期刊
APPLIED SCIENCES-BASEL
卷 9, 期 15, 页码 -出版社
MDPI
DOI: 10.3390/app9153192
关键词
photovoltaic plant; power forecasting; recurrent neural networks; LSTM
类别
资金
- Fundamental Research Funds for the Central Universities [2018ZD05]
Photovoltaic (PV) power is attracting more and more concerns. Power output prediction, as a necessary technical requirement of PV plants, closely relates to the rationality of power grid dispatch. If the accuracy of power prediction in PV plants can be further enhanced by forecasting, stability of power grid will be improved. Therefore, a 1-h-ahead power output forecasting based on long-short-term memory (LSTM) networks is proposed. The forecasting output of the model is based on the time series of 1-h-ahead numerical weather prediction to reveal the spatio-temporal characteristic. The comprehensive meteorological conditions, including different types of season and weather conditions, were considered in the model, and parameters of LSTM models were investigated simultaneously. Analysis of prediction result reveals that the proposed model leads to a superior prediction performance compared with traditional PV output power predictions. The accuracy of output power prediction is enhanced by 3.46-13.46%.
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