Energy Consumption Prediction using Support Vector Regression

Energy consumption prediction is a crucial area of research due to its significant impact on energy efficiency and sustainability. Current research on this topic faces challenges in accurately forecasting energy usage patterns, limiting the effectiveness of energy management systems. This paper proposes a novel approach utilizing Support Vector Regression (SVR) to improve the accuracy of energy consumption prediction models. The study explores the integration of SVR with historical energy data and external factors to enhance the predictive capabilities of the model. The innovative methodology presented in this paper aims to address the limitations of existing prediction techniques and contribute to the advancement of energy forecasting technology.