Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output Powers

Mao Yang; Xiaoxuan Shen; Dawei Huang; Xin Su

doi:10.17775/CSEEJPES.2022.03760

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Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output Powers

Regular Papers | 更新时间：2025-12-18

- Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output Powers
- Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output Powers
- 中国电机工程学会电力与能源系统学报(英文) 2025年11卷第2期页码：661-670
- 作者机构：
  
  Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University,Jilin,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.03760
  中图分类号：
- 纸质出版：2025
- 稿件说明：
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Mao Yang, Xiaoxuan Shen, Dawei Huang, 等. Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output Powers[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(2):661-670.

Mao Yang, Xiaoxuan Shen, Dawei Huang, et al. Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output Powers[J]. CSEE Journal of Power and Energy Systems, 2025, 11(2): 661-670.
Mao Yang, Xiaoxuan Shen, Dawei Huang, 等. Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output Powers[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(2):661-670. DOI： 10.17775/CSEEJPES.2022.03760.

Mao Yang, Xiaoxuan Shen, Dawei Huang, et al. Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output Powers[J]. CSEE Journal of Power and Energy Systems, 2025, 11(2): 661-670. DOI： 10.17775/CSEEJPES.2022.03760.

摘要

Abstract

In recent years

a spurt of photovoltaic power generation has brought certain impact on stability of the power system

which puts forward higher requirements on accuracy of photovoltaic power prediction. Therefore

this paper proposes a hybrid power prediction model based on fluctuation classification and feature factor extraction. First

based on fluctuation characteristics of photovoltaic power

fluctuation classification is applied to forecast power before the day

and weather is divided into complex fluctuation types and simple types. Then

parallel factor algorithm is used to reduce prediction model redundancy

which can reduce high-dimensional numerical weather prediction feature matrix to extract relevant features. Finally

the Long Short-Term Memory (LSTM) deep learning model is used to forecast very short-term photovoltaic power. The proposed hybrid model is compared with other methods

and photovoltaic data from several sites are selected for comparison and validation in this paper. Simulation results show that very short-term prediction method of photovoltaic power proposed in this paper can significantly improve prediction accuracy.

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