CN114091317A - Photovoltaic power station power prediction method based on NWP irradiance correction and error prediction - Google Patents

Photovoltaic power station power prediction method based on NWP irradiance correction and error prediction Download PDF

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CN114091317A
CN114091317A CN202111354751.XA CN202111354751A CN114091317A CN 114091317 A CN114091317 A CN 114091317A CN 202111354751 A CN202111354751 A CN 202111354751A CN 114091317 A CN114091317 A CN 114091317A
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吕清泉
张睿骁
马明
陶钰磊
高鹏飞
张彦琪
赵多贤
张金平
张健美
张珍珍
王定美
李津
周强
朱红路
侯汝印
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STATE GRID GASU ELECTRIC POWER RESEARCH INSTITUTE
State Grid Gansu Electric Power Co Ltd
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Abstract

The invention relates to a photovoltaic power station power prediction method based on NWP irradiance correction and error prediction, which comprises the following steps: acquiring historical operating data of a photovoltaic power station and NWP numerical weather forecast; establishing an NWP irradiance correction model to realize multi-step correction of the NWP irradiance; thirdly, obtaining power prediction data by adopting the corrected irradiance based on the optimized power prediction model of the PSO-ELM; fourthly, calculating an error according to the power prediction data, and establishing an error prediction model based on a time sequence; and obtaining a plurality of error predicted values by a data iteration method, and combining the error predicted values with power predicted data to obtain a final photovoltaic power predicted value. The method has higher prediction precision, can effectively improve the reliability of photovoltaic power generation power prediction, and provides reference for a decision maker of a power system.

Description

Photovoltaic power station power prediction method based on NWP irradiance correction and error prediction
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power station power prediction method based on NWP irradiance correction and error prediction.
Background
In recent years, it has become a common consensus among countries to accelerate the development of new energy in order to solve the problem of fossil energy depletion and to realize energy transformation. Photovoltaic power generation is rapidly developing with its advantages of being clean and sustainable. However, photovoltaic power generation has strong randomness and intermittent periodicity. When photovoltaic power generation is connected to the grid in a large scale, impact can be generated on an electric power system, and safe and stable operation of the electric power system is seriously influenced. Because the photovoltaic short-term power prediction has important significance for the power generation planning of the power department, the accurate short-term power prediction can improve the reliability of the photovoltaic power generation connected to the power grid, reduce the influence of the photovoltaic power generation uncertainty on the power system, and is one of the key technologies for solving the grid-connected obstacle of the photovoltaic power generation.
At present, methods related to short-term power prediction of photovoltaic power generation can be roughly divided into two categories, namely physical methods and statistical methods. The physical method is based on a solar radiation transfer equation, a photovoltaic module operation equation and the like to establish a physical model so as to directly calculate the photovoltaic output power, but the modeling process is complex and the robustness is poor due to the involvement of various equations, and the method is difficult to realize; the statistical method is to train historical operating data of a photovoltaic power station to establish a prediction model through an intelligent algorithm according to the relation between the output power of photovoltaic power generation and meteorological factors, the model is relatively simple, but due to the limitation of the intelligent algorithm, a prediction result has a certain degree of error. Meanwhile, input data of power prediction is derived from numerical weather forecast, and the influence of the error of the numerical weather forecast on the power prediction is considered to be less in the prior art, so that the residual error of the conventional prediction method has negative influence on the power prediction precision, and the predicted data needs to be subjected to post-processing to reduce the influence.
Disclosure of Invention
The invention aims to provide a photovoltaic power station power prediction method based on NWP irradiance correction and error prediction, which improves prediction accuracy.
In order to solve the problems, the photovoltaic power station power prediction method based on NWP irradiance correction and error prediction comprises the following steps:
acquiring historical operating data of a photovoltaic power station and NWP numerical weather forecast;
establishing an NWP irradiance correction model to realize multi-step correction of the NWP irradiance;
thirdly, obtaining power prediction data by adopting the corrected irradiance based on the optimized power prediction model of the PSO-ELM;
fourthly, calculating an error according to the power prediction data, and establishing an error prediction model based on a time sequence; and obtaining a plurality of error predicted values by a data iteration method, and combining the error predicted values with power predicted data to obtain a final photovoltaic power predicted value.
The multi-step NWP irradiance correction method comprises the following steps:
determining data parameters of the corrected modelnWill bet-n+1 totThe actually measured irradiance at the moment is used as a correction variable so as to correct the NWP irradiance at the next moment;
selectingt-n+1,t-n+2…t-1, measured irradiance sum at time ttNWP forecast irradiance at +1 time is used as input to the model,tthe actually measured irradiance at the +1 moment is used as the output of the model, an ELM extreme learning machine algorithm is adopted to establish an irradiance correction model, and the aim of correcting the irradiance is achievedtCorrection of the predicted irradiance at time NWP + 1.
Thirdly, adopting a rolling iteration mode to performtThe NWP irradiance correction value at the +1 moment is used as input, and then the correction value is applied totCorrecting the NWP forecast irradiance at the +2 moment to realize multistep correction of the NWP forecast irradiance;
selecting a root mean square error RMSE, an average absolute error MAE and an average absolute percentage error MAPE to evaluate the NWP irradiance correction result, wherein the calculation formula is as follows:
Figure RE-DEST_PATH_IMAGE001
Figure RE-906072DEST_PATH_IMAGE002
Figure RE-DEST_PATH_IMAGE003
in the formula:g a for the corrected irradiance/predicted power;g measured is the actual irradiance/measured power;nis the number of samples.
The power prediction data in the step three is obtained according to the following method:
establishing a photovoltaic power prediction model by adopting an ELM extreme learning machine algorithm;
classifying data according to months, taking actual measurement historical meteorological data as input, taking actual measurement historical power as output, and training a prediction model;
iii, optimizing the number of hidden layer nodes of the ELM by adopting a Particle Swarm Optimization (PSO), and searching the optimal number of hidden layer nodes by taking the minimum mean square error of a prediction result as an objective function of the PSO optimization to realize the optimization of the photovoltaic power prediction model;
iv, different prediction models are selected according to months, and the corrected NWP irradiance data and other meteorological information are input to obtain power prediction data.
Compared with the prior art, the invention has the following advantages:
1. the method and the device consider that a hybrid prediction model is established from three aspects of data preprocessing, model parameter optimization and data post-processing in different stages of the prediction process, so that the power prediction precision is improved.
2. The irradiance correction model is established, the NWP irradiance is corrected through actually measured irradiance, and errors of input data in the prediction model are reduced.
3. The invention establishes a power prediction model based on PSO-ELM, and adopts a PSO optimization prediction model to reduce the error of the model.
4. The method utilizes the error sequence to establish an error prediction model, combines the power prediction value and the error prediction value to obtain a final power prediction result, and further reduces the error of power prediction.
5. Compared with the traditional method, the method has higher prediction precision, can effectively improve the reliability of photovoltaic power generation power prediction, and provides reference for a decision maker of a power system.
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The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a single step modification diagram of the present invention.
FIG. 3 is a schematic view of a multi-step modification of the present invention.
Fig. 4 is a schematic diagram of a photovoltaic power prediction model according to the present invention.
FIG. 5 is a schematic diagram of an error prediction model according to the present invention.
FIG. 6 is a graph of predicted power versus measured power according to the present invention.
Detailed Description
As shown in fig. 1, the photovoltaic power plant power prediction method based on NWP irradiance correction and error prediction includes the following steps:
acquiring historical operating data of a photovoltaic power station and NWP numerical weather forecast:
checking the integrity and reasonableness of the data, deleting error data, filling up missing data and the like. The collected raw data comprises historical power data and historical meteorological data (including environmental data such as irradiance, temperature and wind speed) of the photovoltaic power station and contemporaneous NWP numerical weather forecast data, and various data can be corresponded on a time scale.
And establishing a NWP irradiance correction model to realize multi-step correction of the NWP irradiance. The specific process is as follows:
determining data parameters of the corrected modelnWill bet-n+1 totThe measured irradiance at a time is used as a correction variable to correct for the next time (i.e., the time at which the measured irradiance is measured)t+1 time) is corrected, the correction process is as shown in fig. 2.
Selectingt-n+1,t-n+2…t-1, measured irradiance sum at time ttNWP forecast irradiance at +1 time is used as input to the model,tthe actually measured irradiance at the +1 moment is used as the output of the model, an ELM extreme learning machine algorithm is adopted to establish an irradiance correction model, and the aim of correcting the irradiance is achievedtCorrection of the predicted irradiance at time NWP + 1.
Given aNA plurality of different training samples (x i ,t i ) Wherein, in the step (A),x i =[x i1,x i2…x in ]Tfor the input value of the sample, the value of the sample,t i =[t i1,t i2…t in ]Tis the desired output value of the sample. Has the advantages ofLA hidden node andg(x) The mathematical model of the ELM as an excitation function can be expressed as:
Figure RE-110789DEST_PATH_IMAGE004
in the formula:
Figure RE-DEST_PATH_IMAGE005
is to connect the input sample with the second
Figure RE-496640DEST_PATH_IMAGE006
The weight vector of each hidden node is calculated,
Figure RE-DEST_PATH_IMAGE007
is connected to the first
Figure RE-103202DEST_PATH_IMAGE006
A weight vector of hidden nodes and output samples,b i is the first
Figure RE-96565DEST_PATH_IMAGE006
An offset of each hidden node;β i is the output weight;g(x) Is an excitation function;
Figure RE-539310DEST_PATH_IMAGE008
to representw i Andx j the inner product of (d).
As described aboveNThe matrix form of the equation can be written as:
Figure RE-DEST_PATH_IMAGE009
wherein the content of the first and second substances,His the output of the hidden node;βis the output weight;Tis the desired output.
HTo (1) aiColumn is the firstiThe output vector of each hidden layer node. Connection weight between hidden layer and output layerβCan be obtained using a least squares solution of the following system of equations:
Figure RE-214005DEST_PATH_IMAGE010
the solution can be expressed as:
Figure RE-DEST_PATH_IMAGE011
in the formula:H +is the generalized inverse of Moore-Penrose of the hidden layer output matrix.
And thirdly, single-step correction of the NWP irradiance can be realized in the last step, the correction time is short, and sometimes the prediction time cannot meet the requirement of the prediction time.
Therefore, in a rolling iterative manner, willtThe NWP irradiance correction value at the +1 moment is used as input, and then the correction value is applied totAnd correcting the NWP predicted irradiance at the +2 moment to realize multi-step correction of the NWP predicted irradiance, and obtaining a correction result for a longer time as shown in figure 3. And selecting a proper iteration step number according to the predicted time length to adapt to the prediction requirements of different time scales.
Selecting a root mean square error RMSE, an average absolute error MAE and an average absolute percentage error MAPE to evaluate the NWP irradiance correction result, and evaluating the subsequent power prediction precision by adopting the three indexes, wherein the calculation formula is as follows:
Figure RE-924341DEST_PATH_IMAGE001
Figure RE-354185DEST_PATH_IMAGE002
Figure RE-595811DEST_PATH_IMAGE003
in the formula:g a for the corrected irradiance/predicted power;g measured is the actual irradiance/measured power;nis the number of samples.
Thirdly, obtaining power prediction data by adopting the corrected irradiance based on the optimized power prediction model of the PSO-ELM. The specific process is as follows:
and i, establishing a photovoltaic power prediction model by adopting an ELM extreme learning machine algorithm, as shown in FIG. 4.
And ii, classifying the data according to the month, taking the actually measured historical meteorological data as input, taking the actually measured historical power as output, and training the prediction model.
And iii, optimizing the number of hidden layer nodes of the ELM by adopting a Particle Swarm Optimization (PSO), and searching the optimal number of hidden layer nodes by taking the minimum mean square error of the prediction result as an objective function of the PSO optimization so as to realize the optimization of the photovoltaic power prediction model.
Optimizing hidden layer node number in ELM algorithm according to fitness function by utilizing particle swarm optimizationL. By comparing the fitness function of each iteration particle, the particle speed and position are updated to obtain the optimal hidden layer node numberL best 。The velocity and position update formula for the particles is as follows:
Figure RE-808618DEST_PATH_IMAGE012
Figure RE-DEST_PATH_IMAGE013
in the formula:
Figure RE-137574DEST_PATH_IMAGE014
are particlesiIn the first placekFirst in +1 iterationsdVelocity in dimension;
Figure RE-DEST_PATH_IMAGE015
is the inertial weight;
Figure RE-472741DEST_PATH_IMAGE016
are particlesiIn the first placekIn the second iterationdVelocity in dimension;c 1、c 2is a learning factor;
Figure RE-DEST_PATH_IMAGE017
、
Figure RE-139345DEST_PATH_IMAGE018
random numbers which are uniformly distributed in the (0,1) interval;kis the iteration number;Pbest id i, the optimal position of the particle individual;
Figure RE-DEST_PATH_IMAGE019
are particlesiIn the first placekIn the second iterationdA position in a dimension;Gbest kd globally optimal position for the whole particle swarm;
Figure RE-405111DEST_PATH_IMAGE020
particlesiIn the first placekFirst in +1 iterationsdThe position in the dimension.
Iv, different prediction models are selected according to months, and the corrected NWP irradiance data and other meteorological information are input to obtain power prediction data.
Fourthly, calculating an error according to the power prediction data, and establishing an error prediction model based on a time sequence; and obtaining a plurality of error predicted values by a data iteration method, and combining the error predicted values with power predicted data to obtain a final photovoltaic power predicted value.
The embodiment takes annual data of a national key laboratory photovoltaic demonstration test power station of a new energy power system of North China Power university as an example, and the data sampling time is 15 minutes.
The photovoltaic power station power prediction method based on NWP irradiance correction and error prediction comprises the following steps:
the method includes the steps of obtaining historical operation data of the photovoltaic power station and NWP numerical weather forecast.
And establishing a NWP irradiance correction model to realize multi-step correction of the NWP irradiance.
Wherein: table 1 shows the results for different irradiance correction parameters. Comprehensively considering three error indexes and selecting parametersn=5 as parameter input for subsequent instance verification.
Table 1: different irradiance correction parameter results
Figure RE-DEST_PATH_IMAGE021
Thirdly, obtaining power prediction data by adopting the corrected irradiance based on the optimized power prediction model of the PSO-ELM.
Fourthly, calculating an error according to the power prediction data, and establishing an error prediction model based on a time sequence; and obtaining a plurality of error predicted values by a data iteration method, and combining the error predicted values with power predicted data to obtain a final photovoltaic power predicted value.
The predictive model was built according to the data relationships shown in fig. 5.X 1-X 4In order to be an input, the user can select,X 5is an output;X 2-X 5in order to be an input, the user can select,X 6and so on for output. A plurality of error predictors are obtained according to a data iteration method similar to that of fig. 3. And combining the error prediction result and the power prediction to obtain the final photovoltaic prediction power.
FIG. 6 is a graph comparing power prediction and measured power for steps 1, 4 and 8; table 2 shows the prediction evaluation indexes of the conventional ELM power prediction algorithm, the PSO-optimized ELM power prediction algorithm and the error correction-based PSO-ELM power prediction algorithm. As can be seen from fig. 6 and table 2, the prediction accuracy of the PSO-optimized ELM algorithm is higher than that of the conventional ELM algorithm, and the accuracy of the photovoltaic power generation power prediction is further improved after the error prediction model is added. The longer the prediction step number is, the longer the prediction time is, and the prediction precision is reduced to some extent; the smaller the number of prediction steps is, the lower the corresponding evaluation indexes are, which indicates that the ultra-short-term prediction has better precision.
TABLE 2 Power prediction results for different step counts
Figure RE-840771DEST_PATH_IMAGE022

Claims (3)

1. The photovoltaic power station power prediction method based on NWP irradiance correction and error prediction comprises the following steps:
acquiring historical operating data of a photovoltaic power station and NWP numerical weather forecast;
establishing an NWP irradiance correction model to realize multi-step correction of the NWP irradiance;
thirdly, obtaining power prediction data by adopting the corrected irradiance based on the optimized power prediction model of the PSO-ELM;
fourthly, calculating an error according to the power prediction data, and establishing an error prediction model based on a time sequence; and obtaining a plurality of error predicted values by a data iteration method, and combining the error predicted values with power predicted data to obtain a final photovoltaic power predicted value.
2. The photovoltaic power plant power prediction method based on NWP irradiance correction and error prediction as recited in claim 1, wherein: the multi-step NWP irradiance correction method comprises the following steps:
determining data parameters of the corrected modelnWill bet-n+1 totThe measured irradiance at a moment is used as a correction variable so as to carry out the NWP irradiance at the next momentCorrecting the lines;
selectingt-n+1,t-n+2…t-1, measured irradiance sum at time ttNWP forecast irradiance at +1 time is used as input to the model,tthe actually measured irradiance at the +1 moment is used as the output of the model, an ELM extreme learning machine algorithm is adopted to establish an irradiance correction model, and the aim of correcting the irradiance is achievedtCorrection of the predicted irradiance at time NWP + 1.
Thirdly, adopting a rolling iteration mode to performtThe NWP irradiance correction value at the +1 moment is used as input, and then the correction value is applied totCorrecting the NWP forecast irradiance at the +2 moment to realize multistep correction of the NWP forecast irradiance;
selecting a root mean square error RMSE, an average absolute error MAE and an average absolute percentage error MAPE to evaluate the NWP irradiance correction result, wherein the calculation formula is as follows:
Figure RE-RE-DEST_PATH_IMAGE001
Figure RE-655056DEST_PATH_IMAGE002
Figure RE-RE-DEST_PATH_IMAGE003
in the formula:g a for the corrected irradiance/predicted power;g measured is the actual irradiance/measured power;nis the number of samples.
3. The photovoltaic power plant power prediction method based on NWP irradiance correction and error prediction as recited in claim 1, wherein: the power prediction data in the step three is obtained according to the following method:
establishing a photovoltaic power prediction model by adopting an ELM extreme learning machine algorithm;
classifying data according to months, taking actual measurement historical meteorological data as input, taking actual measurement historical power as output, and training a prediction model;
iii, optimizing the number of hidden layer nodes of the ELM by adopting a Particle Swarm Optimization (PSO), and searching the optimal number of hidden layer nodes by taking the minimum mean square error of a prediction result as an objective function of the PSO optimization to realize the optimization of the photovoltaic power prediction model;
iv, different prediction models are selected according to months, and the corrected NWP irradiance data and other meteorological information are input to obtain power prediction data.
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CN115529002A (en) * 2022-11-28 2022-12-27 天津海融科技有限公司 Photovoltaic power generation power prediction method under various weather conditions
CN116050187A (en) * 2023-03-30 2023-05-02 国网安徽省电力有限公司电力科学研究院 TS fuzzy outlier self-correction method and system for second-level photovoltaic power prediction

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* Cited by examiner, † Cited by third party
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CN114676893A (en) * 2022-03-11 2022-06-28 中国长江三峡集团有限公司 Photovoltaic power station solar irradiance short-term prediction method based on optimal graph structure and storage medium
CN114676893B (en) * 2022-03-11 2024-02-02 中国长江三峡集团有限公司 Photovoltaic power station solar irradiance short-term prediction method based on optimal graph structure and storage medium
CN115529002A (en) * 2022-11-28 2022-12-27 天津海融科技有限公司 Photovoltaic power generation power prediction method under various weather conditions
CN116050187A (en) * 2023-03-30 2023-05-02 国网安徽省电力有限公司电力科学研究院 TS fuzzy outlier self-correction method and system for second-level photovoltaic power prediction

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