CN110766134A - Photovoltaic power station short-term power prediction method based on cyclic neural network - Google Patents

Photovoltaic power station short-term power prediction method based on cyclic neural network Download PDF

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CN110766134A
CN110766134A CN201910910254.XA CN201910910254A CN110766134A CN 110766134 A CN110766134 A CN 110766134A CN 201910910254 A CN201910910254 A CN 201910910254A CN 110766134 A CN110766134 A CN 110766134A
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陈志聪
陈辉煌
吴丽君
程树英
林培杰
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Abstract

The invention relates to a photovoltaic power station short-term power prediction method based on a recurrent neural network, which comprises the following steps: step S1: obtaining corresponding NWP meteorological parameters according to the weather type of the day to be predicted; step S2: collecting a plurality of calendar history data before a day to be predicted; step S3, processing the historical data and using the processed historical data as a training data set; step S4: learning the training data set by adopting a cyclic neural network, and adjusting parameters of the network by using a random gradient descent method to obtain a prediction model; step S5: and taking the NWP meteorological parameters of the day to be predicted as the input of the prediction model to obtain the predicted power value. The method can obviously improve the accuracy and reliability of the short-term power prediction of the photovoltaic power station.

Description

Photovoltaic power station short-term power prediction method based on cyclic neural network
Technical Field
The invention belongs to a short-term prediction technology of photovoltaic power station power, and particularly relates to a photovoltaic power station short-term power prediction method based on a recurrent neural network.
Background
In recent years, fossil fuels are gradually exhausted, environmental pollution is increasingly serious, wide attention of all countries in the world is attracted, a continuous development road is a necessary way in the future for the continuation of human civilization, new energy needs to be searched to bear energy required by human social operation, solar energy is the most spotlighted of the new energy, but the safe and stable operation of a power grid is not facilitated due to strong fluctuation and large randomness of the solar energy, and the capacity of a photovoltaic power station is estimated in advance by utilizing a photovoltaic power prediction technology to serve as a basis for allocating the power grid, so that the operation of the power grid is more stable and reliable.
Currently, there are a lot of photovoltaic power prediction methods, which can be broadly divided into three categories: the scholars indicate that in the statistical research methods, the physical method accounts for 11%, the mixed method accounts for 17%, and the rest are statistical methods, and account for up to 72%, wherein the most research of the artificial neural network accounts for 24%.
Disclosure of Invention
In view of this, the present invention provides a photovoltaic power plant short-term power prediction method based on a recurrent neural network, so as to improve the accuracy of photovoltaic power plant short-term power prediction.
In order to achieve the purpose, the invention adopts the following technical scheme:
a photovoltaic power station short-term power prediction method based on a recurrent neural network comprises the following steps:
step S1: obtaining corresponding NWP meteorological parameters according to the weather type of the day to be predicted;
step S2: collecting a plurality of calendar history data before a day to be predicted;
step S3, processing the historical data and using the processed historical data as a training data set;
step S4: learning the training data set by adopting a cyclic neural network, and adjusting parameters of the network by using a random gradient descent method to obtain a prediction model;
step S5: and taking the NWP meteorological parameters of the day to be predicted as the input of the prediction model to obtain the predicted power value.
Further, the step S1 is specifically: the NWP meteorological parameters corresponding to the sunny days comprise ground horizontal irradiance, scattering horizontal irradiance and ambient temperature; the NWP meteorological parameters corresponding to the partial cloudy days comprise ground level irradiance, scattering level irradiance, ambient temperature and relative humidity; the NWP meteorological parameters corresponding to the cloudy days comprise ground level irradiance, scattering level irradiance, ambient temperature and relative humidity; the NWP meteorological parameters corresponding to rainy days include relative humidity.
Further, the historical data includes historical power and historical NWP meteorological parameters.
Further, the step S3 is specifically:
step S31, removing data corresponding to the part of the historical data caused by the power change of the non-meteorological parameters and removing data of the night;
and step S32, performing normalization processing on the history data after the elimination processing, and taking the normalized history data as a training data set.
Further, the step S4 is specifically:
step S41: the forward propagation uses the RNN network in the recurrent neural network, and the method comprises the following steps:
St＝f(UXt+WSt-1+B1)
Ot＝g(VSt+B2)
wherein U, W and V are weights; b is1And B2Is a bias value; xtIs the RNN network input at time t; stHidden layer for time tAn output of (d); o istIs the output of the RNN network at time t; f (-) is the activation function of the hidden layer, and the expression isg (-) is the activation function of the output layer, the output layer will select different activation functions during training and prediction, linear function during training is selected, and positive linear function is used during prediction, the expression of which is
Figure RE-GDA0002334218510000032
Step S42: the back propagation uses a random gradient descent method, which specifically comprises the following steps:
first, an objective function is defined
Wherein, p is a parameter to be adjusted, namely a weight value and a bias value;
Figure RE-GDA0002334218510000034
is an objective function; y istThe actual power value at the time t; a ist(p) is a network output value at the time t, namely a predicted value; e.g. of the typet(p) is the corresponding error;
then, the parameters were adjusted using a random gradient descent method:
Figure RE-GDA0002334218510000035
α is learning rate, which can be constant or variable with training times, and the expression is
Figure RE-GDA0002334218510000036
Compared with the prior art, the invention has the following beneficial effects:
the method is based on the cyclic neural network, considers the current meteorological parameters and the influence of historical power on the current power, and greatly improves the accuracy and reliability of the short-term power prediction of the photovoltaic power station compared with the conventional short-term power prediction method of the photovoltaic power station.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a block diagram of an RNN network in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of a descending curve of the stochastic gradient descent method in an embodiment of the present invention;
fig. 4 is a diagram of a prediction result of a photovoltaic power plant short-term power prediction model based on a recurrent neural network in a sunny day in an embodiment of the present invention.
Fig. 5 is a diagram of a prediction result of a photovoltaic power plant short-term power prediction model based on a recurrent neural network in a partial cloudy day in an embodiment of the present invention.
Fig. 6 is a diagram of a prediction result of a photovoltaic power plant short-term power prediction model based on a recurrent neural network in a cloudy day in an embodiment of the present invention.
Fig. 7 is a diagram of a prediction result of a photovoltaic power plant short-term power prediction model based on a recurrent neural network in a rainy day in an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the present invention provides a method for predicting short-term power of a photovoltaic power station based on a recurrent neural network, comprising the following steps:
step S1: obtaining corresponding NWP meteorological parameters according to the weather type of the day to be predicted;
step S2: acquiring historical power and historical NWP meteorological parameters of a plurality of ephemeris history data before a day to be predicted;
step S3, processing the historical power and the historical NWP meteorological parameters, and taking the processed historical data as a training data set;
step S4: learning the training data set by adopting a cyclic neural network, and adjusting parameters of the network by using a random gradient descent method to obtain a prediction model;
step S5: and taking the NWP meteorological parameters of the day to be predicted as the input of the prediction model to obtain the predicted power value.
In this embodiment, according to the weather type of the day to be predicted, the obtaining of the corresponding NWP meteorological parameters specifically includes: the NWP meteorological parameters corresponding to the sunny days comprise ground horizontal irradiance, scattering horizontal irradiance and ambient temperature; the NWP meteorological parameters corresponding to the partial cloudy days comprise ground level irradiance, scattering level irradiance, ambient temperature and relative humidity; the NWP meteorological parameters corresponding to the cloudy days comprise ground level irradiance, scattering level irradiance, ambient temperature and relative humidity; the NWP meteorological parameters corresponding to rainy days include relative humidity.
In this embodiment, the step S3 specifically includes:
step S31, removing data corresponding to the part of the historical data caused by the power change of the non-meteorological parameters and removing data of the night;
and step S32, performing normalization processing on the history data after the elimination processing, and taking the normalized history data as a training data set.
In this embodiment, the step S4 specifically includes:
step S41: the forward propagation uses the RNN network in the recurrent neural network, and the method comprises the following steps:
St＝f(UXt+WSt-1+B1)
Ot＝g(VSt+B2)
wherein U, W and V are weights; b is1And B2Is a bias value; xtIs the RNN network input at time t; stIs the output of the hidden layer at the time t; o istIs the output of the RNN network at time t; f (-) is the activation function of the hidden layer, and the expression is
Figure RE-GDA0002334218510000061
g (-) is the activation function of the output layer, the output layer will select different activation functions during training and prediction, linear function during training is selected, and positive linear function is used during prediction, the expression of which is
Figure RE-GDA0002334218510000062
The structure of the RNN network is shown in fig. 2;
step S42: the back propagation uses a random gradient descent method, which specifically comprises the following steps:
first, an objective function is defined
Figure RE-GDA0002334218510000063
Wherein, p is a parameter to be adjusted, namely a weight value and a bias value;
Figure RE-GDA0002334218510000064
is an objective function; y istThe actual power value at the time t; a ist(p) is a network output value at the time t, namely a predicted value; e.g. of the typet(p) is the corresponding error;
then, the parameters were adjusted using a random gradient descent method:
Figure RE-GDA0002334218510000065
α is learning rate, which can be constant or variable with training times, and the expression is
Figure RE-GDA0002334218510000066
The descending curve of the random gradient descent method is schematically shown in FIG. 3.
In the example, a photovoltaic power station No. 22 (with a capacity of 16.8kW) of a DKA Solar center (a server knowledgebase Solar center) is taken as a research object, photovoltaic power prediction is performed under four different weather conditions of a clear day, a partial cloudy day, a cloudy day and a rainy day, and test data of each weather type are selected as follows: 2018/8/17, 2018/8/18, 2018/8/22; partial cloudy day: 2018/1/23, 2018/1/24, 2018/1/25; in cloudy days: 2018/10/31, 2018/11/16, 2018/11/17; in rainy days: 2018/3/8, 2018/3/9, 2018/11/7; the training data was selected as 20 days history data prior to the test data. The predicted effect maps are shown in fig. 4 to 7. As can be seen from the Mean Absolute Percent Error (MAPE) and the Root Mean Square Error (RMSE) in Table 1, the method provided by the invention can perform more accurate prediction, and fully embodies the accuracy of the invention.
TABLE 1 Performance testing under four weather conditions
Figure RE-GDA0002334218510000071
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.

Claims (5)

1. A photovoltaic power station short-term power prediction method based on a recurrent neural network is characterized by comprising the following steps:
step S1: obtaining corresponding NWP meteorological parameters according to the weather type of the day to be predicted;
step S2: collecting a plurality of calendar history data before a day to be predicted;
step S3, processing the historical data and using the processed historical data as a training data set;
step S4: learning the training data set by adopting a cyclic neural network, and adjusting parameters of the network by using a random gradient descent method to obtain a prediction model;
step S5: and taking the NWP meteorological parameters of the day to be predicted as the input of the prediction model to obtain the predicted power value.
2. The photovoltaic power plant short-term power prediction method based on the recurrent neural network as claimed in claim 1, wherein the step S1 specifically comprises: the NWP meteorological parameters corresponding to the sunny days comprise ground horizontal irradiance, scattering horizontal irradiance and ambient temperature; the NWP meteorological parameters corresponding to the partial cloudy days comprise ground level irradiance, scattering level irradiance, ambient temperature and relative humidity; the NWP meteorological parameters corresponding to the cloudy days comprise ground level irradiance, scattering level irradiance, ambient temperature and relative humidity; the NWP meteorological parameters corresponding to rainy days include relative humidity.
3. The recurrent neural network-based short-term power prediction method for photovoltaic power plants as claimed in claim 1, characterized in that: the historical data includes historical power and historical NWP meteorological parameters.
4. The photovoltaic power plant short-term power prediction method based on the recurrent neural network as claimed in claim 3, wherein the step S3 specifically comprises:
step S31, removing data corresponding to the part of the historical data caused by the power change of the non-meteorological parameters and removing data of the night;
and step S32, performing normalization processing on the history data after the elimination processing, and taking the normalized history data as a training data set.
5. The photovoltaic power plant short-term power prediction method based on the recurrent neural network as claimed in claim 1, wherein the step S4 specifically comprises:
step S41: the forward propagation uses the RNN network in the recurrent neural network, and the method comprises the following steps:
St＝f(UXt+WSt-1+B1)
Ot＝g(VSt+B2)
wherein U, W and V are weights; b is1And B2Is a bias value; xtIs the RNN network input at time t; stIs the output of the hidden layer at the time t; o istIs the output of the RNN network at time t; f (-) is the activation function of the hidden layer, and the expression is
Figure FDA0002214490510000021
g (-) is the activation function of the output layer, the output layer will select different activation functions during training and prediction, linear function during training is selected, and positive linear function is used during prediction, the expression of which is
Step S42: the back propagation uses a random gradient descent method, which specifically comprises the following steps:
first, an objective function is defined
Figure FDA0002214490510000023
Wherein, p is a parameter to be adjusted, namely a weight value and a bias value;
Figure FDA0002214490510000031
is an objective function; y istThe actual power value at the time t; a ist(p) is a network output value at the time t, namely a predicted value; e.g. of the typet(p) is the corresponding error;
then, the parameters were adjusted using a random gradient descent method:
Figure FDA0002214490510000032
α is learning rate, which can be constant or variable with training times, and the expression is
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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111539573A (en) * 2020-04-27 2020-08-14 广州市香港科大霍英东研究院 Power prediction method and system for wind-solar hybrid off-grid system
CN111695736A (en) * 2020-06-15 2020-09-22 河北锐景能源科技有限公司 Photovoltaic power generation short-term power prediction method based on multi-model fusion
CN112149905A (en) * 2020-09-25 2020-12-29 福州大学 Photovoltaic power station short-term power prediction method based on wavelet transformation and wavelet neural network
CN112215478A (en) * 2020-09-27 2021-01-12 珠海博威电气股份有限公司 Power coordination control method and device for optical storage power station and storage medium
CN112230628A (en) * 2020-11-10 2021-01-15 北京中超伟业信息安全技术股份有限公司 Data identification method and system for high-noise industrial process
CN112686472A (en) * 2021-01-22 2021-04-20 国网河南省电力公司许昌供电公司 Power prediction method for distributed photovoltaic equivalent power station
CN112949936A (en) * 2021-03-29 2021-06-11 福州大学 Short-term photovoltaic power prediction method based on similar-day wavelet transform and multilayer perceptron
CN113919232A (en) * 2021-10-25 2022-01-11 北京航天创智科技有限公司 Photovoltaic power station power prediction method and system based on recurrent neural network

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170286830A1 (en) * 2016-04-04 2017-10-05 Technion Research & Development Foundation Limited Quantized neural network training and inference
CN107766937A (en) * 2017-09-11 2018-03-06 重庆大学 Feature based chooses and the wind power ultra-short term prediction method of Recognition with Recurrent Neural Network
CN108038580A (en) * 2017-12-30 2018-05-15 国网江苏省电力公司无锡供电公司 The multi-model integrated Forecasting Methodology of photovoltaic power based on synchronous extruding wavelet transformation
CN108280551A (en) * 2018-02-02 2018-07-13 华北电力大学 A kind of photovoltaic power generation power prediction method using shot and long term memory network
CN109284870A (en) * 2018-10-08 2019-01-29 南昌大学 Short-term method for forecasting photovoltaic power generation quantity based on shot and long term Memory Neural Networks

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170286830A1 (en) * 2016-04-04 2017-10-05 Technion Research & Development Foundation Limited Quantized neural network training and inference
CN107766937A (en) * 2017-09-11 2018-03-06 重庆大学 Feature based chooses and the wind power ultra-short term prediction method of Recognition with Recurrent Neural Network
CN108038580A (en) * 2017-12-30 2018-05-15 国网江苏省电力公司无锡供电公司 The multi-model integrated Forecasting Methodology of photovoltaic power based on synchronous extruding wavelet transformation
CN108280551A (en) * 2018-02-02 2018-07-13 华北电力大学 A kind of photovoltaic power generation power prediction method using shot and long term memory network
CN109284870A (en) * 2018-10-08 2019-01-29 南昌大学 Short-term method for forecasting photovoltaic power generation quantity based on shot and long term Memory Neural Networks

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
XIAOCI ZHANG等: "GT-SGD: A Novel Gradient Synchronization Algorithm in Training Distributed Recurrent Neural Network Language Models", 《2017 INTERNATIONAL CONFERENCE ON NETWORKING AND NETWORK APPLICATIONS (NANA)》 *
牛哲文等: "基于深度门控循环单元神经网络的短期风功率预测模型", 《电力自动化设备》 *
王增新等: "光伏发电预测技术的应用研究", 《云南电力技术》 *

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111539573A (en) * 2020-04-27 2020-08-14 广州市香港科大霍英东研究院 Power prediction method and system for wind-solar hybrid off-grid system
CN111539573B (en) * 2020-04-27 2022-09-30 广州市香港科大霍英东研究院 Power prediction method and system for wind-solar hybrid off-grid system
CN111695736A (en) * 2020-06-15 2020-09-22 河北锐景能源科技有限公司 Photovoltaic power generation short-term power prediction method based on multi-model fusion
CN112149905A (en) * 2020-09-25 2020-12-29 福州大学 Photovoltaic power station short-term power prediction method based on wavelet transformation and wavelet neural network
CN112215478A (en) * 2020-09-27 2021-01-12 珠海博威电气股份有限公司 Power coordination control method and device for optical storage power station and storage medium
CN112215478B (en) * 2020-09-27 2024-05-28 珠海博威电气股份有限公司 Power coordination control method and device for optical storage station and storage medium
CN112230628A (en) * 2020-11-10 2021-01-15 北京中超伟业信息安全技术股份有限公司 Data identification method and system for high-noise industrial process
CN112230628B (en) * 2020-11-10 2021-11-16 北京中超伟业信息安全技术股份有限公司 Data identification method and system for high-noise industrial process
CN112686472A (en) * 2021-01-22 2021-04-20 国网河南省电力公司许昌供电公司 Power prediction method for distributed photovoltaic equivalent power station
CN112686472B (en) * 2021-01-22 2022-09-20 国网河南省电力公司许昌供电公司 Power prediction method for distributed photovoltaic equivalent power station
CN112949936A (en) * 2021-03-29 2021-06-11 福州大学 Short-term photovoltaic power prediction method based on similar-day wavelet transform and multilayer perceptron
CN113919232A (en) * 2021-10-25 2022-01-11 北京航天创智科技有限公司 Photovoltaic power station power prediction method and system based on recurrent neural network

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Application publication date: 20200207