An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings

Samira Rastbod, Farnaz Rahimi, Yara Dehghan, Saeed Kamranfar, Omrane Benjeddou and Moncef L. Nehdi ()
Additional contact information
Samira Rastbod: Department of Architecture, Abhar Branch, Islamic Azad University, Abhar 4561934367, Iran
Farnaz Rahimi: Department of Architecture, Eram Institute of Higher Education, Shiraz 7195746733, Iran
Yara Dehghan: Faculty of Architecture and Urban Planning, Department of Architecture, Islamic Azad University of Central Tehran Branch, Tehran 1955847781, Iran
Saeed Kamranfar: Department of Architecture, Built Environment and Construction Engineering, Polytechnic Milan, 20133 Milan, Italy
Omrane Benjeddou: Civil Engineering Department, College of Engineering, Prince Sattam bin Abdulaziz University, Alkharj 16273, Saudi Arabia
Moncef L. Nehdi: Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada

Sustainability, 2022, vol. 15, issue 1, 1-15

Abstract: Recent developments in indirect predictive methods have yielded promising solutions for energy consumption modeling. The present study proposes and evaluates a novel integrated methodology for estimating the annual thermal energy demand (D AN ), which is considered as an indicator of the heating and cooling loads of buildings. A multilayer perceptron (MLP) neural network is optimally trained by symbiotic organism search (SOS), which is among the strongest metaheuristic algorithms. Three benchmark algorithms, namely, political optimizer (PO), harmony search algorithm (HSA), and backtracking search algorithm (BSA) are likewise applied and compared with the SOS. The results indicate that (i) utilizing the properties of the building within an artificial intelligence framework gives a suitable prediction for the D AN indicator, (ii) with nearly 1% error and 99% correlation, the suggested MLP-SOS is capable of accurately learning and reproducing the nonlinear D AN pattern, and (iii) this model outperforms other models such as MLP-PO, MLP-HSA and MLP-BSA. The discovered solution is finally expressed in an explicit mathematical format for practical uses in the future.

Keywords: thermal energy; building energy demand; artificial intelligence; symbiotic organism search (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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