Short-term load forecasting using Artificial Bee Colony Optimization with Deep Learning Model on Power Systems

In power systems, Short-term load prediction is a serious element of effective energy management as well as grid operation. It contains forecast of power demand over a quite short time prospect that ranges from hours to few days in advance. Exact load forecasting is vital for guaranteeing a steady and trustworthy power supply, enhancing generation and distribution, as well as allowing utilities to create knowledgeable choices about resource allocation and demand response plans. Leveraging historical information, climate situations, time of day, and other pertinent factors, advanced machine learning (ML) techniques used in order to offer exact load forecasts, assist grid operators in justifying imbalances, decrease operational costs, as well as improving general grid reliability. Therefore, this study presents a Short-term load forecasting using Artificial Bee Colony Optimization with Deep Learning (STLF-ABCDL) model on Power Systems. The STLF-ABCDL technique incorporates a multiple-stage procedure, starting with min-max normalization-based pre-processing for assuring data consistency. Besides, the STLF-ABCDL technique applies Long Short-Term Memory (LSTM) for forecasting load. For boosting efficacy of LSTM model, the ABC algorithm can be utilized to tune the parameters and optimize network hyperparameters. The presented STLFABCDL technique exhibits considerable enhancements in the load prediction results, providing enhanced stability and optimal resource allocation.