Comparative Analysis of Hybrid Deep Learning Models for Electricity Load Forecasting During Extreme Weather

Extreme weather events present some of the most severe natural threats to the electric grid, and accurate load forecasting during those events is essential for grid management and disaster preparedness. In this study, we evaluate the effectiveness of hybrid deep learning (DL) models for electrical load forecasting in the IEEE 118-bus system. Our analysis focuses on the Connecticut region during extreme weather. In addition, we determine multivariate models capable of multi-input and multi-output forecasting while incorporating weather data to improve forecasting accuracy. This research is divided into two case studies that analyze different combined DL model architectures. Case Study 1 conducts CNN-Recurrent (RNN, LSTM, GRU, BiRNN, BiGRU, and BiLSTM) models with fully connected dense layers, which combine convolution and recurrent neural networks to capture both spatial and temporal dependencies in the data. Case Study 2 evaluates Hybrid CNN-Recurrent models with a fully connected dense layer model that incorporates a flattening step before the recurrent layers to increase the temporal learning process. Based on the results obtained from our simulations, the hybrid CNN-GRU-FC (using BiGRU) model in Case Study 2 obtained the best performance with an RMSE of 9.112 MW and MAPE of 11.68% during the hurricane period. The Hybrid CNN-GRU-FC model presents a better accuracy of bidirectional recurrent models for load forecasting under extreme weather conditions.