Improvement in Solar-Radiation Forecasting Based on Evolutionary KNEA Method and Numerical Weather Prediction

Guosheng Duan, Lifeng Wu, Fa Liu, Yicheng Wang and Shaofei Wu
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Guosheng Duan: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Lifeng Wu: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Fa Liu: Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Science and Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Yicheng Wang: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Shaofei Wu: School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China

Sustainability, 2022, vol. 14, issue 11, 1-20

Abstract: Accurate forecasting of solar radiation (Rs) is significant to photovoltaic power generation and agricultural management. The National Centers for Environmental Prediction (NECP) has released its latest Global Ensemble Forecast System version 12 (GEFSv12) prediction product; however, the capability of this numerical weather product for Rs forecasting has not been evaluated. This study intends to establish a coupling algorithm based on a bat algorithm (BA) and Kernel-based nonlinear extension of Arps decline (KNEA) for post-processing 1–3 d ahead Rs forecasting based on the GEFSv12 in Xinjiang of China. The new model also compares two empirical statistical methods, which were quantile mapping (QM) and Equiratio cumulative distribution function matching (EDCDFm), and compares six machine-learning methods, e.g., long-short term memory (LSTM), support vector machine (SVM), XGBoost, KNEA, BA-SVM, BA-XGBoost. The results show that the accuracy of forecasting Rs from all of the models decreases with the extension of the forecast period. Compared with the GEFS raw Rs data over the four stations, the RMSE and MAE of QM and EDCDFm models decreased by 20% and 15%, respectively. In addition, the BA-KNEA model was superior to the GEFSv12 raw Rs data and other post-processing methods, with R 2 = 0.782–0.829, RMSE = 3.240–3.685 MJ m −2 d −1 , MAE = 2.465–2.799 MJ m −2 d −1 , and NRMSE = 0.152–0.173.

Keywords: forecasting; solar radiation; Global Ensemble Forecast System; bat algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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