Prediction of Electricity Consumption Using Interpretable Machine Learning Approach

The continuously increasing demand for electricity presents significant issues for policymakers and utility companies. An accurate forecast of electricity usage is critical for effective energy management. This thesis provides an interpretable machine-learning approach for forecasting energy demand that incorporates macroeconomic variables such as GDP, inflation, and industrial growth. The study uses data from the World Development Indicators from 1973 to 2021 and employs the multi-model. Our findings emphasize the importance of economic considerations on electricity demand, resulting in a reliable method for forecasting energy consumption. This study adds to the body of knowledge by providing a clear model to aid in decision-making processes connected to energy management and policy creation.