CN112053006A - Migration learning-based optimization time acceleration method and system for combined cooling heating and power system - Google Patents
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Abstract
The invention discloses a migration learning-based optimization time acceleration method and system for a combined cooling heating and power system, wherein the method comprises the following steps: receiving cooling, heating and power load sample data corresponding to the optimization history and the optimization target of the combined cooling, heating and power system, and respectively recording the sample data as a source domain and a target domain; clustering sample data in a source domain and a target domain respectively to obtain a plurality of target domain sample classes and a plurality of source domain sample classes; comparing the target domain sample class with the source domain sample class based on the maximum mean difference, and judging whether a matched source domain sample class data set exists or not; if the initial population of the genetic algorithm exists, determining the initial population of the genetic algorithm by adopting transfer learning based on the source domain sample class data set; if not, randomly generating an initial population of the genetic algorithm; and optimizing the combined cooling heating and power system based on a genetic algorithm. Based on the similar historical optimization problem, the invention determines the initial population of the genetic algorithm from the historical data through transfer learning, defines the possible areas of the optimization solution and realizes the acceleration of the optimization of the combined cooling heating and power system.
Description
Technical Field
The invention belongs to the technical field of optimization of combined cooling heating and power systems, and particularly relates to a method and a system for accelerating optimization time of a combined cooling heating and power system based on transfer learning.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The combined cooling, heating and power system (CCHP) realizes the cascade utilization of energy while meeting various load requirements of users on cooling, heating and electricity through the optimized scheduling of energy, and has become one of the important directions of distributed energy development due to its high energy utilization efficiency, flexibly adjustable energy supply scheme and economic and low-carbon benefits. The optimization method for the combined cooling heating and power system developed more perfectly at the present stage is day-ahead optimization, namely an optimization model for realizing optimization targets based on various relevant data predicted day-ahead in work, such as user cold load, heat load, electric load and weather data. The model determines the optimal working plan of each main device in the new energy combined cooling heating and power system by taking hours as units.
Most of calculation methods used in the day-ahead optimization are genetic algorithms, and the basic frameworks of the genetic algorithms are three, namely, the genetic algorithms are selected for coding, fitness function and initial population. However, since genetic algorithms involve a large number of individual calculations, the calculation time will rise exponentially when the problem is complex. According to the knowledge of the inventor, when the combined cooling heating and power system is optimized by using a genetic algorithm in the existing literature, a method for randomly generating an initial population is mostly adopted, so that the system optimization time is long, and a heavy calculation burden is brought to the system.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method and a system for accelerating the optimization time of a combined cooling heating and power system based on transfer learning.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
a combined cooling heating and power system optimization time acceleration method based on transfer learning comprises the following steps:
receiving cooling, heating and power load sample data corresponding to the optimization history and the optimization target of the combined cooling, heating and power system, and respectively recording the sample data as a source domain and a target domain;
clustering sample data in a source domain and a target domain respectively to obtain a plurality of target domain sample classes and a plurality of source domain sample classes;
comparing the target domain sample class with the source domain sample class based on the maximum mean difference, and judging whether a matched source domain sample class data set exists or not;
if the initial population of the genetic algorithm exists, determining the initial population of the genetic algorithm by adopting transfer learning based on the source domain sample class data set; if not, randomly generating an initial population of the genetic algorithm;
and optimizing the combined cooling heating and power system based on a genetic algorithm.
One or more embodiments provide a combined cooling, heating and power system optimization time acceleration system based on transfer learning, including:
a data acquisition module configured to: receiving cooling, heating and power load sample data corresponding to the optimization history and the optimization target of the combined cooling, heating and power system, and respectively recording the sample data as a source domain and a target domain;
a data clustering module configured to: clustering sample data in a source domain and a target domain respectively to obtain a plurality of target domain sample classes and a plurality of source domain sample classes;
an initial population determination module configured to: comparing the target domain sample class with the source domain sample class based on the maximum mean difference, and judging whether a matched source domain sample class data set exists or not; if the initial population of the genetic algorithm exists, determining the initial population of the genetic algorithm by adopting transfer learning based on the source domain sample class data set; if not, randomly generating an initial population of the genetic algorithm;
a system optimization module configured to: and optimizing the combined cooling heating and power system based on a genetic algorithm.
One or more embodiments provide an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the migration learning-based optimization time acceleration method for a combined cooling, heating, and power system.
One or more embodiments provide a computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the optimization time acceleration method for a combined cooling, heating and power system based on transfer learning.
The above one or more technical solutions have the following beneficial effects:
based on the cooling, heating and power load sample data corresponding to the optimization problem, similar historical cooling, heating and power load sample data is searched by adopting the maximum mean difference, namely, a data set corresponding to the historical optimization problem similar to the current optimization problem is searched, and the initial population of the genetic algorithm is determined by transfer learning, so that the possible area of the optimized solution of the genetic algorithm is defined, the optimization acceleration of the cooling, heating and power combined supply system is realized, and the operation burden of the system is reduced.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of a migration learning-based optimization time acceleration method for a combined cooling, heating and power system in an embodiment of the present invention.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
In order to solve the defects of the conventional genetic algorithm for optimizing the CCHP system, the embodiment discloses a migration learning-based optimization time acceleration method for a combined cooling heating and power system, which comprises the following steps:
step 1: receiving cold load, heat load and electric load prediction data of a corresponding target optimized by a combined cooling heating and power system, and loading the data into a target domain; and receiving corresponding historical cold load, heat load and electric load data optimized by the combined cooling heating and power system, and loading the historical cold load, heat load and electric load data into a model library, which is also called a source domain.
The target cold load, heat load and electric load prediction data are obtained by the combined cold-heat-electricity supply system based on the problem to be optimized according to the optimization target.
Step 2: and clustering the sample data in the source domain and the target domain respectively to obtain a plurality of target domain sample classes and a plurality of source domain sample classes.
For the source domain or target domain samples generated by the evolved population, because the optimized problem usually includes several peaks, all the samples in the source domain or target domain are regarded as the whole subject to the same distribution, and the MMD is used to judge the similarity of the source domain and target domain problems, so that it is difficult to accurately evaluate the similarity. In view of this, the K-means algorithm is first adopted to cluster the samples in the source domain and the target domain, respectively, and it is assumed that the samples in each of the clustered classes obey the same distribution (assuming that there are j sample classes after clustering). Specifically, the cold, hot and electric load data corresponding to each sample are taken as multidimensional data, and the distance between the samples is calculated by the Euclidean distance.
The present embodiment performs clustering using a K-means clustering algorithm. The K-means algorithm is derived from a vector quantization method in signal processing, and is now popular in the field of data mining as a cluster analysis method. The purpose of the K-means clustering is: dividing n points into k clusters, so that each point belongs to the cluster corresponding to the mean value (namely the cluster center) nearest to the point, and taking the cluster as the standard of clustering.
And step 3: and comparing the target domain sample class with the source domain sample class based on the maximum mean difference, judging whether a matched source domain sample class data set exists, if so, executing the step 4, otherwise, randomly generating an initial population of the genetic algorithm.
The Maximum Mean Difference (MMD) is an index used to determine whether two data distributions are the same, and was originally used mainly for the detection problem of double samples. The basic principle of MMD is: suppose that one satisfies Q1Distributed source domain data set
And one satisfies Q2Distributed target domain data set
Let H be the regenerated Hilbert space (RKHS) and there is a mapping function from the original space to the Hilbert space
Then, when n and m tend to infinity, XsAnd XtThe maximum mean difference over RKHS can be expressed as:
the step 3 specifically includes:
step 3.1: for each target domain sample class, determining a source domain sample class with the minimum difference based on the maximum mean difference, and recording as a matched sample class;
specifically, a model library sample class is used as a source domain data set, a problem sample class to be optimized is used as a target domain data set, and specifically, for each sample class in a target domain, the maximum mean difference f (X) between the sample class and all historical model sample classes in the model library is calculated respectivelys，Xt). In this embodiment, for j sample classes in the target domain, first, the method startsAnd (5) calculating the average value of the maximum average difference between the sample class and the kth historical model sample class in the model base, and recording the average value as Avek(ii) a Then, let k be k +1 until the maximum average difference Ave between the sample class and all historical models in the model base is obtainedkAnd k is 1,2,3 and …, and the historical model sample class with the minimum Ave value is taken as a matching sample class of the sample class.
Based on the method, the corresponding matching sample class of each target domain sample class is obtained.
Step 3.2: and calculating the average value of the maximum average value difference between the target domain sample class and the corresponding matched sample class thereof, and if the average value is smaller than a set threshold value, determining that a matched source domain sample class data set exists, namely a sample data set corresponding to the matched sample class corresponding to each target domain sample class.
And 4, step 4: an initial population of genetic algorithms is determined by migration learning.
And performing transfer learning, and determining an initial population of the problem to be optimized through the matched source domain sample class data set, namely randomly extracting part of individuals from the matched source domain sample class data set to form the initial population of the genetic algorithm.
And 5: and (4) carrying out day-ahead optimization on the CCHP system by using a genetic algorithm, and updating the model library.
And selecting the daily saving rate of the operation cost, the daily saving rate of the primary energy and the CO2 daily emission reduction rate of the new energy combined cooling heating and power system as the optimal scheduling target, and performing day-ahead optimal scheduling on the output plan of each device of the system by using a genetic algorithm.
And simultaneously, adding the cold load, heat load and electric load sample data corresponding to the optimization result into the model library, namely updating the model library.
Example two
The objective of this embodiment is to provide a combined cooling heating and power system optimization time acceleration system based on transfer learning, including:
a data acquisition module configured to: receiving cooling, heating and power load sample data corresponding to the optimization history and the optimization target of the combined cooling, heating and power system, and respectively recording the sample data as a source domain and a target domain;
a data clustering module configured to: clustering sample data in a source domain and a target domain respectively to obtain a plurality of target domain sample classes and a plurality of source domain sample classes;
an initial population determination module configured to: comparing the target domain sample class with the source domain sample class based on the maximum mean difference, and judging whether a matched source domain sample class data set exists or not; if the initial population of the genetic algorithm exists, determining the initial population of the genetic algorithm by adopting transfer learning based on the source domain sample class data set; if not, randomly generating an initial population of the genetic algorithm;
a system optimization module configured to: and optimizing the combined cooling heating and power system based on a genetic algorithm.
EXAMPLE III
The embodiment aims at providing an electronic device.
An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing time and accelerating time of a combined cooling, heating and power system based on transfer learning according to an embodiment.
Example four
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the optimization time acceleration method for a combined cooling, heating and power system based on transfer learning according to an embodiment.
The steps involved in the apparatuses of the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
By adopting the technical scheme in one or more embodiments, the problem of overlong calculation time caused by the fact that a new energy combined cooling heating and power system is optimized in the day-ahead mode by independently using a genetic algorithm can be solved, the idea of transfer learning is integrated into the genetic algorithm, and valuable historical information is extracted from the solved similar historical problems and is used for guiding the optimization and the solution of the new problems.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A combined cooling heating and power system optimization time acceleration method based on transfer learning is characterized by comprising the following steps:
receiving cooling, heating and power load sample data corresponding to the optimization history and the optimization target of the combined cooling, heating and power system, and respectively recording the sample data as a source domain and a target domain;
clustering sample data in a source domain and a target domain respectively to obtain a plurality of target domain sample classes and a plurality of source domain sample classes;
comparing the target domain sample class with the source domain sample class based on the maximum mean difference, and judging whether a matched source domain sample class data set exists or not;
if the initial population of the genetic algorithm exists, determining the initial population of the genetic algorithm by adopting transfer learning based on the source domain sample class data set; if not, randomly generating an initial population of the genetic algorithm;
and optimizing the combined cooling heating and power system based on a genetic algorithm.
2. The migration learning-based combined cooling, heating and power system optimization time acceleration method as claimed in claim 1, wherein the clustering adopts a K-Means clustering method.
3. The migration learning-based cooling, heating and power combined system optimization time acceleration method according to claim 1, wherein the determining whether there is a matched source domain sample class data set comprises:
for each target domain sample class, determining a source domain sample class with the minimum difference based on the maximum mean difference, and recording as a matched sample class;
and judging whether a matched source domain sample class data set exists according to the maximum mean difference value between the target domain sample class and the matched sample class.
4. The migration learning-based cooling, heating and power combined supply system optimization time acceleration method according to claim 3, wherein an average value of the maximum mean difference between the target domain sample class and its corresponding matching sample class is calculated, and if the average value is smaller than a set threshold, it is determined that there exists a matching source domain sample class data set, that is, a sample data set corresponding to the corresponding matching sample class of each target domain sample class.
5. The method for accelerating the optimization time of the combined cooling heating and power system based on the transfer learning of claim 1, wherein the step of determining the initial population of the genetic algorithm by the transfer learning comprises the following steps: and randomly extracting a part of individuals from the matched source domain sample class data set to form an initial population of the genetic algorithm.
6. The migration learning-based combined cooling heating and power system optimization time acceleration method according to claim 1, wherein the combined cooling and power system optimization based on the genetic algorithm includes:
and selecting the daily saving rate of the operation cost, the daily saving rate of the primary energy and the CO2 daily emission reduction rate of the new energy combined cooling heating and power system as the optimal scheduling target, and performing day-ahead optimal scheduling on the output plan of each device of the system by using a genetic algorithm.
7. The method for accelerating the optimization time of the combined cooling heating and power system based on the transfer learning of claim 1, wherein when the combined cooling heating and power system is optimized based on the genetic algorithm, the obtained sample data of the cooling load, the heating load and the electric load are added into a source domain.
8. A combined cooling heating and power system optimization time acceleration system based on transfer learning is characterized by comprising:
a data acquisition module configured to: receiving cooling, heating and power load sample data corresponding to the optimization history and the optimization target of the combined cooling, heating and power system, and respectively recording the sample data as a source domain and a target domain;
a data clustering module configured to: clustering sample data in a source domain and a target domain respectively to obtain a plurality of target domain sample classes and a plurality of source domain sample classes;
an initial population determination module configured to: comparing the target domain sample class with the source domain sample class based on the maximum mean difference, and judging whether a matched source domain sample class data set exists or not; if the initial population of the genetic algorithm exists, determining the initial population of the genetic algorithm by adopting transfer learning based on the source domain sample class data set; if not, randomly generating an initial population of the genetic algorithm;
a system optimization module configured to: and optimizing the combined cooling heating and power system based on a genetic algorithm.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for optimizing time acceleration of a combined cooling, heating and power system based on transfer learning according to any one of claims 1 to 7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the optimization time acceleration method for a combined cooling, heating and power system based on transfer learning according to any one of claims 1 to 7.
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