CN116565865A - Wind power prediction method based on deep error feedback learning - Google Patents

Wind power prediction method based on deep error feedback learning Download PDF

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CN116565865A
CN116565865A CN202310843436.6A CN202310843436A CN116565865A CN 116565865 A CN116565865 A CN 116565865A CN 202310843436 A CN202310843436 A CN 202310843436A CN 116565865 A CN116565865 A CN 116565865A
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胡宇晗
朱利鹏
李佳勇
帅智康
曾杨
郑李梦千
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • G—PHYSICS
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    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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Abstract

The invention provides a wind power prediction method based on depth error feedback learning, which comprises the following steps of S1, constructing a prediction error estimation model based on XGBoost, wherein the prediction error estimation model takes weather forecast data as input and prediction error as output; s2, constructing a wind power secondary prediction model based on double-layer LSTM, wherein the wind power secondary prediction model takes weather forecast data as main input, prediction error as feedback input and wind power predicted value as output; and step S3, sending the current weather forecast data into a prediction error estimation model to obtain a future prediction error, and sending the current weather forecast data and the future prediction error into the wind power secondary prediction model together to obtain a final wind power predicted value. The method can effectively improve the prediction precision of the wind power plant, reduce the uncertainty of the output of the wind power plant and facilitate the improvement of the utilization efficiency of wind resources.

Description

Wind power prediction method based on deep error feedback learning
Technical Field
The invention relates to the technical field of wind power prediction, in particular to a wind power prediction method based on deep error feedback learning.
Background
Wind energy has the advantages of wide distribution range, cleanness, reproducibility and the like. However, due to the outstanding intermittence, volatility and uncertainty of wind power generation, large scale grid connection thereof presents serious challenges for safe and reliable operation of the power system. If the wind power can be predicted efficiently and reliably, the uncertainty of the output of the wind power can be effectively reduced, and important support and guarantee are provided for the safe and stable operation of the system after large-scale wind power access.
The existing prediction method is difficult to accurately describe the relation between wind power plant meteorological data and the output power of a station, so that a prediction error inevitably exists in a wind power prediction result, and if the hidden useful information in the error can be fully excavated and learned, the wind power prediction precision is expected to be further improved. However, existing methods all attempt to improve prediction accuracy by directly improving the prediction model itself, and the potential value of prediction errors for improving prediction performance is not mined, and the prediction result faces a bottleneck that cannot be further improved.
Disclosure of Invention
The invention aims to provide a wind power prediction method based on deep error feedback learning so as to improve the prediction accuracy of wind power.
In order to solve the technical problems, the invention adopts the following technical methods: a wind power prediction method based on deep error feedback learning comprises the following steps:
step S1, constructing a prediction error estimation model based on XGBoost, wherein weather forecast data is taken as input and a prediction error is taken as output;
s2, constructing a wind power secondary prediction model based on double-layer LSTM, wherein the wind power secondary prediction model takes weather forecast data as main input, prediction error as feedback input and wind power predicted value as output;
and step S3, sending the current weather forecast data into the prediction error estimation model to obtain a future prediction error, and sending the current weather forecast data and the future prediction error into the wind power secondary prediction model together to obtain a final wind power predicted value.
Further, the construction of the prediction error estimation model includes the following steps:
s11, collecting weather forecast data and wind power of a history of a prediction target wind power plant, and calculating a prediction error of the history;
s12, constructing a prediction error estimation model based on XGBoost, wherein the prediction error estimation model takes weather forecast data as input and prediction error as output;
s13, setting initial parameters of a prediction error estimation model, training the prediction error estimation model by using historical weather forecast data and historical prediction errors, and continuously optimizing the model parameters based on grid search until the model converges.
Further, the process of calculating the prediction error of the history in step S11 is as follows:
1) Collecting weather forecast data of a forecast target wind farm historyAnd wind power->；
2) Will be、/>Normalizing to obtain->、/>；
3) Constructing a preliminary prediction model based on double-layer LSTM and taking weather forecast data as input and wind power data as output, setting initial parameters of the preliminary prediction model, and utilizing、/>Training the preliminary prediction model by the data, and continuously optimizing the model parameters based on grid search until the model converges;
4) Will beSending the wind power prediction value into a trained preliminary prediction model to obtain a wind power prediction value of wind power plant history +.>；
5) The method of 4)Performing inverse normalization to obtain->；
6) Based on that obtained in 5)And 1) collected->Calculating the prediction error of the history +.>The following formula:
(1)
7) The method obtained in 6) is carried outPerforming normalizationDissolving to obtain->。
Further, in step S13, initial parameters of the prediction error estimation model are set and usedAnd->Training the prediction error estimation model, and continuously optimizing the model parameters based on grid search until the model converges.
Still further, the preliminary prediction model comprises an input layer, two LSTM layers, a full connection layer and an output layer; the wind power secondary prediction model comprises two input layers, two LSTM layers, a full connection layer and an output layer.
Further, the wind power secondary prediction model is constructed by the following steps:
s21, constructing a wind power secondary prediction model based on double-layer LSTM, wherein the wind power secondary prediction model takes weather forecast data as main input, prediction error as feedback input and wind power predicted value as output;
s22, setting initial parameters of a wind power secondary prediction model, and utilizing、/>、/>Training a wind power secondary prediction model, wherein the wind power secondary prediction model is used for training>Vector +.>Will->And->Forming a combined vectorThen input into the full connection layer, output +.>；
And S23, continuously optimizing parameters of the wind power secondary prediction model based on grid search until the model converges.
Further, the step S3 includes:
s31, collecting current weather forecast data of a prediction target wind power plant；
S32, willNormalizing to obtain->；
S33, willSending the prediction error into a trained prediction error estimation model to obtain future prediction error +.>；
S34, will、/>Sending the wind power into a trained wind power secondary prediction model to obtain a prediction result +.>；
S35, willPerforming inverse normalization to obtain final wind power predicted value +.>。
Preferably, the weather forecast data includes wind speed, wind direction, air temperature and air pressure.
Compared with the traditional wind power prediction method, the wind power prediction method based on the depth error feedback learning provided by the invention extracts historical error information in a preliminary prediction stage, estimates future prediction errors based on XGBoost (eXtreme Gradient Boosting extreme gradient lifting algorithm), and applies the future prediction errors to secondary wind power prediction, so that hidden useful information in the prediction errors is fully excavated and utilized, the prediction accuracy of wind power is improved, the uncertainty of wind power plant output is reduced, and the utilization efficiency of wind resources is improved.
Drawings
FIG. 1 is a flow chart of a wind power prediction method based on depth error feedback learning according to the present invention;
FIG. 2 is a frame diagram of a wind power prediction method based on depth error feedback learning according to the present invention;
FIG. 3 is a block diagram of a preliminary predictive model in accordance with the present invention;
FIG. 4 is a structural diagram of a wind power secondary prediction model in the invention.
Detailed Description
The invention will be further described with reference to examples and drawings, to which reference is made, but which are not intended to limit the scope of the invention.
In wind power prediction, the prediction result inevitably has errors, and if the hidden useful information in the errors can be fully mined and learned, the wind power prediction precision is expected to be further improved. The method takes the method as a starting point, extracts historical error information in a preliminary prediction stage, estimates future prediction errors based on XGBoost (eXtreme Gradient Boosting extreme gradient lifting algorithm), and acts the future prediction errors on wind power secondary prediction to fully learn the errors so as to improve the overall prediction performance.
As shown in fig. 1 and fig. 2, a wind power prediction method based on deep error feedback learning mainly includes the following three steps of constructing a preliminary prediction model, a prediction error estimation model, a wind power secondary prediction model and predicting future wind power.
Step S1
S11, collecting weather forecast data and wind power of a history of a prediction target wind power plant, and calculating a prediction error of the history.
1) Collecting weather forecast data of a forecast target wind farm historyAnd wind power->。
2) Will be、/>Normalizing to obtain->、/>。
3) A preliminary prediction model based on double-layer LSTM and taking weather forecast data as input and wind power data as output is constructed, and as shown in figure 3, the preliminary prediction model consists of an input layer, two LSTM layers, a full connection layer and an output layer. Setting initial parameters of the preliminary prediction model, and utilizing、/>And training the preliminary prediction model by the data, and continuously optimizing the model parameters based on grid search until the model converges.
4) As shown in fig. 3, willSending the wind power into a trained preliminary prediction model to obtain preliminary predicted wind power, wherein the preliminary predicted wind power is also a wind power predicted value +.>。
5) The method of 4)Performing inverse normalization to obtain->。
6) Based on that obtained in 5)And 1) collected->Calculating the prediction error of the history +.>The following formula:
(1)
7) The method obtained in 6) is carried outNormalizing to obtain->。
S12, constructing a prediction error estimation model based on XGBoost, wherein the prediction error estimation model takes weather forecast data as input and prediction error as output.
S13, setting initial parameters of a prediction error estimation model, and utilizingAnd->Training the prediction error estimation model, and continuously optimizing the model parameters based on grid search until the model converges.
Step S2
S21, constructing a wind power secondary prediction model based on double-layer LSTM, wherein the wind power secondary prediction model is composed of two input layers, two LSTM layers, a full connection layer and an output layer, and the weather forecast data is used as main input, the prediction error is used as feedback input and the wind power predicted value is used as output.
S22, setting initial parameters of a wind power secondary prediction model, and utilizing、/>、/>Training of the power quadratic prediction model, as shown in FIG. 4,/for training>Vector +.>Will->And->Forming a combined vectorThen input into the full connection layer, output +.>。
And S23, continuously optimizing parameters of the wind power secondary prediction model based on grid search until the model converges.
Step S3
S31, collecting current weather forecast data of a prediction target wind power plant。
S32, willNormalizing to obtain->。
S33, willSending the prediction error into a trained prediction error estimation model to obtain future prediction error +.>。
S34, will、/>Sending the wind power into a trained wind power secondary prediction model to obtain secondary predicted wind power which is also a prediction result +.>。
S35, willPerforming inverse normalization to obtain final wind power predicted value +.>。
Preferably, the weather forecast data includes characteristic information such as wind speed, wind direction, air temperature, air pressure, etc.
In order to verify the effectiveness and accuracy of the wind power prediction method based on the depth error feedback learning, two indexes of absolute average error (MAE) and Root Mean Square Error (RMSE) can be adopted to evaluate the method, and the method is specifically:
a) Predicted value of wind power in step S35Predicted by a wind power secondary prediction model, and calculated +.>MAE and RMSE values of (A).
b) Obtained in step S32Sending the wind power into a trained preliminary prediction model, and predicting the wind power by adopting the preliminary prediction model to obtain a prediction result +.>Will->After inverse normalization, MAE and RMSE values were calculated.
c) Comparing the MAE and RMSE values obtained in a) and b), if the MAE and RMSE values obtained in a) are smaller than the MAE and RMSE values obtained in b), the fact that error information is introduced into a prediction model can effectively improve prediction accuracy is indicated, otherwise, the prediction accuracy cannot be improved. In addition, the smaller the values of MAE and RMSE, the higher the prediction accuracy of the model.
The foregoing embodiments are preferred embodiments of the present invention, and in addition, the present invention may be implemented in other ways, and any obvious substitution is within the scope of the present invention without departing from the concept of the present invention.
In order to facilitate understanding of the improvements of the present invention over the prior art, some of the figures and descriptions of the present invention have been simplified, and some other elements have been omitted from this document for clarity, as will be appreciated by those of ordinary skill in the art.

Claims (8)

1. The wind power prediction method based on the deep error feedback learning is characterized by comprising the following steps of:
step S1, constructing a prediction error estimation model based on XGBoost, wherein weather forecast data is taken as input and a prediction error is taken as output;
s2, constructing a wind power secondary prediction model based on double-layer LSTM, wherein the wind power secondary prediction model takes weather forecast data as main input, prediction error as feedback input and wind power predicted value as output;
and step S3, sending the current weather forecast data into the prediction error estimation model to obtain a future prediction error, and sending the current weather forecast data and the future prediction error into the wind power secondary prediction model together to obtain a final wind power predicted value.
2. The wind power prediction method based on depth error feedback learning according to claim 1, wherein: the construction of the prediction error estimation model comprises the following steps:
s11, collecting weather forecast data and wind power of a history of a prediction target wind power plant, and calculating a prediction error of the history;
s12, constructing a prediction error estimation model based on XGBoost, wherein the prediction error estimation model takes weather forecast data as input and prediction error as output;
s13, setting initial parameters of a prediction error estimation model, training the prediction error estimation model by using historical weather forecast data and historical prediction errors, and continuously optimizing the model parameters based on grid search until the model converges.
3. The wind power prediction method based on depth error feedback learning according to claim 2, wherein: the process of calculating the prediction error of the history in step S11 is as follows:
1) Collecting weather forecast data of a forecast target wind farm historyAnd wind power->；
2) Will be、/>Normalizing to obtain->、/>；
3) Constructing a preliminary prediction model based on double-layer LSTM and taking weather forecast data as input and wind power data as output, setting initial parameters of the preliminary prediction model, and utilizing、/>Training the preliminary prediction model by the data, and continuously optimizing the model parameters based on grid search until the model converges;
4) Will beSending the wind power prediction value into a trained preliminary prediction model to obtain a wind power prediction value of wind power plant history +.>；
5) The method of 4)Performing inverse normalization to obtain->；
6) Based on that obtained in 5)And 1) collected->Calculating the prediction error of the history +.>The following formula:
(1)
7) The method obtained in 6) is carried outNormalizing to obtain->。
4. A method for predicting wind power based on depth error feedback learning as claimed in claim 3, wherein: in step S13, initial parameters of the prediction error estimation model are set, and usedAnd->Training the prediction error estimation model, and continuously optimizing the model parameters based on grid search until the model converges.
5. The wind power prediction method based on depth error feedback learning according to claim 4, wherein: the preliminary prediction model comprises an input layer, two LSTM layers, a full connection layer and an output layer; the wind power secondary prediction model comprises two input layers, two LSTM layers, a full connection layer and an output layer.
6. The wind power prediction method based on depth error feedback learning according to claim 5, wherein: the wind power secondary prediction model is constructed by the following steps:
s21, constructing a wind power secondary prediction model based on double-layer LSTM, wherein the wind power secondary prediction model takes weather forecast data as main input, prediction error as feedback input and wind power predicted value as output;
s22, setting initial parameters of a wind power secondary prediction model, and utilizing、/>、/>Training a wind power secondary prediction model, wherein the wind power secondary prediction model is used for training>Vector +.>Will->And->Form a combination vector->Then input into the full connection layer, output +.>；
And S23, continuously optimizing parameters of the wind power secondary prediction model based on grid search until the model converges.
7. The wind power prediction method based on depth error feedback learning according to claim 6, wherein: the step S3 includes:
s31, collecting current weather forecast data of a prediction target wind power plant；
S32, willNormalizing to obtain->；
S33, willSending the prediction error into a trained prediction error estimation model to obtain future prediction error +.>；
S34, will、/>Sending the wind power into a trained wind power secondary prediction model to obtain a prediction result +.>；
S35, willPerforming inverse normalization to obtain final wind power predicted value +.>。
8. The wind power prediction method based on depth error feedback learning according to claim 7, wherein: the weather forecast data comprises wind speed, wind direction, air temperature and air pressure.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107704966A (en) * 2017-10-17 2018-02-16 华南理工大学 A kind of Energy Load forecasting system and method based on weather big data
CN111815033A (en) * 2020-06-19 2020-10-23 上海电力大学 Offshore wind power prediction method based on RCNN and meteorological time sequence characteristics
CN112348292A (en) * 2021-01-07 2021-02-09 中国电力科学研究院有限公司 Short-term wind power prediction method and system based on deep learning network
CN114330915A (en) * 2021-12-31 2022-04-12 青海大学 Short-term wind power combination model prediction method
CN115238980A (en) * 2022-07-13 2022-10-25 湖南大学 Micro-grid transient power angle trajectory prediction method based on attention mechanism and space-time diagram convolutional neural network
CN116307287A (en) * 2023-05-19 2023-06-23 国网信息通信产业集团有限公司 Prediction method, system and prediction terminal for effective period of photovoltaic power generation

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107704966A (en) * 2017-10-17 2018-02-16 华南理工大学 A kind of Energy Load forecasting system and method based on weather big data
CN111815033A (en) * 2020-06-19 2020-10-23 上海电力大学 Offshore wind power prediction method based on RCNN and meteorological time sequence characteristics
CN112348292A (en) * 2021-01-07 2021-02-09 中国电力科学研究院有限公司 Short-term wind power prediction method and system based on deep learning network
CN114330915A (en) * 2021-12-31 2022-04-12 青海大学 Short-term wind power combination model prediction method
CN115238980A (en) * 2022-07-13 2022-10-25 湖南大学 Micro-grid transient power angle trajectory prediction method based on attention mechanism and space-time diagram convolutional neural network
CN116307287A (en) * 2023-05-19 2023-06-23 国网信息通信产业集团有限公司 Prediction method, system and prediction terminal for effective period of photovoltaic power generation

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张瀚超 等: "偏差修正算法的风电短期功率预测", 《新型工业化》, vol. 9, no. 2, pages 1 - 9 *

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