Evaluation of opaque deep-learning solar power forecast models towards power-grid applications. (October 2022)

• Record Type: Journal Article
• Authors: Cheng, Lilin; Zang, Haixiang; Wei, Zhinong; Zhang, Fengchun; Sun, Guoqiang
• Is Part Of: Renewable energy. Volume 198(2022)
• Page Start: 960
• Page End: 972
• Publication Date: 2022-10
• Subjects: Solar power forecasting -- Deep learning -- Forecasting interpretability -- Model evaluation -- Solar photovoltaic -- Integrated solar power system; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2022.08.054
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Solar photovoltaic power plays a vital role in global renewable energy power generation, and an accurate solar power forecast can further promote applications in integrated power systems. Due to advanced artificial intelligence technologies, various deep-learning models have been developed with the benefits of improved prediction precision, but these models inevitably sacrifice their interpretability compared to linear methods. Since a 100% accurate forecast is impossible to achieve, an opaque black-box model will always raise doubts for the operators of renewable power-grids, especially when the prediction deviation may produce higher economic costs and even a system turbulence. Motivated by this, the present study summarizes the requirements of deep-learning solar power forecast models from the power-grid application perspective. Post-hoc evaluation and discussion are conducted to analyze the performances of a typical deep-learning benchmark model based on open-access dataset for solar forecasting. Based on the results, the aim of this study is to increase confidence of deep-learning-based intelligent models into the practical engineering utilization of solar power forecasting. The case studies indicate that some simple evaluation procedures can aid a better understanding of the factors that influence the performances of opaque models, and these procedures can help in the design methods for model modifications.