Load Forecasting with Hybrid Deep Learning Model for Efficient Power System Management

Aim: Load forecasting for efficient power system management. Background: Short-term energy load forecasting (STELF) is a valuable tool for utility companies and energy providers because it allows them to predict and plan for changes in energy. Method: 1D CNN BI-LSTM model incorporating convolutional layers. Result: The results provide the Root Mean Square Error of 0.952. The results shows that the proposed model outperforms the existing CNN based model with improved accuracy, hourly prediction, load forecasting. Conclusion: The proposed model has several applications, including optimal energy allocation and demand-side management, which are essential for smart grid operation and control. The model’s ability to accurately management forecast electricity load will enable power utilities to optimize their generation.