基于相似日选取和PCA-LSTM的光伏出力组合预测模型研究
RESEARCH ON PHOTOVOLTAIC OUTPUT COMBINATION PREDICTION MODEL BASED ON SIMILAR DAY SELECTION AND PCA-LSTM
孟亦康 1许野 1王鑫鹏 1王涛 1李薇1
作者信息
- 1. 华北电力大学环境科学与工程学院,北京 102206
- 折叠
摘要
构建一套融合主成分分析方法(PCA)、改进的K-均值聚类方法、动态时间规整算法(DTW)和长短期记忆神经网络(LSTM)的光伏出力组合预测模型.在运用PCA法提取气象要素的主成分因子的基础上,创新性地联合使用改进的K-均值聚类方法和DTW算法生成内部关联程度高且与待预测日的天气特征相近的历史日样本集;然后,结合LSTM神经网络,构建基于相似日选取的光伏发电功率预测模型,最终实现了云南某光伏电站发电功率的精准预测.与其他预测模型的对比结果显示,该文构建的组合预测模型具备更好的预测性能和广阔的应用前景.
Abstract
In this paper,a PV output portfolio forecasting model is constructed by integrating principal component analysis(PCA),an improved K-means clustering method,dynamic time warping(DTW),and a long-short term memory(LSTM)neural network.Based on the PCA method to extract the principal component factors of meteorological elements,the improved K-means clustering method and DTW algorithm are innovatively used to generate a set of historical day samples with a high degree of internal correlation and similar weather characteristics to the day to be predicted.Then,the LSTM neural network is combined to build a PV power prediction model based on the selection of similar days,which finally achieves the accurate prediction of power generation of a PV plant in Yunnan.The comparison results with other prediction models show that the combined prediction model constructed in this paper has better prediction performance and broad application prospects.
关键词
光伏电站/主成分分析/长短期记忆神经网络/预测模型/改进的K-均值聚类方法/动态时间规整算法Key words
PV power station/principal component analysis/long-short term memory/prediction model/improved K-means/dynamic time warping引用本文复制引用
出版年
2024
NETL
NSTL
万方数据