TENG Xiaobi， GU Jie， QIN Kangping， MIAO Yuancheng， SONG Bingbing， LI Yuyou， WEN Honglin
With the advancement of the new power system construction, wind and photovoltaic (PV) energy, as vital pillars of future power supply, play a crucial role in enhancing the reliability of power systems and facilitating the deep integration of clean energy into the electricity market. The resource endowment characteristics lead to significant volatility and randomness in PV power generation, making dynamic modeling of its uncertainty essential for ensuring the safe and economic operation of the power grid and for the efficient utilization of PV energy. Additionally, factors such as dust accumulation on the surface of PV modules and the aging of inverters can affect the power output characteristics of PV equipment, causing the parameters of PV power prediction models to vary over time. Traditional offline learning methods for prediction would result in considerable errors under such conditions. In this paper, we propose a new method for short-term PV power probabilistic forecasting based on online Gaussian processes. First, we introduce a variable selection technique combining orthogonalized maximal information coefficient with feature collaboration, aiming to identify meteorological factors that significantly impact PV power output. Then, we employ Gaussian process models to characterize the complex mapping relationships in PV power prediction. Finally, we use online algorithms to update the parameters of the PV prediction model in real-time, addressing the time-varying nature of parameters due to dust accumulation and inverter aging. Tests and validations using the PV power dataset from the 2014 Global Energy Forecasting Competition demonstrate that our proposed model performs well in point forecasting, interval forecasting, and probabilistic forecasting. It also exhibits strong adaptability, effectively handling dynamic system issues such as time-varying model parameters.
