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

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

doi:10.1186/s40327-018-0064-7

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 efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient 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, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.

Highlights

MAIN TEXT Introduction Emission of greenhouse gases including carbon dioxide (CO2) in higher layers of the atmosphere are known as the main cause of global warming phenomena
An annual saving of 60 billion Euros is estimated by improvement of European Union (EU) buildings’ energy performance by 20 percent (Li et al 2010)
In the UK and some European countries, the rate of demolition of existing buildings and constructing new ones is very low as 0.1 percent, whilst having new buildings rate of over 1 percent

Summary

MAIN TEXT

Introduction Emission of greenhouse gases including carbon dioxide (CO2) in higher layers of the atmosphere are known as the main cause of global warming phenomena. The statistical methods use building historical data and frequently apply regression to model the energy consumption/performance of buildings. In 1995, an early study on the application of ANN in prediction of energy consumption using simple FFN model was performed to forecast electric energy usage of a building in tropical climate based on the occupancy and temperature data. Three single output ANN is developed to predict primary energy consumption of space heating and cooling and the ratio of yearly discomfort hours by setting whole-building parameters as network inputs (i.e. geometry, envelope, operation and HVAC). The research investigates the impact of using hidden layer showing an insignificant difference in accuracy of the models It reveals that external temperature is more important than humidity and solar radiation in estimating energy consumption of the study case. W is the weight vector and approximated by empirical risk function as:

Minimise : W

Findings

Conclusion