportion contribution score corresponding to the daily scheduling plan data;
The new energy consumption proportion contribution evaluation model is trained based on the new energy consumption proportion contribution evaluation training method in advance.
In a fifth aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the new energy consumption proportion contribution estimation training method or implements the new energy consumption proportion contribution estimation method of the integrated energy base when executing the computer program.
A sixth aspect of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the new energy consumption proportion contribution estimation training method, or implements the new energy consumption proportion contribution estimation method of the integrated energy base.
According to the new energy consumption proportion contribution evaluation model training method, a target scheduling plan meeting the annual new energy consumption proportion of the comprehensive energy base is constructed; determining a historical new energy consumption proportion contribution score of historical day-ahead scheduling plan data of the comprehensive energy base according to the target scheduling plan; generating a data set according to the corresponding relation among the historical day-ahead scheduling plan data, the historical day-ahead power prediction data corresponding to the historical day-ahead scheduling plan data and the historical new energy consumption proportion contribution score; the data set is adopted to train a preset deep neural network so as to obtain a new energy consumption proportion contribution assessment model for outputting new energy consumption proportion contribution scores of the daily scheduling plan data according to the daily scheduling plan data and the corresponding daily power prediction data of the comprehensive energy base, training effectiveness and reliability of the new energy consumption proportion contribution assessment model can be effectively improved, accuracy and effectiveness of new energy consumption proportion contribution assessment results can be improved, an auxiliary scheduling decision function is played, energy consumption waste caused by centralized adjustment for reaching standards is avoided, and rationality and reliability of energy configuration of the comprehensive energy base can be effectively improved.
Additional advantages, objects, and features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.
It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present application are not limited to the above-detailed description, and that the above and other objects that can be achieved with the present application will be more clearly understood from the following detailed description.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a new energy consumption proportion contribution evaluation model training method in an embodiment of the application.
FIG. 2 is a schematic flow chart of another embodiment of a new energy consumption proportional contribution assessment model training method.
Fig. 3 is a flow chart of a new energy consumption proportion contribution evaluation method according to another embodiment of the present application.
Fig. 4 is a schematic structural diagram of a training device for a new energy consumption proportional contribution assessment model according to another embodiment of the present application.
Fig. 5 is a schematic structural diagram of a new energy consumption proportion contribution evaluation device according to another embodiment of the present application.
Fig. 6 is a schematic flow chart of a new energy consumption proportion contribution evaluation model training and new energy consumption proportion contribution evaluation method provided in an application example of the present application.
Fig. 7 is a schematic diagram illustrating the structure of the LSTM model in an application example of the present application.
Detailed Description
The following description of the embodiments of the present application will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
It should be noted here that, in order to avoid obscuring the present application due to unnecessary details, only structures and/or processing steps closely related to the solution according to the present application are shown in the drawings, while other details not greatly related to the present application are omitted.
It should be emphasized that the term "comprises/comprising" when used herein is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
It is also noted herein that the term "coupled" may refer to not only a direct connection, but also an indirect connection in which an intermediate is present, unless otherwise specified.
Hereinafter, embodiments of the present application will be described with reference to the drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.
Considering the problems that the existing day-ahead scheduling plan generation method does not always consider the annual new energy consumption ratio index, even if the annual new energy consumption ratio index is considered, a good enough effect cannot be obtained due to low method effectiveness, and the like, the embodiment of the application provides a new energy consumption ratio contribution evaluation model training method.
The following examples are provided to illustrate the invention in more detail.
The embodiment of the application provides a new energy consumption proportion contribution evaluation model training method which can be realized by a new energy consumption proportion contribution evaluation model training device, and referring to fig. 1, the new energy consumption proportion contribution evaluation model training method specifically comprises the following contents:
step 100: and constructing a target scheduling plan meeting the annual new energy consumption ratio of the comprehensive energy base.
Specifically, according to historical daily wind power and photovoltaic power monitoring data, a target scheduling plan is established according to the situation that wind power and photovoltaic power are as many as possible.
Specifically, a new annual energy consumption rate under any one of the scheduling plans is calculated, and if the new annual energy consumption rate is equal to or higher than a target rate, the scheduling plan is determined as the target scheduling plan, and if the new annual energy consumption rate is not equal to or higher than the target rate, the daily thermal power output plan DeltaR is lowered T And the new energy consumption duty ratio is just met, so that the scheduling plan can meet the annual new energy consumption duty ratio, and the target plan is determined as the target scheduling plan.
Step 200: and determining a historical new energy consumption proportion contribution score of historical day-ahead scheduling plan data of the comprehensive energy base according to the target scheduling plan.
In step 200, the new energy consumption proportion contribution evaluation model training device first receives historical day-ahead schedule data corresponding to historical day-ahead power prediction data collected from a database of the integrated energy base. It will be appreciated that the historical day-ahead power forecast data and the corresponding historical day-ahead schedule data specifically refer to: and acquiring historical day-ahead power prediction data samples acquired at each historical sampling time point respectively, and acquiring a next day historical day-ahead scheduling plan data sample corresponding to the current day at the historical sampling time point of each historical day-ahead power prediction data sample.
Step 300: and generating a data set according to the corresponding relation among the historical day-ahead scheduling plan data, the historical day-ahead power prediction data corresponding to the historical day-ahead scheduling plan data and the historical new energy consumption proportion contribution score.
It may be appreciated that the data set stores a correspondence between the historical day-ahead schedule data, the historical day-ahead power prediction data, and the historical new energy consumption proportion contribution score, where the correspondence refers to a correspondence between the historical day-ahead power prediction data and the historical new energy consumption proportion contribution score, and the data set stores not only the historical day-ahead power prediction data and the historical new energy consumption proportion contribution score.
Step 400: and training a preset deep neural network by adopting the data set to obtain a new energy consumption proportion contribution assessment model for outputting new energy consumption proportion contribution scores of the daily scheduling plan data according to the daily scheduling plan data and the corresponding daily power prediction data of the comprehensive energy base.
In one or more embodiments of the present application, the new energy consumption proportional contribution score may be simply referred to as a new energy consumption score.
In step 400, training a preset deep neural network by using the data set, specifically: the data set can be used as a training set to train the deep neural network; the data set can be divided into a training set, a verification set, a test set and the like, so that after the deep neural network is trained according to the training set, the deep neural network is further optimized according to the verification set and the test set and the like, the reliability and the effectiveness of a model training result are improved, and the model training method can be specifically set according to actual application situations.
In one or more embodiments of the present application, the new energy consumption proportion contribution estimation model is a machine learning model for generating new energy consumption proportion contribution estimation, and the architecture of the model is the same as the infrastructure of the deep neural network, that is, the new energy consumption proportion contribution estimation model is the deep neural network with current training completed, and then, according to actual application requirements, the latest updated historical daily scheduling plan data, the historical daily power prediction data and the historical new energy consumption proportion contribution score can be collected from the database of the integrated energy base regularly, and then, the updated historical daily scheduling plan data, the historical daily power prediction data and the historical new energy consumption proportion contribution score are utilized to perform optimization iteration on the new energy consumption proportion contribution estimation model, so as to obtain an updated new energy consumption proportion contribution estimation model, so as to further improve reliability and effectiveness of model training results, and be more suitable for state change of the integrated energy base.
From the above description, it can be seen that the training method for the new energy consumption proportion contribution assessment model provided by the embodiment of the application can effectively improve the training effectiveness and reliability of the new energy consumption proportion contribution assessment model, can improve the accuracy and effectiveness of the new energy consumption proportion contribution assessment result, plays a role in assisting in scheduling decision, avoids energy consumption waste caused by centralized adjustment for reaching standards, and can effectively improve the rationality and reliability of energy configuration of the comprehensive energy base.
In order to further improve the effectiveness and reliability of the data base for training the model, in the training method of the new energy consumption proportion contribution estimation model provided in the embodiment of the present application, referring to fig. 2, step 200 of the training method of the new energy consumption proportion contribution estimation model specifically includes the following contents:
step 210: acquiring historical day-ahead power prediction data samples corresponding to a plurality of historical sampling time points of the comprehensive energy base respectively, wherein each historical day-ahead power prediction data sample comprises: historical day-ahead photovoltaic power prediction data and historical day-ahead wind power prediction data.
Step 220: acquiring historical day-ahead schedule plan data samples of the next day corresponding to each historical day-ahead power prediction data sample, wherein each historical day-ahead schedule plan sample comprises: historical day-ahead schedule photovoltaic duty cycle, historical day-ahead schedule wind power duty cycle, and historical day-ahead schedule thermal power duty cycle.
Step 230: and respectively determining the historical new energy consumption proportion contribution scores corresponding to the historical daily scheduling plan samples according to the new energy consumption proportion of the current day under the target scheduling plan and the new energy consumption proportion corresponding to the historical daily scheduling plan samples.
Specifically, the daily previous scheduling plan in the historical data can be compared with the target scheduling plan, the new energy consumption ratio of the day under the target scheduling plan and the new energy consumption ratio under the historical daily previous scheduling plan are calculated, and then the historical new energy consumption proportion contribution scores corresponding to the historical daily previous scheduling plan samples are calculated respectively.
In order to further improve the application effectiveness of the training model, in the training method of the new energy consumption proportion contribution estimation model provided in the embodiment of the present application, referring to fig. 2, before step 400 of the training method of the new energy consumption proportion contribution estimation model, the method specifically further includes the following contents:
step 010: and constructing a loss function for representing the difference value between the new energy consumption proportion contribution score and the new energy consumption proportion contribution score output by the deep neural network.
In step 010, a difference value between the new energy consumption proportion contribution score and the new energy consumption proportion contribution score output by the deep neural network may be determined according to an average value of squares of differences between the new energy consumption proportion contribution score and the new energy consumption proportion contribution score.
Correspondingly, referring to fig. 2, the step 400 of the training method of the new energy consumption proportion contribution evaluation model specifically includes the following:
step 410: and training a preset deep neural network based on the data set by taking the minimum value of the loss function as a target to obtain a new energy consumption proportion contribution assessment model for outputting new energy consumption proportion contribution scores of the daily scheduling plan data according to the daily scheduling plan data of the comprehensive energy base and the corresponding daily power prediction data.
In order to further improve the reliability of model mapping learning, in the new energy consumption proportion contribution evaluation model training method provided by the embodiment of the application, the historical daily power prediction data and the historical daily scheduling plan data are both time sequence data; correspondingly, the deep neural network comprises: LSTM model.
It is understood that the LSTM model refers to a Long short-term memory (Long short-term memory) model.
In order to further improve the effectiveness of quantifying the new energy consumption proportion contribution, in the new energy consumption proportion contribution evaluation model training method provided in the embodiment of the present application, step 230 in the new energy consumption proportion contribution evaluation model training method specifically includes the following contents:
determining a historical new energy consumption proportion contribution score corresponding to each historical day-ahead scheduling plan sample based on a preset new energy consumption proportion contribution score quantification formula;
wherein, the new energy consumption proportion contribution score quantization formula includes:
in the formula (1), S i A new energy consumption proportion contribution score is represented; c (C) i Representing new energy consumption duty ratios corresponding to the historical daily schedule samples;
and representing the new energy consumption duty ratio of the current day under the target dispatching plan.
Based on the embodiment of the new energy consumption proportion contribution evaluation model training method, the application also provides an embodiment of a new energy consumption proportion contribution evaluation method of a comprehensive energy base, and referring to fig. 3, the new energy consumption proportion contribution evaluation method of the comprehensive energy base specifically comprises the following contents:
Step 500: front scheduling plan data and corresponding day-ahead power forecast data of the integrated energy base are obtained.
Step 600: inputting the daily scheduling plan data and the corresponding daily power prediction data into a new energy consumption proportion contribution assessment model so that the new energy consumption proportion contribution assessment model outputs a new energy consumption proportion contribution score corresponding to the daily scheduling plan data; the new energy consumption proportion contribution evaluation model is trained and obtained in advance based on the new energy consumption proportion contribution evaluation model training method.
The new energy consumption proportion contribution evaluation model in the new energy consumption proportion contribution evaluation method of the integrated energy base provided by the application can be specifically realized based on the processing flow of the embodiment of the new energy consumption proportion contribution evaluation model training method in the embodiment, and the functions of the new energy consumption proportion contribution evaluation model training method are not described herein, and can be referred to in the detailed description of the embodiment of the new energy consumption proportion contribution evaluation model training method.
From the above description, it can be seen that the new energy consumption proportion contribution evaluation method provided by the embodiment of the application can effectively improve the training effectiveness and reliability of the new energy consumption proportion contribution evaluation model, can improve the accuracy and effectiveness of the new energy consumption proportion contribution evaluation result, plays a role in assisting in scheduling decision, avoids energy consumption waste caused by centralized adjustment for reaching standards, and can effectively improve the rationality and reliability of energy configuration of the comprehensive energy base.
From the aspect of software, the present application further provides a new energy consumption proportion contribution evaluation model training device for executing all or part of the new energy consumption proportion contribution evaluation model training method, referring to fig. 4, where the new energy consumption proportion contribution evaluation model training device is connected to a database of a comprehensive energy base and the new energy consumption proportion contribution evaluation device respectively, so as to retrieve historical data from the database, and send a model obtained by training to the new energy consumption proportion contribution evaluation device for on-line application, where the new energy consumption proportion contribution evaluation model training device specifically includes:
the target plan construction module 10 is used for constructing a target scheduling plan meeting the annual new energy consumption ratio of the comprehensive energy base.
And the score calculation module 20 is used for determining a historical new energy consumption proportion contribution score of the historical daily scheduling plan data of the comprehensive energy base according to the target scheduling plan.
The data set generating module 30 is configured to generate a data set according to the corresponding relationship between the historical day-ahead schedule data, the historical day-ahead power prediction data corresponding to the historical day-ahead schedule data, and the historical new energy consumption proportion contribution score.
The model training module 40 is configured to train a preset deep neural network by using the data set, so as to obtain a new energy consumption proportion contribution assessment model for outputting a new energy consumption proportion contribution score of the daily scheduling plan data according to the daily scheduling plan data and the corresponding daily power prediction data of the comprehensive energy base.
The embodiment of the new energy consumption proportion contribution estimation model training device provided by the application can be specifically used for executing the processing flow of the embodiment of the new energy consumption proportion contribution estimation model training method in the embodiment, and the functions of the embodiment of the new energy consumption proportion contribution estimation model training method are not described herein, and can be referred to for a detailed description of the embodiment of the new energy consumption proportion contribution estimation model training method.
From the above description, it can be seen that the training device for the new energy consumption proportion contribution evaluation model provided by the embodiment of the application can effectively improve the training effectiveness and reliability of the new energy consumption proportion contribution evaluation model, can improve the accuracy and effectiveness of the new energy consumption proportion contribution evaluation result, plays a role in assisting in scheduling decision, avoids energy consumption waste caused by centralized adjustment for reaching standards, and can effectively improve the rationality and reliability of energy configuration of the comprehensive energy base.
From the aspect of software, the present application further provides a new energy consumption proportion contribution evaluation device for executing all or part of the new energy consumption proportion contribution evaluation method, referring to fig. 5, where the new energy consumption proportion contribution evaluation device is respectively in communication connection with the new energy consumption proportion contribution evaluation model training device and a client device held by a user, so as to be capable of receiving a new energy consumption proportion contribution evaluation model from a day-ahead schedule model training device, and sending predicted output schedule data to the client device for viewing by the user, and the new energy consumption proportion contribution evaluation device specifically includes:
a data acquisition module 50 for acquiring front dispatch plan data and corresponding front-of-day power prediction data of the integrated energy base;
the model prediction module 60 is configured to input the daily scheduling plan data and the corresponding daily power prediction data into a new energy consumption proportion contribution assessment model, so that the new energy consumption proportion contribution assessment model outputs a new energy consumption proportion contribution score corresponding to the daily scheduling plan data; the new energy consumption proportion contribution evaluation model is trained and obtained in advance based on the new energy consumption proportion contribution evaluation model training method.
The embodiment of the new energy consumption proportion contribution evaluation device provided in the application may be specifically used for executing the processing flow of the embodiment of the new energy consumption proportion contribution evaluation method in the above embodiment, and the functions thereof are not described herein, and may refer to the detailed description of the embodiment of the new energy consumption proportion contribution evaluation method.
From the above description, it can be seen that the new energy consumption proportion contribution evaluation device provided by the embodiment of the application can effectively improve the training effectiveness and reliability of the new energy consumption proportion contribution evaluation model, can improve the accuracy and effectiveness of the new energy consumption proportion contribution evaluation result, plays a role in assisting in scheduling decision, avoids energy consumption waste caused by centralized adjustment for reaching standards, and can effectively improve the rationality and reliability of energy configuration of the comprehensive energy base.
It may be understood that the portion of the new energy consumption proportion contribution estimation model training device that performs new energy consumption proportion contribution estimation model training, and the portion of the new energy consumption proportion contribution estimation device that performs new energy consumption proportion contribution estimation may be completed in the client device. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The present application is not limited in this regard. If all operations are completed in the client device, the client device may further include a processor for specific processing of new energy consumption proportion contribution assessment model training and new energy consumption proportion contribution assessment.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Any suitable network protocol may be used for communication between the server and the client device, including those not yet developed at the filing date of this application. The network protocols may include, for example, TCP/IP protocol, UDP/IP protocol, HTTP protocol, HTTPS protocol, etc. Of course, the network protocol may also include, for example, RPC protocol (Remote Procedure Call Protocol ), REST protocol (Representational State Transfer, representational state transfer protocol), etc. used above the above-described protocol.
In order to further explain the scheme, the application further provides a specific application example of the new energy consumption proportion contribution assessment model training and new energy consumption proportion contribution assessment method, the contribution of the daily scheduling plan to the annual new energy consumption ratio can be assessed, the scheme firstly utilizes a scoring mode to score the contribution of the daily scheduling plan to the new energy consumption ratio of the corresponding year in the historical data, then takes the daily wind power, the photovoltaic power prediction data and the daily scheduling plan of the historical data as inputs, and takes the corresponding score as a target to output and train the deep neural network model. After training, the network model can take the day-ahead power prediction data and the day-ahead scheduling plan as input, and output the contribution score of the scheduling plan to the new annual energy consumption ratio, so as to assist the scheduler in making further decisions.
Referring to fig. 6, the new energy consumption proportion contribution evaluation model training and new energy consumption proportion contribution evaluation method provided by the application example specifically includes the following contents:
s1, according to historical daily wind power and photovoltaic power monitoring data, a scheduling plan is established according to the situation that wind power and photovoltaic power are as many as possible.
S2, calculating the annual new energy consumption ratio under the dispatching plan, if the annual new energy consumption ratio reaches the target ratio or above, jumping to the step S3, and if the annual new energy consumption ratio does not reach the consumption ratio, reducing the daily thermal power output plan delta R T Until just meeting the new energy consumption duty ratio. At this time, the target scheduling plan is
In the middle of
Wind power, photovoltaic power and thermal power output ratios at the ith moment and the jth moment are respectively calculated, and the scheduling plan can meet the annual new energy consumption ratio and is called a target scheduling plan.
S3, comparing the daily front scheduling plan in the historical data with the target scheduling plan, and calculating the new energy consumption duty ratio of the day under the target scheduling plan
And new energy consumption duty ratio C under historical day-ahead scheduling plan i The day-ahead schedule scores:
s4, a training data set consisting of daily wind power prediction data, daily photovoltaic power prediction data, daily scheduling plans and new energy consumption duty ratio scores corresponding to the daily scheduling plans is established. The composition of the individual samples is shown below:
Wherein the left side is model input and comprises wind power prediction data P Wi Photovoltaic power prediction data P Pi Wind power duty ratio R of day-ahead scheduling plan Wi Solar energy scheduling photovoltaic duty cycle R Pi Thermal power duty ratio R of day-ahead dispatch plan Ti The right side is the target output of the model, namely the new energy consumption duty ratio score S of the day-ahead scheduling plan i 。
S5, establishing a model, taking P into consideration Wi 、P Pi 、R Wi 、R Pi 、R Ti Are time series data, here, taking a deep neural network model based on an LSTM module as an example, and a specific structure is shown in fig. 7. The LSTM model includes: a network composed of a plurality of LSTM layers connected in sequence, and a multi-layer perceptron MLP connected with the network.
S6, establishing a loss function:
s7, training the model in S5 by using the data set established in S4 and the loss function in S6 to obtain a trained model.
S8, inputting the daily wind power prediction data, the daily photovoltaic power prediction data and the daily scheduling plan into the trained model in S7 to obtain a new energy consumption duty ratio score of the daily scheduling plan, wherein the higher the score is, the higher the contribution of the daily scheduling plan to the annual new energy consumption duty ratio is.
In summary, the new energy consumption proportion contribution evaluation model training and the new energy consumption proportion contribution evaluation method provided by the application example of the application provide a annual new energy consumption proportion contribution quantization scoring mode of a historical day-ahead schedule, realize the network structure which is input into power prediction data and the day-ahead schedule and output into the new energy consumption contribution score, provide a set of flow methods for establishing a data set by performing quantization scoring on the new energy consumption proportion contribution of the historical day-ahead schedule and performing deep learning network training through the data set so as to realize estimation on the new energy consumption proportion contribution of the current day-ahead schedule. The contribution of the day-ahead schedule to the annual new energy consumption duty cycle can be scored after its generation to assist in subsequent schedule optimization or decision-making.
The embodiment of the application further provides an electronic device (i.e., an electronic device), where the electronic device may include a processor, a memory, a receiver, and a transmitter, where the processor is configured to execute the new energy consumption proportion contribution estimation model training method or the new energy consumption proportion contribution estimation method mentioned in the foregoing embodiment, and the processor and the memory may be connected by a bus or other manners, for example, through a bus connection. The receiver may be connected to the processor, memory, by wire or wirelessly.
The processor may be a central processing unit (Central Processing Unit, CPU). The processor may also be any other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof.
The memory, as a non-transitory computer readable storage medium, may be used to store a non-transitory software program, a non-transitory computer executable program, and a module, such as a program instruction/module corresponding to the new energy consumption proportion contribution evaluation model training method or the new energy consumption proportion contribution evaluation method in the embodiments of the present application. The processor executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory, that is, the new energy consumption proportion contribution estimation model training method or the new energy consumption proportion contribution estimation method in the above method embodiment is implemented.
The memory may include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data created by the processor, etc. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory that, when executed by the processor, perform the new energy consumption rate contribution estimation model training method or the new energy consumption rate contribution estimation method of the embodiments.
In some embodiments of the present application, the user equipment may include a processor, a memory, and a transceiver unit, where the transceiver unit may include a receiver and a transmitter, and the processor, the memory, the receiver, and the transmitter may be connected by a bus system, the memory storing computer instructions, and the processor executing the computer instructions stored in the memory to control the transceiver unit to transmit and receive signals.
As an implementation manner, the functions of the receiver and the transmitter in the present application may be considered to be implemented by a transceiver circuit or a dedicated chip for transceiver, and the processor may be considered to be implemented by a dedicated processing chip, a processing circuit or a general-purpose chip.
As another implementation manner, a manner of using a general-purpose computer may be considered to implement the server provided in the embodiments of the present application. I.e. program code for implementing the functions of the processor, the receiver and the transmitter are stored in the memory, and the general purpose processor implements the functions of the processor, the receiver and the transmitter by executing the code in the memory.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, is used for realizing the steps of the new energy consumption proportion contribution evaluation model training method or the new energy consumption proportion contribution evaluation method. The computer readable storage medium may be a tangible storage medium such as Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disk, a removable memory disk, a CD-ROM, or any other form of storage medium known in the art.
Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein can be implemented as hardware, software, or a combination of both. The particular implementation is hardware or software dependent on the specific application of the solution and the design constraints. 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 application. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave.
It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.
The features described and/or illustrated in this application for one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
The foregoing description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and variations may be made to the embodiment of the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (10)
1. The new energy consumption proportion contribution evaluation training method is characterized by comprising the following steps of:
constructing a target scheduling plan meeting the annual new energy consumption ratio of the comprehensive energy base;
determining a historical new energy consumption proportion contribution score of historical day-ahead scheduling plan data of the comprehensive energy base according to the target scheduling plan;
generating a data set according to the corresponding relation among the historical day-ahead scheduling plan data, the historical day-ahead power prediction data corresponding to the historical day-ahead scheduling plan data and the historical new energy consumption proportion contribution score;
And training a preset deep neural network by adopting the data set to obtain a new energy consumption proportion contribution assessment model for outputting new energy consumption proportion contribution scores of the daily scheduling plan data according to the daily scheduling plan data and the corresponding daily power prediction data of the comprehensive energy base.
2. The new energy consumption rate contribution estimation training method of claim 1, wherein the determining the historical new energy consumption rate contribution score of the historical daily schedule data of the integrated energy base according to the target schedule includes:
acquiring historical day-ahead power prediction data samples corresponding to a plurality of historical sampling time points of the comprehensive energy base respectively, wherein each historical day-ahead power prediction data sample comprises: historical day-ahead photovoltaic power prediction data and historical day-ahead wind power prediction data;
acquiring historical day-ahead schedule plan data samples of the next day corresponding to each historical day-ahead power prediction data sample, wherein each historical day-ahead schedule plan sample comprises: historical day-ahead schedule plan photovoltaic duty cycle, historical day-ahead schedule plan wind power duty cycle, and historical day-ahead schedule plan thermal power duty cycle;
And respectively determining the historical new energy consumption proportion contribution scores corresponding to the historical daily scheduling plan samples according to the new energy consumption proportion of the current day under the target scheduling plan and the new energy consumption proportion corresponding to the historical daily scheduling plan samples.
3. The new energy consumption rate contribution estimation training method of claim 1, further comprising, prior to training a preset deep neural network using the data set:
constructing a loss function for representing a difference value between a new energy consumption proportion contribution score and the new energy consumption proportion contribution score output by the deep neural network;
correspondingly, the training the preset deep neural network by adopting the data set comprises the following steps:
and training a preset deep neural network based on the data set by taking the minimum value of the loss function as a target.
4. The new energy consumption proportion contribution evaluation training method of claim 1, wherein the historical day-ahead power prediction data and the historical day-ahead schedule plan data are both time-series data;
correspondingly, the deep neural network comprises: LSTM model.
5. The method for training the new energy consumption proportion contribution evaluation according to claim 2, wherein the determining the historical new energy consumption proportion contribution score corresponding to each of the historical daily schedule samples according to the new energy consumption ratio of the current day under the target schedule and the new energy consumption ratio corresponding to each of the historical daily schedule samples includes:
determining a historical new energy consumption proportion contribution score corresponding to each historical day-ahead scheduling plan sample based on a preset new energy consumption proportion contribution score quantification formula;
wherein, the new energy consumption proportion contribution score quantization formula includes:
in the formula (1), S i A new energy consumption proportion contribution score is represented; c (C) i Representing new energy consumption duty ratios corresponding to the historical daily schedule samples;
and representing the new energy consumption duty ratio of the current day under the target dispatching plan.
6. The new energy consumption proportion contribution evaluation method of the comprehensive energy base is characterized by comprising the following steps of:
acquiring front scheduling plan data and corresponding daily front power prediction data of the comprehensive energy base;
inputting the daily scheduling plan data and the corresponding daily power prediction data into a new energy consumption proportion contribution assessment model so that the new energy consumption proportion contribution assessment model outputs a new energy consumption proportion contribution score corresponding to the daily scheduling plan data;
The new energy consumption proportion contribution evaluation model is obtained by training in advance based on the new energy consumption proportion contribution evaluation training method according to any one of claims 1 to 5.
7. The utility model provides a new forms of energy consumption proportion contribution aassessment trainer which characterized in that includes:
the target plan construction module is used for constructing a target scheduling plan meeting the annual new energy consumption ratio of the comprehensive energy base;
the scoring calculation module is used for determining historical new energy consumption proportion contribution scores of historical daily scheduling plan data of the comprehensive energy base according to the target scheduling plan;
the data set generation module is used for generating a data set according to the corresponding relation among the historical day-ahead scheduling plan data, the historical day-ahead power prediction data corresponding to the historical day-ahead scheduling plan data and the historical new energy consumption proportion contribution score;
and the model training module is used for training a preset deep neural network by adopting the data set to obtain a new energy consumption proportion contribution assessment model for outputting new energy consumption proportion contribution scores of the daily scheduling plan data according to the daily scheduling plan data of the comprehensive energy base and the corresponding daily power prediction data.
8. The utility model provides a new energy consumption proportion contribution evaluation device of comprehensive energy base which characterized in that includes:
the data acquisition module is used for acquiring front scheduling plan data and corresponding daily front power prediction data of the comprehensive energy base;
the model prediction module is used for inputting the daily scheduling plan data and the corresponding daily power prediction data into a new energy consumption proportion contribution evaluation model so that the new energy consumption proportion contribution evaluation model outputs a new energy consumption proportion contribution score corresponding to the daily scheduling plan data;
the new energy consumption proportion contribution evaluation model is obtained by training in advance based on the new energy consumption proportion contribution evaluation training method according to any one of claims 1 to 5.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the new energy consumption proportional contribution assessment training method according to any one of claims 1 to 5 or implements the new energy consumption proportional contribution assessment method of an integrated energy base according to claim 6 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the new energy consumption proportion contribution estimation training method according to any one of claims 1 to 5, or implements the new energy consumption proportion contribution estimation method of an integrated energy base according to claim 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211469275.0A CN116029562A (en) | 2022-11-22 | 2022-11-22 | New energy consumption proportion contribution evaluation model training method, evaluation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211469275.0A CN116029562A (en) | 2022-11-22 | 2022-11-22 | New energy consumption proportion contribution evaluation model training method, evaluation method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116029562A true CN116029562A (en) | 2023-04-28 |
Family
ID=86071331
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211469275.0A Pending CN116029562A (en) | 2022-11-22 | 2022-11-22 | New energy consumption proportion contribution evaluation model training method, evaluation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116029562A (en) |
-
2022
- 2022-11-22 CN CN202211469275.0A patent/CN116029562A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10951500B2 (en) | Method and apparatus for warning | |
| CN107871164B (en) | Fog computing environment personalized deep learning method | |
| CN103595568B (en) | A kind of the Internet based on LS-SVM real-Time Signal Transfer method | |
| CN108171259A (en) | A kind of wisdom tip water quality monitoring system and method based on NB-IoT | |
| CN102316496A (en) | Data merging method based on Kalman filtering in wireless sensor network | |
| CN106452919A (en) | Fog node optimization method based on fussy theory | |
| Zhao et al. | An edge streaming data processing framework for autonomous driving | |
| CN115603464B (en) | Intelligent generation management system for power grid operation ticket based on digital twin | |
| CN115366157A (en) | Industrial robot maintenance method and device | |
| CN116258192A (en) | Day-ahead scheduling plan generation model training method, plan generation method and device | |
| CN112987617A (en) | Near-zero energy consumption building digital management system and energy efficiency monitoring method | |
| CN106549812A (en) | A kind of data center apparatus management-control method and system based on software definition | |
| CN116029562A (en) | New energy consumption proportion contribution evaluation model training method, evaluation method and device | |
| CN109389865A (en) | Link flight number transposing systems flight Support Resource dispatching management information system and method | |
| CN116258233A (en) | Day-ahead scheduling plan deviation degree estimation model training method, estimation method and device | |
| CN116435989A (en) | Novel method, device and system for predicting Internet of things state of power system equipment | |
| Tehrani et al. | Baseline load forecasting using a Bayesian approach | |
| CN112596457B (en) | Intelligent control method and system for kitchen disposal, collection and transportation | |
| CN111857015B (en) | Power transmission and transformation cloud intelligent controller | |
| CN116258232A (en) | Day-ahead scheduling plan optimization method and device for integrated energy base | |
| CN114969010A (en) | Power distribution network monitoring processing method and device | |
| US11025058B2 (en) | Tolerance-based intelligent edge network energy delivery | |
| CN113610358A (en) | Remote water quality risk early warning system based on cloud platform | |
| Wang et al. | On-line traffic forecasting of mobile communication system | |
| CN111897655B (en) | Power transmission and transformation high in clouds intelligent control ware |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |