Combination Forecasting Method of Short-term Photovoltaic Power Based on Weather Classification

被引：0

作者：

Ye L. ^{[1
]}

Pei M. ^{[1
]}

Lu P. ^{[1
]}

Zhao J. ^{[1
]}

He B. ^{[1
]}

机构：

[1] College of Information and Electrical Engineering, China Agricultural University, Beijing

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2021年 / 45卷 / 01期

关键词：

Clear-sky-like process of photovoltaic power; Combination forecasting; Fluctuation process of photovoltaic power; Granger causality analysis; Short-term photovoltaic (PV) power forecasting; Variational mode decomposition;

D O I：

10.7500/AEPS20200613003

中图分类号：

学科分类号：

摘要：

The fluctuation characteristics of the photovoltaic (PV) power are closely related to the weather types, and the short-term PV power forecasting has problems of low forecasting accuracy in the power fluctuation process and the weak correlation between meteorological factors and the power fluctuation process. This paper proposes a combination forecasting method of short-term PV power based on weather classification. Firstly, the weather process is divided into five types based on the meteorological factors and fluctuation characteristics of PV power. Based on the variational mode decomposition algorithm, the PV power is decomposed into the clear-sky-like process and the fluctuation process. Secondly, the Granger causality algorithm is used to select the key meteorological factors, which are closely related to the fluctuation process of PV power with various weather types. Finally, a combined forecasting model of short-term PV power based on weather classification is established. The model fully considers the specificity of the deep learning algorithm, separately forecasts the clear-sky-like process and the fluctuation process of PV power with various weather types. The simulation results show that the proposed short-term PV power forecasting method can significantly improve the accuracy of the short-term PV power forecasting. © 2021 Automation of Electric Power Systems Press.

引用

页码：44 / 54

页数：10

共 25 条

[1] GONG Yingfei, LU Zongxiang, QIAO Ying, Et al., An overview of photovoltaic energy system output forecasting technology, Automation of Electric Power Systems, 40, 4, pp. 140-151, (2016)
[2] GUERMOUI M, MELGANI F, GAIRAA K, Et al., A comprehensive review of hybrid models for solar radiation forecasting, Journal of Cleaner Production, (2020)
[3] ZHANG X N, FANG F, LIU J Z., Weather-classification-MARS-based photovoltaic power forecasting for energy imbalance market, IEEE Transactions on Industrial Electronics, 66, 11, pp. 8692-8702, (2019)
[4] KIM G G, CHOI J H, PARK S Y, Et al., Prediction model for PV performance with correlation analysis of environmental variables, IEEE Journal of Photovoltaics, 9, 3, pp. 832-841, (2019)
[5] YE Lin, CHEN Zheng, ZHAO Yongning, Et al., Photovoltaic power forecasting model based on genetic algorithm and fuzzy radial basis function neural network, Automation of Electric Power Systems, 39, 16, pp. 16-22, (2015)
[6] LONG Huan, ZHANG Zijun, SU Yan, Analysis of daily solar power prediction with data-driven approaches, Applied Energy, 126, pp. 29-37, (2014)
[7] Energy standard of the People's Republic of China: technical requirement of power forecasting system for PV power station: NB/T 32011-2013, (2014)
[8] ISHII T, OTANI K, TAKASHIMA T, Et al., Solar spectral influence on the performance of photovoltaic (PV) modules under fine weather and cloudy weather conditions, Progress in Photovoltaics: Research and Applications, 21, 4, pp. 481-489, (2013)
[9] LIMA F J L, MARTINS F R, PEREIRA E B, Et al., Forecast for surface solar irradiance at the Brazilian northeastern region using NWP model and artificial neural networks, Renewable Energy, 87, pp. 807-818, (2016)
[10] MOHD SHAH A S B, YOKOYAMA H, KAKIMOTO N., High-precision forecasting model of solar irradiance based on grid point value data analysis for an efficient photovoltaic system, IEEE Transactions on Sustainable Energy, 6, 2, pp. 474-481, (2015)

← 1 2 3 →