电气技术2024，Vol.25Issue(8)：1-10,17,11.
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
Short term photovoltaic power prediction in high-altitude and cold regions based on numerrical weather prediction factor expansion and improved ensemble learning
刘伟 1杨凯宁1
作者信息
- 1. 东北石油大学电气信息工程学院,黑龙江 大庆 163000
- 折叠
摘要
Abstract
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.关键词
光伏功率短期预测/高寒地区/Stacking集成学习/数值天气预报(NWP)差分因子/前向验证Key words
short term prediction of photovoltaic power/high-altitude and cold regions/Stacking ensemble learning/numerical weather prediction(NWP)difference factor/forward validation引用本文复制引用
刘伟,杨凯宁..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[J].电气技术,2024,25(8):1-10,17,11.
