基于数值天气预报因子扩充和改进集成学习的高寒地区短期光伏功率预测OA

Due to meteorological conditions,photovoltaic systems in high-altitude and cold regions exhibit more significant fluctuations in their photovoltaic power.This article takes a photovoltaic power station in Heilongjiang as an example and proposes a short-term photovoltaic power prediction method for high-altitude and cold regions based on numerical weather prediction(NWP)factor expansion and improved conventional Stacking ensemble learning.In response to the large fluctuations in photovoltaic power in high-altitude and cold regions,the NWP differential factor is introduced as a cross feature to enhance the sensitivity of the model to weather changes.Subsequently,an ensemble learning model is constructed using extreme gradient boosting(XGBoost)and long short term memory(LSTM)network as base learners,and temporal convolutional network(TCN)as meta learners,and the model structure is optimized using forward validation.Finally,comparative experimental analysis is conducted,and the results show that the proposed method has higher prediction accuracy and stability.