@article{Yang2026, 
author = {Mao Yang and Kaixuan Wang and Xin Su and Miaomiao Ma and Gang Wu and Dawei Huang},
title = {Short-term Photovoltaic Output Probability Prediction Method Considering Spatio-temporal-condition Dependence of Prediction Error},
year = {2026},
journal = {CSEE Journal of Power and Energy Systems},
volume = {12},
number = {3},
pages = {1386-1399},
keywords = {ASU, conditional-temporal dependency, LSTM, prediction error, probability prediction, spatio–temporal dependence},
url = {https://www.sciopen.com/article/10.17775/CSEEJPES.2022.02360},
doi = {10.17775/CSEEJPES.2022.02360},
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 > 3%. Moreover, conditional dependency and temporal correlation of the updated prediction error are better than those of conventional interval prediction.}
}