A CNN-LSTM Model for Electric Load Forecasting

This paper presents a novel approach to electric load forecasting by integrating multifactor prediction methods with time series analysis. The proposed CNN-LSTM model leverages historical electric load data for time series forecasting while incorporating various external factors to improve prediction accuracy. We construct a comprehensive feature pool, combining original load data with empirical variables such as temperature, seasonal trends, and holidays. The CNN component extracts salient features from the data, while the LSTM component captures temporal dependencies. Our experimental results, based on data from 26 transformers over 41 months, demonstrate that the CNN-LSTM model outperforms traditional methods, achieving higher accuracy in electric load forecasting.