LAPSE:2023.29996
Published Article
LAPSE:2023.29996
Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings
Hossein Moayedi, Amir Mosavi
April 14, 2023
A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).
Keywords
air conditioning, Artificial Intelligence, Big Data, consumption prediction, deep learning, Energy Efficiency, heating loads, heating ventilation, Machine Learning, metaheuristic, operational research
Suggested Citation
Moayedi H, Mosavi A. Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings. (2023). LAPSE:2023.29996
Author Affiliations
Moayedi H: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam; Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam
Mosavi A: Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany; School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway; John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hun [ORCID]
Journal Name
Energies
Volume
14
Issue
5
First Page
1331
Year
2021
Publication Date
2021-03-01
Published Version
ISSN
1996-1073
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PII: en14051331, Publication Type: Journal Article
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LAPSE:2023.29996
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doi:10.3390/en14051331
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Apr 14, 2023
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