Forecasting energy consumption using machine learning: a case study of Kyrgyzstan using socioeconomic data

Abstract

As global energy demands soar, understanding and accurately forecasting energy consumption becomes crucial for both economic stability and sustainable development. This paper explores the application of machine learning techniques to predict energy consumption, using the "World Energy Consumption" dataset from Kaggle. Focusing on Kyrgyzstan as a case study, we investigate how factors such as energy per capita, population, and GDP influence energy demand. Employing a regression model, we evaluate the effectiveness of socioeconomic indicators in predicting energy needs. Results demonstrate the potential for these methods to contribute to energy management strategies, though limitations point toward the need for more complex models and broader datasets.