期刊
RENEWABLE ENERGY
卷 198, 期 -, 页码 960-972出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2022.08.054
关键词
Solar power forecasting; Deep learning; Forecasting interpretability; Model evaluation; Solar photovoltaic; Integrated solar power system
资金
- National Natural Science Foundation of China [52077062]
Deep-learning solar power forecast models have improved prediction precision but sacrificed interpretability. This study aims to increase confidence in the practical engineering utilization of deep-learning-based intelligent models for solar power forecasting through evaluation and analysis of a typical model.
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 intel-ligence technologies, various deep-learning models have been developed with the benefits of improved predic-tion 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据