Accurate prediction of regional electricity sales is crucial for the power sector’s effective energy management and planning. Existing forecasting models largely rely on historical electricity sales data and partially incorporate temperature effects

yet they inadequately consider a broad range of meteorological factors. In response

it introduces a novel forecasting method combining multi- head attention mechanisms with long short-term memory networks（MHAM-LSTM）for regional electricity sales. Initially

key variables are identified and redundant variables are eliminated through correlation analysis. Subsequently

the multi-head attention mechanism is used to focus on the key indicators that have an important impact on electricity sales. Finally

the LSTM network delves into the latent patterns of time-series data to forecast regional electricity sales. Experimental results show that the MHAM-LSTM model surpasses comparative models

including random forest

deep neural networks

long short-term memory networks

temporal convolutional networks

and transformer

in electricity sales forecasting accuracy

demonstrating significant performance advantages. Additionally

the analysis of meteorological factor importance reveals that incorporating multiple meteorological variables

particularly temperature

wind speed

and humidity

plays a crucial role in improving prediction accuracy.