Vol. 5 No. 1 (2025): Issue 5
Articles

Energy Consumption Prediction using Support Vector Regression

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  • Emily Carter,
  • Samuel Liu,
  • Isabel Thompson
Emily Carter
School of Energy Systems and Engineering, Thompson Rivers University, Kamloops, V2C 0C8, Canada
Samuel Liu
Centre for Sustainable Energy Research, Cape Breton University, Sydney, B1P 6L2, Canada
Isabel Thompson
Department of Environmental and Energy Sciences, Laurentian University, Greater Sudbury, P3E 2C6, Canada
DOI: https://doi.org/10.71070/es.v5i1.88

Published 2025-02-22

Keywords

  • Energy Consumption,
  • Prediction Models,
  • Support Vector Regression,
  • Energy Management,
  • Forecasting Technology

How to Cite

Carter, E., Liu, S., & Thompson, I. (2025). Energy Consumption Prediction using Support Vector Regression. Energy & System, 5(1). https://doi.org/10.71070/es.v5i1.88

Abstract

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.

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