Optimizing Short-Term Electrical Demand Forecasting with Deep Learning and External Influences

Short-term electrical demand forecasting is crucial for the efficient operation of modern power grids. Traditional methods often fail by neglecting system nonlinearities and external factors that influence electricity consumption. In this study, we propose an enhanced deep learning-based forecasting model that integrates external factors such as meteorological data and economic indicators to improve prediction accuracy. Using an ISO NE (Independent System Operator New England) dataset from 2017 to 2019, we analyze 23 independent variables to assess their impact on model performance. Our findings demonstrate that careful variable selection reduces dimensionality while maintaining forecasting accuracy, enabling the effective application of deep learning models. The CNN plus LSTM composite model achieved the lowest prediction error of 0.15%, outperforming standalone CNN (0.8%) and LSTM (1.44%) approaches. The combination of CNN's feature extraction capabilities with LSTM's strength in handling time series data was instrumental in achieving superior performance. Our results highlight the importance of incorporating external influences in electricity demand forecasting and suggest future directions for developing more precise and efficient models.