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 networks

considering the impact of weather and date types for short-term power load forecasting.Initially

the wavelet transform 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 performance

significantly enhancing forecasting precision.