CN110766225A - Neural network-based method and device for predicting daily transaction income of electric power - Google Patents
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Abstract
The invention discloses a neural network-based power transaction income prediction method, which comprises the following steps: acquiring historical income data and corresponding historical influence data of the power transaction; determining dependent variables and independent variables in the model; training a function relation between the dependent variable and the independent variable through a preset algorithm according to the historical income data and the historical influence data to obtain a prediction model; and inputting the influence data of any day to be predicted into the prediction model to obtain the predicted value of the electric power transaction income of the day. The relation between the electricity selling income and the influence factors is fitted through an algorithm, a prediction model is trained, based on the relation, the electricity selling income of a certain day in the future is predicted, an analysis data base is provided for an electricity selling company, and the electricity selling income is improved.
Description
Technical Field
The invention relates to the technical field of intelligent energy, in particular to a method and a device for predicting the daily trading income of electric power based on a neural network.
Background
In the domestic electric power trading market, the electric power trading market can be divided into medium and long term electric power trading and spot electric power trading. The spot power transaction is divided into power day-ahead transaction and power real-time transaction. The power day ahead transaction is the declaration of the amount of power required per hour for 24 hours on day D-1 (and possibly an earlier day). And in the D day, the electric power transaction system can clear and count according to the reporting condition, and the electricity selling company can be rewarded and punished by the reporting quantity of the electricity selling company. The income after the electricity selling company declares the electricity quantity is firstly related to the condition of the electricity market, such as the actual electricity consumption, the electricity price and the like of the users of the agents of the electricity selling company. The influence factors are also related to weather, economy, local population and the like, so that the physical mechanism for obtaining the declaration quantity and the income of the power selling company is complex, and the explicit functional relationship is extremely difficult to obtain, so that the functional relationship between the electric quantity and the income is difficult to obtain, and the income is difficult to predict.
Disclosure of Invention
The invention provides a neural network-based method and device for predicting the daily transaction income of electric power, which are used for fitting the relationship between the electric power selling income and the influence factors thereof and predicting the electric power selling income.
In a first aspect, the invention provides a neural network-based method for predicting the return of a day-ahead transaction of electric power, which comprises the following steps:
acquiring historical income data and corresponding historical influence data of the power transaction;
determining dependent variables and independent variables in the model;
training a function relation between the dependent variable and the independent variable through a preset algorithm according to the historical income data and the historical influence data to obtain a prediction model;
and inputting the influence data of any day to be predicted into the prediction model to obtain the predicted value of the electric power transaction income of the day.
In a second aspect, the present invention provides a neural network-based electric power day-ahead transaction profit prediction apparatus, including:
the data acquisition module is used for acquiring historical income data and corresponding historical influence data of the power transaction;
the determining module is used for determining a dependent variable and an independent variable in the model;
the training module is used for training the functional relation between the dependent variable and the independent variable through a preset algorithm according to the historical income data and the historical influence data so as to obtain a prediction model;
and the prediction module is used for inputting the influence data of any day to be predicted into the prediction model so as to obtain the predicted value of the electric power trading income of the day.
The invention provides a neural network-based electric power day-ahead transaction income prediction method and device.
Drawings
In order to more clearly illustrate the embodiments or prior art solutions in the present specification, the drawings needed to be used in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and it is obvious for a person skilled in the art to obtain other drawings based on these drawings without any creative effort.
Fig. 1 is a schematic flowchart of a method for predicting the future trade income of electric power based on a neural network according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an electric power day-ahead transaction profit prediction apparatus based on a neural network according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.
Fig. 1 is a schematic flow chart of a method for predicting the future trade income of electric power based on a neural network according to an embodiment of the present invention.
As shown in fig. 1, a method for predicting a future trade profit of electric power based on a neural network according to an embodiment of the present invention may include the following steps:
Historical revenue data for a power transaction is for example revenue data for a power selling company that has historically declared the amount of power sold,
the historical influence data is, for example, at least one of historical power data, historical revenue data, historical weather data of the power selling area, economic data of the power selling area, and historical electricity price.
The historical impact data may include weather data (e.g., temperature, humidity, overcast and overcast), historical economic data within the utility area (such as population of the site, GDP data, average income, etc.), historical time within the utility area, data related to other utility in the electricity market (e.g., amount of winning bid, number of utility, size of utility users, etc.), historical electricity prices in the electricity trading market.
The historical profit data may be profit data of the electricity sold for a certain day or profit data of the electricity sold for a certain month. The historical impact data must correspond to the revenue data. For example, revenue data of the sales electricity amount for D days, and the corresponding historical influence data is influence data of D days, such as weather data, historical economic data, and the like of D days.
At step 102, dependent variables and independent variables in the model are determined.
In the invention, the dependent variable is predicted income data of power sale in D days, and the independent variable is all influence data in D days.
Y is revenue data, more specifically, when element Y (0) of Y is revenue data for d days, then the argument X (0) corresponding to this Y (0) may be, but is not limited to, the following:
(a) data to be declared on day d (which may be a vector, e.g., a 24-data vector, representing 24-hour declaration data), and earlier declaration data such as d-1 and d-2 may be considered as appropriate;
(b) day d, earlier weather data such as day d-1, day d-2, etc. may also be considered as appropriate;
(c) d days of economic data (the same group can be used for economic data in the same month), and average economic data of the last month or earlier can be considered as appropriate;
(d) data relating to the electricity market on day d, such as the number of electricity vendors participating in the transaction on day d and the total electricity consumption of customers of their agents, and earlier data on days d-1, d-2, etc. may also be considered as appropriate;
(e) the predicted electricity rate for d-day or the electricity rate for d-1 day, and earlier electricity rate data such as d-2 may be considered as appropriate.
The data of the above-mentioned influencing factors are combined into a total vector or array, which is the influencing factor of the profit data Y0 (dependent variable) and is denoted as X0 (independent variable).
Wherein Y ═ { Y (0), Y (1), Y (2), …, Y (n) }, where each Y (i) is revenue data for a certain day, X ═ { X (0), X (1), X (2), …, X (n) }, where X (i) is the influence factor of Y (i) determined according to (2).
And 103, training a function relation between the dependent variable and the independent variable through a preset algorithm according to the historical income data and the historical influence data to obtain a prediction model.
The preset algorithm comprises any one of a neural network algorithm, a regression tree algorithm, a multiple linear regression algorithm and a support vector regression algorithm.
Neural network algorithms are preferred in the present invention. Neural networks are a common function simulation algorithm. As long as enough data is available, the method can fit many complex continuous functions, especially when the relation of the functions is complex and ambiguous, the functional relation between independent variables and dependent variables can be conveniently obtained by using a neural network, and the obtained model is a black box model. In the problem studied by the invention, the income after the electricity selling company declares the electricity quantity is related to the condition of the electricity market, for example, the actual electricity consumption, the electricity price and the like of the user of the electricity selling company agent are related, and the influence factors are also related to the weather condition, the economic condition, the population of the location and the like, so the physical mechanism for obtaining the declared quantity and the income of the electricity selling company is very complex, the obtaining of the explicit functional relationship is extremely difficult, and the data-driven algorithm is more efficient. Illustratively, a neural network with a certain structure is selected, such as the number of layers of the neural network, the number of neurons in each layer and the like; the number of layers and the number of neurons in each layer can also be used as a hyper-parameter to determine the group with the best effect through cross validation
And 104, inputting the influence data of any day to be predicted into a prediction model to obtain the predicted value of the electric power transaction income of the day.
In the present application, when it is necessary to examine how a certain set of revenue Y is reported in a future day, an influence factor X influencing Y is first constructed according to step 102, and it is necessary to pay attention to some data to be predicted, such as weather data. Then, X is input into a prediction model (neural network prediction model), and the prediction model outputs a predicted value of Y.
In the embodiment of the present invention, the following steps (not shown in the drawings) may be further included: and dividing the historical income data and the corresponding historical influence data into training set data and verification set data. Further, the aforementioned step 103 may include the following steps (not shown in the figure): A. training the functional relation between the dependent variable and the independent variable by utilizing the training set data through a preset algorithm; B. verifying the functional relation between the dependent variable and the independent variable by using the verification set data to determine whether a preset condition is met; C. and if the preset condition is met, determining the functional relation between the dependent variable and the independent variable meeting the preset condition as the prediction model.
Further, the following steps (not shown in the figure) can be included: and selecting 80% of the historical income data and the corresponding historical influence data as the training set data, and determining other historical income data and the corresponding historical influence data which are determined to be outside the training set data as verification set data. By way of example: the resulting data set { X, Y } (historical revenue data and historical impact data) is divided into a training set and a validation set (e.g., 80% of the data is the training set and 20% of the data is the validation set.
The invention provides a neural network-based electric power day-ahead transaction income prediction method, which is characterized in that the relation between electric power selling income and influence factors is fitted through an algorithm, a prediction model is trained, and based on the fitting, the electric power selling income on a certain day in the future is predicted, so that an analysis data base is provided for an electric power selling company, and the electric power selling income is improved.
Fig. 2 is a schematic structural diagram of an electric power day-ahead transaction profit prediction apparatus based on a neural network according to an embodiment of the present invention.
As shown in fig. 2, a neural network-based power day-ahead transaction profit prediction apparatus may include:
and the data acquisition module 21 is configured to acquire historical profit data and corresponding historical influence data of the power transaction.
And the determining module 22 is used for determining the dependent variable and the independent variable in the model.
And the training module 23 is configured to train a functional relationship between the dependent variable and the independent variable through a preset algorithm according to the historical revenue data and the historical influence data to obtain a prediction model.
And the prediction module 24 is used for inputting the influence data of any day to be predicted into the prediction model so as to obtain the predicted value of the electric power trading income of the day.
Further, the following modules (not shown in the figure) may also be included: and the dividing module is used for dividing the historical income data and the corresponding historical influence data into training set data and verification set data. Further, the training module 23 may include: a training unit (not shown in the figure) for training the functional relationship between the dependent variable and the independent variable through a preset algorithm by using the training set data; a verification unit (not shown in the figure) for verifying the functional relationship between the dependent variable and the independent variable by using the verification set data to determine whether a preset condition is satisfied; a determining unit (not shown in the figure), configured to determine, if the preset condition is satisfied, a functional relationship between the dependent variable and the independent variable that satisfies the preset condition as the prediction model.
According to an embodiment of the present invention, the apparatus of the present invention may further include: a selecting module (not shown in the figure) may be configured to select 80% of the historical profit data and the corresponding historical influence data as the training set data, and determine other historical profit data and corresponding historical influence data determined to be outside the training set data as validation set data.
The preset algorithm in the invention comprises any one of a neural network algorithm, a regression tree algorithm, a multiple linear regression algorithm and a support vector regression algorithm.
The invention provides a neural network-based electric power day-ahead transaction income prediction device, which is used for fitting the relation between electric power selling income and influence factors through an algorithm, training a prediction model, predicting the electric power selling income of a certain day in the future based on the relation, providing an analysis data base for an electric power selling company, and improving the electric power selling income.
An embodiment of the invention also provides electronic equipment. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
In a possible implementation manner, the processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program, and the corresponding computer program can also be acquired from other equipment, so that a neural network-based power day-ahead transaction income prediction method is formed on a logic level. And the processor executes the program stored in the memory so as to realize the neural network-based electric power day-ahead transaction income prediction method provided by any embodiment of the invention through the executed program.
The method executed by the neural network-based electric power day-ahead transaction profit prediction method according to the embodiment shown in fig. 1 in the present specification can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of a method disclosed in connection with the embodiments of the present specification may be embodied directly in a hardware decoding processor, or in a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The present specification further proposes a computer-readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by an electronic device including a plurality of application programs, enable the electronic device to perform a neural network-based electric power day-ahead transaction revenue prediction method provided in any embodiment of the present invention, and in particular to perform the method shown in fig. 1.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units or modules by function, respectively. Of course, the functionality of the various elements or modules may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (10)
1. A neural network-based power transaction revenue prediction method, the method comprising:
acquiring historical income data and corresponding historical influence data of the power transaction;
determining dependent variables and independent variables in the model;
training a function relation between the dependent variable and the independent variable through a preset algorithm according to the historical income data and the historical influence data to obtain a prediction model;
and inputting the influence data of any day to be predicted into the prediction model to obtain the predicted value of the electric power transaction income of the day.
2. The method of claim 1, wherein the impact data comprises at least one of historical power data, historical revenue data, historical weather data for a power sales area, economic data for a power sales area, and historical electricity prices.
3. The method of claim 1, further comprising:
dividing the historical income data and the corresponding historical influence data into training set data and verification set data;
the training of the functional relationship between the dependent variable and the independent variable through a preset algorithm according to the historical income data and the historical influence data to obtain a prediction model comprises the following steps:
training the functional relation between the dependent variable and the independent variable by utilizing the training set data through a preset algorithm;
verifying the functional relation between the dependent variable and the independent variable by using the verification set data to determine whether a preset condition is met;
and if the preset condition is met, determining the functional relation between the dependent variable and the independent variable meeting the preset condition as the prediction model.
4. The method of claim 3, further comprising:
and selecting 80% of the historical income data and the corresponding historical influence data as the training set data, and determining other historical income data and the corresponding historical influence data which are determined to be outside the training set data as verification set data.
5. The method according to any one of claims 1 to 4, wherein the predetermined algorithm comprises any one of a neural network algorithm, a regression tree algorithm, a multiple linear regression algorithm, and a support vector regression algorithm.
6. An electric power trading profit prediction apparatus based on a neural network, the apparatus comprising:
the data acquisition module is used for acquiring historical income data and corresponding historical influence data of the power transaction;
the determining module is used for determining a dependent variable and an independent variable in the model;
the training module is used for training the functional relation between the dependent variable and the independent variable through a preset algorithm according to the historical income data and the historical influence data so as to obtain a prediction model;
and the prediction module is used for inputting the influence data of any day to be predicted into the prediction model so as to obtain the predicted value of the electric power trading income of the day.
7. The apparatus of claim 6, wherein the impact data comprises at least one of historical power data, historical revenue data, historical weather data for a power sales area, economic data for a power sales area, historical electricity prices.
8. The apparatus of claim 6, further comprising:
the dividing module is used for dividing the historical income data and the corresponding historical influence data into training set data and verification set data;
the training module comprises:
the training unit is used for training the functional relation between the dependent variable and the independent variable through a preset algorithm by utilizing the training set data;
the verification unit is used for verifying the functional relation between the dependent variable and the independent variable by utilizing the verification set data so as to determine whether a preset condition is met;
and the determining unit is used for determining the functional relation between the dependent variable and the independent variable meeting the preset condition as the prediction model if the preset condition is met.
9. The apparatus of claim 8, further comprising:
and the selection module is used for selecting 80% of the historical income data and the corresponding historical influence data as the training set data, and determining other historical income data and the corresponding historical influence data which are determined to be outside the training set data as verification set data.
10. The apparatus of any one of claims 6-9, wherein the predetermined algorithm comprises any one of a neural network algorithm, a regression tree algorithm, a multiple linear regression algorithm, and a support vector regression algorithm.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111461397A (en) * | 2020-02-26 | 2020-07-28 | 山东浪潮通软信息科技有限公司 | Budget prediction method, equipment and medium based on improved support vector regression |
| CN112418974A (en) * | 2020-10-27 | 2021-02-26 | 北京思特奇信息技术股份有限公司 | Method and system for building block type telecommunication product and electronic equipment |
| CN112702342A (en) * | 2020-12-22 | 2021-04-23 | 北京天融信网络安全技术有限公司 | Network event processing method and device, electronic equipment and readable storage medium |
| CN113627511A (en) * | 2021-08-04 | 2021-11-09 | 中国科学院科技战略咨询研究院 | Model training method and influence monitoring method for influence of climate change on traffic industry |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111461397A (en) * | 2020-02-26 | 2020-07-28 | 山东浪潮通软信息科技有限公司 | Budget prediction method, equipment and medium based on improved support vector regression |
| CN112418974A (en) * | 2020-10-27 | 2021-02-26 | 北京思特奇信息技术股份有限公司 | Method and system for building block type telecommunication product and electronic equipment |
| CN112702342A (en) * | 2020-12-22 | 2021-04-23 | 北京天融信网络安全技术有限公司 | Network event processing method and device, electronic equipment and readable storage medium |
| CN112702342B (en) * | 2020-12-22 | 2022-12-13 | 北京天融信网络安全技术有限公司 | Network event processing method and device, electronic equipment and readable storage medium |
| CN113627511A (en) * | 2021-08-04 | 2021-11-09 | 中国科学院科技战略咨询研究院 | Model training method and influence monitoring method for influence of climate change on traffic industry |
| CN113627511B (en) * | 2021-08-04 | 2024-02-02 | 中国科学院科技战略咨询研究院 | Model training method for influence of climate change on traffic and influence monitoring method |
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