Day-Ahead Nonparametric Probabilistic Forecasting of Photovoltaic Power Generation Based on the LSTM-QRA Ensemble Model
Fei Mei, Jiaqi Gu, Jixiang Lu, Jinjun Lü, Jiatang Zhang, Yuhan Jiang, Tian Shi, Jianyong Zheng
Abstract
With the rapid growth of photovoltaic (PV) power in recent years, the stability of system operation, the performance of system contingency analysis as well as the power quality of the power grid are threatened by the inherent uncertainty and fluctuation of PV output. It is necessary to have the knowledge of PV output characteristics for reliable power system dispatching. Day-ahead PV power forecasting is an effective support for achieving optimal dispatching. Probabilistic forecasting can describe the uncertainty that is difficult to depict by deterministic forecasting, and the forecasting results are more comprehensive. An ensemble nonparametric probabilistic forecasting model of PV output is proposed based on the traditional deterministic forecasting method. Quantile regression averaging (QRA) is used to ensemble a group of independent long short-term memory (LSTM) deterministic forecasting models for obtaining the probabilistic forecasting of PV output. Real measured data are used to verify the effectiveness of this nonparametric probabilistic forecasting model. Additionally, in comparison with the benchmark methods, LSTM-QRA has higher prediction performance due to the better forecasting accuracy of independent deterministic forecasts.