Evaluation of opaque deep-learning solar power forecast models towards power-grid applications

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.