A hybrid long-term industrial electrical load forecasting model using optimized ANFIS with gene expression programming

Abstract

Electric energy demand forecasting is vital in contemporary power systems, especially amidst market deregulation trends and the increasing influence of industrial customers on power dynamics. However, existing forecasting models encounter challenges such as slow convergence and high complexity. Addressing these issues, this study proposes a hybrid forecasting model that combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gene Expression Programming (GEP) to enhance predictions of electrical energy consumption. Validated using real-time monthly electrical load data from an industrial user in Uganda, the hybrid model outperforms individual ANFIS and GEP models, demonstrating reduced errors and minimal computation time. The application of this hybrid model presents promising results, showcasing exceptional predictive capabilities and offering potential improvements in efficiency and precision for electrical energy consumption forecasting amidst market deregulation and evolving industrial dynamics.