Electricity load forecasting models based on LSTM and GRU with their bidirectional recurrent neural networks

Accurate electricity load forecasting is crucial for power system planning, reliability, and sustainability, enabling more efficient markets and reduced greenhouse gas emissions. This study leverages deep learning algorithms, specifically bidirectional recurrent neural networks, to develop a unified model for predicting one day-ahead electricity demand for the entire year of 2023. The model's performance was evaluated on a monthly basis, allowing for a detailed assessment of its forecasting capabilities across different time periods. Four neural network algorithms were compared: Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU. The GRU model demonstrated superior performance, achieving an R-squared value of 0.8526 in October and a Mean Absolute Percentage Error (MAPE) of 2.34% in March. These results highlight the potential of the proposed model as an effective tool for electricity demand forecasting, supporting the integration of renewable energy sources and enhancing grid resilience.