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Spatio-temporal PV power forecasting considering the time-shift correction and the information fusion strategy of multi-stations.

Authors :
Yang, Xiyun
Yang, Yan
Meng, Lingzhuochao
Zhao, Ya
Source :
ISA Transactions; Aug2023, Vol. 139, p376-390, 15p
Publication Year :
2023

Abstract

Accurate prediction of PV power is essential to ensuring the safe and economic operation of power systems with high PV penetration. The current PV power prediction scheme considering the spatio-temporal correlation characteristics is relatively simple in data processing, resulting in low prediction accuracy; at the same time, the missing data also poses a great problem to the prediction. Therefore, in order to improve the prediction accuracy and solve the problem of missing data, this paper proposes a PV power spatio-temporal prediction model considering time-shift correction and a multi-station information fusion strategy Firstly, relevant power station clusters are constructed using hierarchical clustering, and a similar daily data filtering model considering the variation characteristics of daily power characteristic curves is proposed to filter the data; Secondly, multiple BP neural network models are constructed and multiple reference power stations with high relevance are predicted using irradiance information; Thirdly, the prediction results of multiple reference power stations are input to the data processing module for time-shift analysis and spatial correlation information fusion correction, which solves the missing data problem of the target power station to be predicted. Finally, it is input to One-dimensional Convolutional Neural Network(1DCNN) to achieve the power prediction of the target power station with missing data. The simulation analysis shows that the root mean square error (RMSE) of a sunny day forecast is 3.31%; the RMSE of a non-sunny day forecast is 9.65%, which proves the accuracy of this two-layer neural network is higher compared to other model structures, so the proposed scheme has certain reliability and accuracy in the prediction of PV power with missing data. • The paper examines the current issues and challenges with PV power forecasting. • A similar daily data filter considering the various characteristics of the daily PV power output is proposed. • A BP-1DCNN network considering spatio-temporal sharing is designed to predict the PV power of the missing data plants. • A time-shift correction and spatially relevant information fusion strategy are used to connect the two networks. • The spatio-temporally corrected network prediction model has higher accuracy and solves the problem of missing data. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00190578
Volume :
139
Database :
Supplemental Index
Journal :
ISA Transactions
Publication Type :
Academic Journal
Accession number :
169968797
Full Text :
https://doi.org/10.1016/j.isatra.2023.03.047