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