A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings

Khalid Almutairi, Salem Algarni, Talal Alqahtani, Hossein Moayedi and Amir Mosavi
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Khalid Almutairi: Department of Mechanical Engineering Technology, Applied College, University of Hafr Al Batin, Hafar Al Batin 39524, Saudi Arabia
Salem Algarni: Department of Mechanical Engineering, King Khalid University, Abha 61413, Saudi Arabia
Talal Alqahtani: Department of Mechanical Engineering, King Khalid University, Abha 61413, Saudi Arabia
Hossein Moayedi: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Amir Mosavi: John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary

Sustainability, 2022, vol. 14, issue 10, 1-19

Abstract: Recent studies have witnessed remarkable merits of metaheuristic algorithms in optimization problems. Due to the significance of the early analysis of the thermal load in energy-efficient buildings, this work introduces and compares four novel optimizer techniques—the firefly algorithm (FA), optics-inspired optimization (OIO), shuffled complex evolution (SCE), and teaching–learning-based optimization (TLBO)—for an accurate prediction of the heating load (HL). The models are applied to a multilayer perceptron (MLP) neural network to surmount its computational shortcomings. The models are fed by a literature-based dataset obtained for residential buildings. The results revealed that all models used are capable of properly analyzing and predicting the HL pattern. A comparison between them, however, showed that the TLBO-MLP with the coefficients of determination 0.9610 vs. 0.9438, 0.9373, and 0.9556 (respectively, for FA-MLP, OIO-MLP, and SCE-MLP) and the root mean square error of 2.1103 vs. 2.5456, 2.7099, and 2.2774 presents the most reliable approximation of the HL. It also surpassed several methods used in previous studies. Thus, the developed TLBO-MLP can be a beneficial model for subsequent practical applications.

Keywords: HVAC; heating load; artificial intelligence; metaheuristic algorithms; big data; machine learning; energy; building energy; deep learning; data science (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 (3)

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