The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

Dina M Ibrahim; Abdulbasit Almhafdy; Amal A Al-Shargabi; Manal Alghieth; Ahmed Elragi; Francisco Chiclana

doi:10.7717/peerj-cs.856

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

PeerJ Comput Sci. 2022 Jan 26:8:e856. doi: 10.7717/peerj-cs.856. eCollection 2022.

Authors

Dina M Ibrahim^{1

2}, Abdulbasit Almhafdy³, Amal A Al-Shargabi¹, Manal Alghieth¹, Ahmed Elragi⁴, Francisco Chiclana⁵

Affiliations

¹ Department of Information Technology, College of Computer, Qassim University, Buraydah, Qassim, Saudi Arabia.
² Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt.
³ Department of Architecture, College of Architecture and Planning, Qassim University, Buraydah, Qassim, Saudi Arabia.
⁴ Department of Civil Engineering, College of Engineering, Qassim University, Buraydah, Qassim, Saudi Arabia.
⁵ Institute of Artificial Intelligence (IAI), Faculty of Technology, De Montfort University Leicester, Leicester, Leicester, United Kingdom.

Abstract

Prediction of building energy consumption is key to achieving energy efficiency and sustainability. Nowadays, the analysis or prediction of building energy consumption using building energy simulation tools facilitates the design and operation of energy-efficient buildings. The collection and generation of building data are essential components of machine learning models; however, there is still a lack of such data covering certain weather conditions. Such as those related to arid climate areas. This paper fills this identified gap with the creation of a new dataset for energy consumption of 3,840 records of typical residential buildings of the Saudi Arabia region of Qassim, and investigates the impact of residential buildings' eight input variables (Building Size, Floor Height, Glazing Area, Wall Area, window to wall ratio (WWR), Win Glazing U-value, Roof U-value, and External Wall U-value) on the heating load (HL) and cooling load (CL) output variables. A number of classical and non-parametric statistical tools are used to uncover the most strongly associated input variables with each one of the output variables. Then, the machine learning Multiple linear regression (MLR) and Multilayer perceptron (MLP) methods are used to estimate HL and CL, and their results compared using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and coefficient of determination (R²) performance measures. The use of the IES simulation software on the new dataset concludes that MLP accurately estimates both HL and CL with low MAE, RMSE, and R², which evidences the feasibility and accuracy of applying machine learning methods to estimate building energy consumption.

Keywords: Buildings characteristics; Cooling load; Energy consumption; Heating load; Statistical analysis.

Grants and funding

Qassim University, represented by the Deanship of Scientific Research, provided the financial support for this research under the number (coc-2019-2-2-I-5422) during the academic year 1440 AH/2019 AD. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.