Hybrid load forecasting considering energy efficiency and renewable energy using neural network

In recent years, the relationship between a country's gross domestic product (GDP) and its electricity consumption has changed significantly due to increased energy efficiency (EE) and renewable energy (RE) adoption. This decoupling disrupts conventional load forecasting models, affecting utility companies. This study has developed an innovative solution using an artificial neural network (ANN) Hybrid method for load forecasting, resulting in a remarkably accurate model with 99.68% precision. Applying this model to Malaysia's electricity consumption from 2020 to 2040 reveals a significant 13% reduction when accounting for EE and RE trends. This method aids risk management, contingency planning, and decision-making by accurately reflecting changing energy usage dynamics influenced by EE and RE sources.