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Regression analysis for prediction of residential energy consumption
Renewable and Sustainable Energy Reviews (2015)
  • M. A. Rafe Biswas, University of Texas at Tyler
  • Nelson Fumo, University of Texas at Tyler
The considerable amount of energy consumption associated to the residential sector justifies and supports energy consumption modeling efforts. Among the three approaches to develop energy models, statistical approaches are a good option to avoid the burden associated to engineering approaches when observed/measured data is available. Among the statistical models, the linear regression analysis has shown promising results because of the reasonable accuracy and relatively simple implementation when compared to other methods. In this study, simple and multiple linear regression analysis along with a quadratic regression analysis were performed on hourly and daily data from a research house. The time interval of the observed data showed to be a relevant factor defining the quality of the model. Multiple linear regression models using the outdoor temperature and solar radiation offered improved coefficient of determination, but deteriorated root mean square error emphasizing the importance of using both parameters to assess and compare models. The content and structure of the paper has been devised to become a comprehensive material to be considered as the starting point for future work in this interesting research area. This paper also conveys the authors׳ belief that the future of residential energy forecasting is moving toward the development of individual models for each household due to the availability of data from smart meters, as well as the development of friendly and easy-to-use engineering software.
  • Energy consumption,
  • Residential buildings,
  • Linear regression,
  • Energy regression models
Publication Date
July, 2015
Publisher Statement
First published in Renewable and Sustainable Energy Reviews.
Citation Information
Fumo, N., & Biswas, M. A. R. (2015). Regression analysis for prediction of residential energy consumption. Renewable & Sustainable Energy Reviews, 47, 332–343.