Revolutionizing Solar Power Forecasts by Correcting the Outputs of the WRF-SOLAR Model

Cheng-Liang Huang, Yuan-Kang Wu (), Chin-Cheng Tsai, Jing-Shan Hong and Yuan-Yao Li
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Cheng-Liang Huang: Department of Electrical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan
Yuan-Kang Wu: Department of Electrical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan
Chin-Cheng Tsai: Meteorology and Information Center, Central Weather Bureau, Taipei 100006, Taiwan
Jing-Shan Hong: Meteorology and Information Center, Central Weather Bureau, Taipei 100006, Taiwan
Yuan-Yao Li: Department of Chemical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan

Energies, 2023, vol. 17, issue 1, 1-18

Abstract: Climate change poses a significant threat to humanity. Achieving net-zero emissions is a key goal in many countries. Among various energy resources, solar power generation is one of the prominent renewable energy sources. Previous studies have demonstrated that post-processing techniques such as bias correction can enhance the accuracy of solar power forecasting based on numerical weather prediction (NWP) models. To improve the post-processing technique, this study proposes a new day-ahead forecasting framework that integrates weather research and forecasting solar (WRF-Solar) irradiances and the total solar power generation measurements for five cities in northern, central, and southern Taiwan. The WRF-Solar irradiances generated by the Taiwan Central Weather Bureau (CWB) were first subjected to bias correction using the decaying average (DA) method. Then, the effectiveness of this correction method was verified, which led to an improvement of 22% in the forecasting capability from the WRF-Solar model. Subsequently, the WRF-Solar irradiances after bias correction using the DA method were utilized as inputs into the transformer model to predict the day-ahead total solar power generation. The experimental results demonstrate that the application of bias-corrected WRF-Solar irradiances enhances the accuracy of day-ahead solar power forecasts by 15% compared with experiments conducted without bias correction. These findings highlight the necessity of correcting numerical weather predictions to improve the accuracy of solar power forecasts.

Keywords: bias correction; solar irradiance prediction; decaying average; solar power forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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