宁夏电力Issue(2)：33-39,45,8.DOI:10.3969/j.issn.1672-3643.2024.02.006
基于小波变换和长短期记忆神经网络的电力负荷预测
Power load forecasting based on wavelet transform and long short-term memory neural network
摘要
Abstract
The power system requires an immediate balance between the generated power and the electricity load,which is characterized by non-linearity,time variability,and uncertainty.To address this issue,this paper proposes a combined forecasting model that integrates wavelet transform(WT)and long short-term memory(LSTM)neural net-works,considering the impact of weather and date types for short-term power load forecasting.Initially,the wavelet trans-form is employed for feature extraction signal denoising to reduce data volatility.Then,the preprocessed data is trained using an LSTM network,and the output results undergo sequence reconstruction for the final load forecast.Finally,the data of WT-LSTM combined forcasting model is seperately compared with that of the BP neural network and LSTM model.The results show that the WT-LSTM neural network combined prediction model has the superior predictive per-formance,significantly enhancing forecasting precision.关键词
小波变换/长短期记忆神经网络/负荷预测/电力系统/预测效果Key words
wavelet transform/long short-term memory neural network/load forecasting/power system/forecasting precision分类
信息技术与安全科学引用本文复制引用
叶梁劲,廖晓辉,李建树,刘思佳..基于小波变换和长短期记忆神经网络的电力负荷预测[J].宁夏电力,2024,(2):33-39,45,8.基金项目
河南省自然科学基金项目(232300421198) （232300421198）
