期刊
NEUROCOMPUTING
卷 397, 期 -, 页码 415-421出版社
ELSEVIER
DOI: 10.1016/j.neucom.2019.09.110
关键词
Solar forecasting; Radial basis function neural network; Competitive swarm optimization; Meta-heuristic method
资金
- Horizon 2020 project (DRiVE Demand Response Integration tEchnologies: unlocking the demand response potential in the distribution grid) of European Commission
- Horizon 2020 project (TABEDE.TowArds Building rEady for Demand rEsponse) of European Commission
- China NSFC [51607177]
- Natural Science Foundation of Guangdong Province [2018A030310671]
- China Post-doctoral Science Foundation [2018M631005]
- outstanding young researcher innovation fund of SIAT, CAS [201822]
Solar power is an important renewable energy resource and acts as a major contributor to replacing fossil fuel generators and reducing carbon emissions. However, the intermittent power output due to the uncertain solar irradiance significantly challenges the economic integrations of solar generation within the existing power system, which calls for effective forecasting methods to improve the solar prediction accuracy. In this paper, a novel improved radial basis function neural network model is proposed and applied in forecasting the short-term solar power generation. A recent proposed meta-heuristic approach named competitive swarm optimization is adopted to train the non-linear and linear parameters of the radial basis function neural network model. The proposed model has been validated in nonlinear benchmark functions and then employed in forecasting the solar power generation of a real-world case study in the Netherlands. Numerical results demonstrate that the proposed competitive swarm optimized radial basis function neural network model could obtain higher accuracy compared to other counterparts and thus provides a useful tool for solar power forecasting. (C) 2020 Elsevier B.V. All rights reserved.
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