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

Saleh Seyedzadeh, Farzad Pour Rahimian, Ivan Glesk, Marc Roper

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.
LanguageEnglish
Number of pages20
JournalVisualization in Engineering
Volume6
Issue number5
Early online date2 Oct 2018
DOIs
Publication statusPublished - 31 Dec 2018

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Energy Consumption
Learning systems
Machine Learning
Energy utilization
Energy
Greenhouses
Energy management
Forecasting
Gas emissions
Fossil fuels
Fuel consumption
Support vector machines
Decision making
Greenhouse
Energy Management
Neural networks
Support Vector Machine
Sector
Regression
Decision Making

Keywords

  • building energy consumption
  • building energy eciency
  • energy benchmarking
  • machine learning

Cite this

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Machine learning for estimation of building energy consumption and performance : a review. / Seyedzadeh, Saleh; Pour Rahimian, Farzad; Glesk, Ivan; Roper, Marc.

In: Visualization in Engineering, Vol. 6, No. 5, 31.12.2018.

Research output: Contribution to journalArticle

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T1 - Machine learning for estimation of building energy consumption and performance

T2 - Visualization in Engineering

AU - Seyedzadeh, Saleh

AU - Pour Rahimian, Farzad

AU - Glesk, Ivan

AU - Roper, Marc

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AB - 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.

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KW - energy benchmarking

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