Power load forecasting based on wavelet transform and long short-term memory neural network
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.
国家科技期刊平台
NSTL
万方数据
维普
