CN110276501A - The prediction technique and device of the electricity price of short term power trade market - Google Patents
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Abstract
The invention discloses the prediction technique of the electricity price of short term power trade market, device, computer readable storage medium and electronic equipments, method includes: the training dataset and forecast sample data set for obtaining short term power trade market, and the training dataset includes multiple history electricity price data and the corresponding multiple influence factor data of each history electricity price data；According to a variety of learning algorithms and the training dataset, Ji Yucemoxingji is determined, and determine the corresponding electricity price data set of the base prediction model collection；The training electricity price data set is to determine Price Forecasting；According to the forecast sample data set and the Price Forecasting forecasted electricity market price.According to the technical solution of the present invention, the electricity price of prediction short term power trade market that can be more accurate.
Description
Technical Field
The invention relates to the technical field of energy, in particular to a method and a device for predicting the electricity price of a short-term electricity trading market.
Background
The short-term electricity price prediction is one of electricity price predictions, and has a certain predictability.
At present, a sample data set in a short-term power trading market is mainly acquired, ensemble learning is performed according to the sample data set and the same learning algorithm, an electricity price prediction model is determined, and electricity prices are predicted through the electricity price prediction model.
However, there are many influencing factors influencing the electricity price, and each influencing factor has a different degree of influence on the electricity price, and a single learning algorithm may not accurately reflect the relation between the influencing factor and the electricity price, so that the electricity price of the short-term electricity trading market may not be accurately predicted by the above method.
Disclosure of Invention
The invention provides a method and a device for predicting the electricity price of a short-term electricity trading market, a computer readable storage medium and electronic equipment, which can more accurately predict the electricity price of the short-term electricity trading market.
In a first aspect, the present invention provides a method for predicting electricity prices of a short-term electricity trading market, comprising:
acquiring a training data set and a prediction sample data set of a short-term power trading market, wherein the training data set comprises historical power price data and influence factor data corresponding to the historical power price data;
determining a base prediction model set according to various learning algorithms and the training data set, and determining a power price data set corresponding to the base prediction model set;
training the electricity price data set to determine an electricity price prediction model;
and predicting the electricity price according to the prediction sample data set and the electricity price prediction model.
Preferably, the first and second electrodes are formed of a metal,
the determining a base prediction model set according to the multiple learning algorithms and the training data set, and determining a power price data set corresponding to the base prediction model set, includes:
dividing the training data set into a first training data set and a second training data set according to a preset hyper-parameter;
determining a base prediction model set according to the first training data set and various learning algorithms;
determining a prediction data set of the second training data set from the set of basis prediction models;
forming a power price data set using each of the historical power price data in the predictive data set and the second training data set.
Preferably, the first and second electrodes are formed of a metal,
the training the electricity price dataset to determine an electricity price prediction model, comprising:
and training the electricity price data set according to a random forest model, determining the trained random forest model, and determining the trained random forest model as an electricity price prediction model.
Preferably, the first and second electrodes are formed of a metal,
the predicting electricity prices according to the prediction sample data set and the electricity price prediction model comprises:
optimizing the hyper-parameters of the electricity price prediction model according to a preset test data set;
and predicting the electricity price according to the prediction sample data set, the electricity price prediction model and the optimized hyper-parameter of the electricity price prediction model.
In a second aspect, the present invention provides an apparatus for predicting electricity prices of a short-term electricity trading market, comprising:
the system comprises an acquisition module, a prediction module and a display module, wherein the acquisition module is used for acquiring a training data set and a prediction sample data set of a short-term power trading market, and the training data set comprises historical power price data and influence factor data corresponding to the historical power price data;
the data set determining module is used for determining a base prediction model set according to various learning algorithms and the training data set, and determining a power price data set corresponding to the base prediction model set;
a model determination module to train the electricity price dataset to determine an electricity price prediction model;
and the electricity price prediction module is used for predicting the electricity price according to the prediction sample data set and the electricity price prediction model.
Preferably, the first and second electrodes are formed of a metal,
the dataset determination module comprising: the device comprises a dividing unit, a model determining unit, a first data set determining unit and a second data set determining unit; wherein,
the dividing unit is used for dividing the training data set into a first training data set and a second training data set according to a preset hyper-parameter;
the model determining unit is used for determining a base prediction model set according to the first training data set and a plurality of learning algorithms;
the first data set determination unit is configured to determine a prediction data set of the second training data set according to the base prediction model set;
the second data set determination unit is configured to form a power rate data set by using each of the historical power rate data in the prediction data set and the second training data set.
Preferably, the first and second electrodes are formed of a metal,
and the model determining module is used for training the electricity price data set according to a random forest model, determining the trained random forest model and determining the trained random forest model as an electricity price prediction model.
Preferably, the first and second electrodes are formed of a metal,
the electricity price prediction module comprises: an optimization unit and a prediction unit; wherein,
the optimization unit is used for optimizing the hyper-parameters of the electricity price prediction model according to a preset test data set;
the prediction unit is used for predicting the electricity price according to the prediction sample data set, the electricity price prediction model and the optimized hyper-parameter of the electricity price prediction model.
In a third aspect, the invention provides a computer-readable storage medium comprising executable instructions which, when executed by a processor of an electronic device, cause the processor to perform the method according to any one of the first aspect.
In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.
The invention provides a method and a device for predicting the electricity price of a short-term power trading market, a computer readable storage medium and electronic equipment. In conclusion, the determined electricity price prediction model comprehensively considers various learning algorithms, the precision and the generalization capability of the electricity price prediction model are improved, and the electricity price of the short-term electricity trading market can be predicted more accurately.
Further effects of the above-mentioned unconventional preferred modes will be described below in conjunction with specific embodiments.
Drawings
In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a flowchart illustrating a method for predicting electricity prices of a short-term electricity trading market according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an apparatus for predicting electricity prices of a short-term electricity trading market according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of another short-term electricity market price forecasting device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a device for predicting electricity prices of a short-term electricity trading market according to another embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for predicting the electricity price of a short-term electricity trading market, including the following steps:
step 101, acquiring a training data set and a prediction sample data set of a short-term power trading market, wherein the training data set comprises a plurality of historical power prices and a plurality of influence factor data corresponding to each historical power price;
102, determining a base prediction model set according to various learning algorithms and the training data set, and determining a power price data set corresponding to the base prediction model set;
step 103, training the electricity price data set to determine an electricity price prediction model;
and 104, predicting the electricity price according to the prediction sample data set and the electricity price prediction model.
As shown in fig. 1, in the method, a training data set and a preset sample data set of a short-term power trading market are obtained, the training data set includes a plurality of historical power price data and a plurality of influence factor data corresponding to each historical power price data, then, a base prediction model set is determined according to a plurality of learning algorithms and the training data set, a power price data set corresponding to the base prediction model set is determined, a power price prediction model is determined by training the power price data set, and then, power prices can be predicted according to the prediction sample data set and the power price prediction model. In conclusion, the determined electricity price prediction model comprehensively considers various learning algorithms, the precision and the generalization capability of the electricity price prediction model are improved, and the electricity price of the short-term electricity trading market can be predicted more accurately.
Specifically, the short-term electricity trading market specifically refers to that electricity purchasing objects and electricity selling objects submit corresponding bid information before the week, day or hour of the electricity trading market, and then an operator of the electricity trading market determines a bargain electricity price by using a market clearing algorithm. The power price prediction of the short-term power trading market comprises week power price prediction, day-ahead power price prediction and hour-ahead power price prediction under the power trading market environment, wherein the day-ahead power price prediction is the most common prediction means for the short-term power trading market power price prediction.
Specifically, the influencing factor data influencing the historical electricity price data includes, but is not limited to, time of day, temperature value, humidity value, gas price data, holidays, historical load, and the like.
In an embodiment of the present invention, the determining a base prediction model set according to a plurality of learning algorithms and the training data set, and determining an electricity price data set corresponding to the base prediction model set include:
dividing the training data set into a first training data set and a second training data set according to a preset hyper-parameter;
determining a base prediction model set according to the first training data set and various learning algorithms;
determining a prediction data set of the second training data set from the set of basis prediction models;
forming a power price data set using each of the historical power price data in the predictive data set and the second training data set.
In this embodiment, the training data set is divided into a first training data set and a second training data set according to a preset hyper-parameter, for example, the number of data in the first training data set and the second training data set may be 4:1, where the learning frame is a Stacking learning frame and the preset hyper-parameter is 2 layers, then the first training data set is trained according to multiple learning algorithms to determine a base prediction model set, characteristics (influence factors) corresponding to each base prediction model in the base prediction model set may be different, the number of models in the base prediction model set is the hyper-parameter, and the number of base prediction models may be specifically determined by combining the data amount of the actual training data set and the change rule of electricity price; aiming at each group of influence factor data in the second training data set, namely determining a plurality of influence factor data corresponding to each historical electricity price data into a group of influence factor data, substituting the group of influence factor data into each base prediction model, respectively determining prediction data corresponding to each base prediction model, and then forming an electricity price data set by using the plurality of historical electricity price data in each prediction data and the second training data, namely the electricity price data set comprises real electricity price data and predicted electricity price data.
It should be noted that, in the process of determining the base prediction model, a selection may be performed from a plurality of learning algorithms, where the selection of the plurality of learning algorithms follows good and different principles, for example, a plurality of different model types (linear model and nonlinear model, deviation reduction model and variance reduction model, etc.) are selected, or different hyper-parameters of the same model are selected, or different random seeds are used to select the learning algorithms so that the learning algorithms have randomness, thereby ensuring the diversity of the learning algorithms of the base prediction model, and accordingly, the accuracy and generalization capability of the electricity price prediction model can be improved, where the plurality of learning algorithms may determine a plurality of learning algorithms by combining the data amount of the actual training data set and the change rule of the electricity price data. Learning algorithms include, but are not limited to, neural network algorithms, decision tree algorithms, and the like.
For example, taking one influencing factor data as an example, given a total number of samples N, the first training data set is X _ train _1 { (X)i，yi) I is 1, 2, …, m, and the second sample data set is X _ train _2 { (X)j，yj) J ═ m +1, m +2, …, N }, where x isiData characterizing the influencing factors, x, of the ith first training datajCharacterizing the influencing factor data, y, of the jth second training dataiCharacterizing the electric value, y, of the ith first training datajCharacterizing the power value of the jth second training data, then training n base models according to the first training data set X-train _1, and training the second training dataRespectively waiting for the influence factor data in the data set X _ train _2 to enter n basic models to obtain predicted values Zj，n，ZjnAnd substituting the j second training data into the predicted value of the n base model, and representing the predicted value by the following table:
the above prediction data and the electricity price data in X _ train _2 are combined together to form an electricity price data set { (Z)j，1，Zj，2，…，Zj，n，yj) And j is m +1, m +2, …, N, and the electricity price data set is trained by using a random forest or other machine learning algorithm to determine an electricity price prediction model, and a predicted value obtained by using the electricity price prediction model is used. Obviously, in the case that the above example is only one influencing factor data, in an actual business scenario, the influencing factor data may be multiple, for example, the influencing factor data may be time, temperature value, humidity value, gas price data, holidays and other influencing factor data that influence the predicted value.
In one embodiment of the present invention, the training of the electricity price data set to determine an electricity price prediction model includes:
and training the electricity price data set according to a random forest model, determining the trained random forest model, and determining the trained random forest model as an electricity price prediction model.
Specifically, the trained random forest model can be determined by training the electricity price data set by using the random forest model, and the trained random forest model is determined as the electricity price prediction model, so that the determined electricity price prediction model has higher precision and generalization capability. Of course, other models may be used to train the electricity price data set.
The integrated learning framework for determining the electricity price prediction model is a 2-layer Stacking learning framework based on a heterogeneous classifier, the first layer is a base prediction model set, and the second layer is a random forest model. The heterogeneous classifier can improve the precision and generalization capability of the electricity price prediction model.
In an embodiment of the present invention, the predicting electricity prices according to the prediction sample data set and the electricity price prediction model includes:
optimizing the hyper-parameters of the electricity price prediction model according to a preset test data set;
and predicting the electricity price according to the prediction sample data set, the electricity price prediction model and the optimized hyper-parameter of the electricity price prediction model.
In this embodiment, in order to further ensure the accuracy and generalization capability of the electricity price prediction model, the hyper-parameters of the electricity price prediction model need to be optimized by presetting the test data set, and then the electricity price can be predicted according to the prediction sample data set, the electricity price prediction model and the hyper-parameters of the optimized electricity price prediction model.
It should be noted that the test set can test the electricity price prediction model to evaluate the electricity price prediction model, and correspondingly, when the electricity price prediction model is not well represented in the test set, the test set can be used to optimize the electricity price test model so as to optimize the hyper-parameters of the electricity price test model.
Referring to fig. 2, based on the same concept as the method embodiment of the present invention, an embodiment of the present invention further provides a device for predicting electricity prices of a short-term electricity trading market, including:
the obtaining module 201 is configured to obtain a training data set and a prediction sample data set of a short-term power trading market, where the training data set includes a plurality of historical electricity price data and a plurality of influence factor data corresponding to each of the historical electricity price data;
a data set determining module 202, configured to determine a base prediction model set according to multiple learning algorithms and the training data set, and determine an electricity price data set corresponding to the base prediction model set;
a model determination module 203 for training the electricity price data set to determine an electricity price prediction model;
and the electricity price prediction module 204 is used for predicting the electricity price according to the prediction sample data set and the electricity price prediction model.
Referring to fig. 3, in an embodiment of the present invention, the data set determining module 202 includes: a dividing unit 2021, a model determining unit 2022, a first data set determining unit 2023, and a second data set determining unit 2024; wherein,
the dividing unit 2021 is configured to divide the training data set into a first training data set and a second training data set according to a preset hyper-parameter;
the model determining unit 2022 is configured to determine a base prediction model set according to the first training data set and a plurality of learning algorithms;
the first data set determining unit 2023, configured to determine a prediction data set of the second training data set according to the base prediction model set;
the second data set determining unit 2024 is configured to form a power rate data set by using each of the historical power rate data in the prediction data set and the second training data set.
In an embodiment of the present invention, the model determining module 203 is configured to train the electricity price data set according to a random forest model, determine a trained random forest model, and determine the trained random forest model as an electricity price prediction model.
Referring to fig. 4, in an embodiment of the invention, the electricity price predicting module 204 includes: optimization unit 2041 and prediction unit 2042; wherein,
the optimizing unit 2041 is configured to optimize the hyper-parameters of the electricity price prediction model according to a preset test data set;
the prediction unit 2042 is configured to predict the electricity price according to the prediction sample data set, the electricity price prediction model, and the super-parameter of the optimized electricity price prediction model.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device includes a processor 501 and a memory 502 storing execution instructions, and optionally includes an internal bus 503 and a network interface 504. The memory 502 may include a memory 5021, such as a Random-access memory (RAM), and may further include a non-volatile memory 5022(non-volatile memory), such as at least 1 disk memory; the processor 501, the network interface 504, and the memory 502 may be connected to each other by an internal bus 503, and the internal bus 503 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 internal bus 503 may be divided into an address bus, a data bus, a control bus, etc., and is indicated by only one double-headed arrow in fig. 5 for convenience of illustration, but does not indicate only one bus or one type of bus. Of course, the electronic device may also include hardware required for other services. When the processor 501 executes execution instructions stored by the memory 502, the processor 501 performs the method of any of the embodiments of the present invention and at least is used to perform the method as shown in fig. 1.
In a possible implementation manner, the processor reads corresponding execution instructions from the nonvolatile memory into the memory and then runs the corresponding execution instructions, and corresponding execution instructions can also be obtained from other equipment, so that a prediction device of the electricity price of the short-term electricity trading market is formed on a logic level. The processor executes the execution instructions stored in the memory to realize a method for predicting the electricity price of the short-term electricity trading market provided by any embodiment of the invention through the executed execution instructions.
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 invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Embodiments of the present invention further provide a computer-readable storage medium, which includes an execution instruction, and when a processor of an electronic device executes the execution instruction, the processor executes a method provided in any one of the embodiments of the present invention. The electronic device may specifically be the electronic device shown in fig. 5; the execution instruction is a computer program corresponding to a device for predicting the electricity price of the short-term electricity trading market.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
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 boiler 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 boiler. 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 boiler that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A method for predicting electricity prices in a short-term electricity trading market, comprising:
acquiring a training data set and a prediction sample data set of a short-term power trading market, wherein the training data set comprises historical power price data and influence factor data corresponding to the historical power price data;
determining a base prediction model set according to various learning algorithms and the training data set, and determining a power price data set corresponding to the base prediction model set;
training the electricity price data set to determine an electricity price prediction model;
and predicting the electricity price according to the prediction sample data set and the electricity price prediction model.
2. The method of claim 1,
the determining a base prediction model set according to the multiple learning algorithms and the training data set, and determining a power price data set corresponding to the base prediction model set, includes:
dividing the training data set into a first training data set and a second training data set according to a preset hyper-parameter;
determining a base prediction model set according to the first training data set and various learning algorithms;
determining a prediction data set of the second training data set from the set of basis prediction models;
forming a power price data set using each of the historical power price data in the predictive data set and the second training data set.
3. The method of claim 1,
the training the electricity price dataset to determine an electricity price prediction model, comprising:
and training the electricity price data set according to a random forest model, determining the trained random forest model, and determining the trained random forest model as an electricity price prediction model.
4. The method according to any one of claims 1 to 3,
the predicting electricity prices according to the prediction sample data set and the electricity price prediction model comprises:
optimizing the hyper-parameters of the electricity price prediction model according to a preset test data set;
and predicting the electricity price according to the prediction sample data set, the electricity price prediction model and the optimized hyper-parameter of the electricity price prediction model.
5. An apparatus for predicting electricity prices of a short-term electricity trading market, comprising:
the system comprises an acquisition module, a prediction module and a display module, wherein the acquisition module is used for acquiring a training data set and a prediction sample data set of a short-term power trading market, and the training data set comprises historical power price data and influence factor data corresponding to the historical power price data;
the data set determining module is used for determining a base prediction model set according to various learning algorithms and the training data set, and determining a power price data set corresponding to the base prediction model set;
a model determination module to train the electricity price dataset to determine an electricity price prediction model;
and the electricity price prediction module is used for predicting the electricity price according to the prediction sample data set and the electricity price prediction model.
6. The apparatus of claim 5,
the dataset determination module comprising: the device comprises a dividing unit, a model determining unit, a first data set determining unit and a second data set determining unit; wherein,
the dividing unit is used for dividing the training data set into a first training data set and a second training data set according to a preset hyper-parameter;
the model determining unit is used for determining a base prediction model set according to the first training data set and a plurality of learning algorithms;
the first data set determination unit is configured to determine a prediction data set of the second training data set according to the base prediction model set;
the second data set determination unit is configured to form a power rate data set by using each of the historical power rate data in the prediction data set and the second training data set.
7. The apparatus of claim 5,
and the model determining module is used for training the electricity price data set according to a random forest model, determining the trained random forest model and determining the trained random forest model as an electricity price prediction model.
8. The apparatus according to any one of claims 5 to 7,
the electricity price prediction module comprises: an optimization unit and a prediction unit; wherein,
the optimization unit is used for optimizing the hyper-parameters of the electricity price prediction model according to a preset test data set;
the prediction unit is used for predicting the electricity price according to the prediction sample data set, the electricity price prediction model and the optimized hyper-parameter of the electricity price prediction model.
9. A computer-readable storage medium comprising executable instructions that, when executed by a processor of an electronic device, cause the processor to perform the method of any of claims 1-4.
10. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of any of claims 1-4 when the processor executes the execution instructions stored by the memory.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110888857A (en) * | 2019-10-14 | 2020-03-17 | 平安科技（深圳）有限公司 | Data label generation method, device, terminal and medium based on neural network |
| CN112308335A (en) * | 2020-11-12 | 2021-02-02 | 南方电网能源发展研究院有限责任公司 | Short-term electricity price prediction method and device based on xgboost algorithm |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110888857A (en) * | 2019-10-14 | 2020-03-17 | 平安科技（深圳）有限公司 | Data label generation method, device, terminal and medium based on neural network |
| CN110888857B (en) * | 2019-10-14 | 2023-11-07 | 平安科技（深圳）有限公司 | Data tag generation method, device, terminal and medium based on neural network |
| CN112308335A (en) * | 2020-11-12 | 2021-02-02 | 南方电网能源发展研究院有限责任公司 | Short-term electricity price prediction method and device based on xgboost algorithm |
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Application publication date: 20190924 |