A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection
Xiaoying Ren,
Yongqian Liu,
Fei Zhang
et al.
Abstract:Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-br… Show more
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