期刊
APPLIED SCIENCES-BASEL
卷 9, 期 19, 页码 -出版社
MDPI
DOI: 10.3390/app9193967
关键词
WRF (Weather Research and Forecasting) model; kalman filter; hybrid model; clearness index; solar radiation forecasting
类别
资金
- National Natural Science Foundation of China [41905084, 2018YFC1507000]
- Scientific Research Foundation of Chengdu University of Information Technology [KYTZ201813]
Day-ahead forecasting of solar radiation is essential for grid balancing, real-time unit dispatching, scheduling and trading in the solar energy utilization system. In order to provide reliable forecasts of solar radiation, a novel hybrid model is proposed in this study. The hybrid model consists of two modules: a mesoscale numerical weather prediction model (WRF: Weather Research and Forecasting) and Kalman filter. However, the Kalman filter is less likely to predict sudden changes in the forecasting errors. To address this shortcoming, we develop a new framework to implement a Kalman filter based on the clearness index. The performance of this hybrid model is evaluated using a one-year dataset of solar radiation taken from a photovoltaic plant located at Maizuru, Japan and Qinghai, China, respectively. The numerical results reveal that the proposed hybrid model performs much better in comparison with the WRF-alone forecasts under different sky conditions. In particular, in the case of clear sky conditions, the hybrid model can improve the forecasting accuracy by 95.7% and 90.9% in mean bias error (MBE), and 42.2% and 26.8% in root mean square error (RMSE) for Maizuru and Qinghai sites, respectively.
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