Dandan Xu1, Haijian Shao1,*, Xing Deng1,2, Xia Wang3
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 567-597, 2022, DOI:10.32604/cmes.2022.019245
- 14 March 2022
Abstract As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind
and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach
with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for
forecasting wind power and PV production. The physical model, statistical learning method, and machine learning
approaches based on historical data are all evaluated for the forecasting of wind power and PV production. More >
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