Short-Term Photovoltaic Output Probability Prediction Method Considering Spatio-Temporal-Condition Dependence of Prediction Error

Mao Yang; Kaixuan Wang; Xin Su; Miaomiao Ma; Gang Wu; Dawei Huang

doi:10.17775/CSEEJPES.2022.02360

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Short-Term Photovoltaic Output Probability Prediction Method Considering Spatio-Temporal-Condition Dependence of Prediction Error

Regular Papers | 更新时间：2026-06-12

- Short-Term Photovoltaic Output Probability Prediction Method Considering Spatio-Temporal-Condition Dependence of Prediction Error
- Short-Term Photovoltaic Output Probability Prediction Method Considering Spatio-Temporal-Condition Dependence of Prediction Error
- 中国电机工程学会电力与能源系统学报(英文) 2026年12卷第3期页码：1386-1399
- 作者机构：
  
  1. Modern Power System Simulation Control & Renewable Energy Technology Key Laboratory of the Ministry of Education, Northeast Electric Power University,Jilin,China
  2. State Grid Jilin Electric Province Power Co., Ltd.,Changchun,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.02360
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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Mao Yang, Kaixuan Wang, Xin Su, 等. Short-Term Photovoltaic Output Probability Prediction Method Considering Spatio-Temporal-Condition Dependence of Prediction Error[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(3):1386-1399.

Mao Yang, Kaixuan Wang, Xin Su, et al. Short-Term Photovoltaic Output Probability Prediction Method Considering Spatio-Temporal-Condition Dependence of Prediction Error[J]. CSEE Journal of Power and Energy Systems, 2026, 12(3): 1386-1399.
Mao Yang, Kaixuan Wang, Xin Su, 等. Short-Term Photovoltaic Output Probability Prediction Method Considering Spatio-Temporal-Condition Dependence of Prediction Error[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(3):1386-1399. DOI： 10.17775/CSEEJPES.2022.02360.

Mao Yang, Kaixuan Wang, Xin Su, et al. Short-Term Photovoltaic Output Probability Prediction Method Considering Spatio-Temporal-Condition Dependence of Prediction Error[J]. CSEE Journal of Power and Energy Systems, 2026, 12(3): 1386-1399. DOI： 10.17775/CSEEJPES.2022.02360.

摘要

Abstract

In a particular time scale (0–6 h)

prediction errors of photovoltaic (PV) power stations are spatio-temporally dependent on surrounding power stations. From the point of view of power stations themselves

they have a conditional-temporal dependency. Therefore

considering these characteristics

prediction accuracy of the PV output can be improved effectively. A short-term PV output probability prediction method is developed based on appearance similarity updating (ASU) and a long short-term memory network (LSTM). Results from Jilin and Inner Mongolia regions show that accuracy of the prediction method considering temporal and spatial dependence of the prediction error is higher than that of a prediction method considering only a single property

and annual root mean square error can be reduced by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$$ 3%. Moreover

conditional dependency and temporal correlation of the updated prediction error are better than those of conventional interval prediction.

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