Machine learning for estimation of building energy consumption and performance : a review

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Seyedzadeh, Saleh and Pour Rahimian, Farzad and Glesk, Ivan and Roper, Marc (2018) Machine learning for estimation of building energy consumption and performance : a review. Visualization in Engineering, 6 (5). ISSN 2213-7459 (https://doi.org/10.1186/s40327-018-0064-7)

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Abstract

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.

ORCID iDs

Seyedzadeh, Saleh ORCID logoORCID: https://orcid.org/0000-0001-6017-289X, Pour Rahimian, Farzad ORCID logoORCID: https://orcid.org/0000-0001-7443-4723, Glesk, Ivan ORCID logoORCID: https://orcid.org/0000-0002-3176-8069 and Roper, Marc ORCID logoORCID: https://orcid.org/0000-0001-6794-4637;
CORE (COnnecting REpositories)