Load Forecasting Method for Power Distribution Networks Oriented towards Time Series Simulation with Deep Learning Method

Load forecasting is a critical component of time series simulation in power systems, essential for the reliability and accuracy of simulations. With the integration of renewable energy sources such as photovoltaics, power systems face increasingly complex load forecasting challenges. This paper introduces a deep learning approach that combines Long Short-Term Memory networks (LSTM) and Attention Mechanisms (AM) to enhance the precision and reliability of load forecasting in power distribution networks. Utilizing electric load data from a specific region in China, the LSTM-AM model captures long-term dependencies in time-series data and highlights the impact of critical periods on forecasting accuracy. Experimental results demonstrate that the LSTM-AM model surpasses traditional Back Propagation neural networks, CNNs, and standard LSTM models in terms of prediction precision, affirming the potential application of the proposed method in the field of electric load forecasting. Moreover, the paper introduces a similar day selection strategy to distinguish between weekdays and weekends, reducing RMSE and MAE from 22.6 MW and 15.1 MW to 20.1 MW and 13.9 MW, respectively, thereby further optimizing the accuracy of the model