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Article

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

by 1,2 and 3,4,5,6,*
1
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
2
Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam
3
Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
4
School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
5
John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
6
School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Jin Woo Moon
Energies 2021, 14(5), 1331; https://doi.org/10.3390/en14051331
Received: 5 January 2021 / Revised: 21 February 2021 / Accepted: 22 February 2021 / Published: 1 March 2021
(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)
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)). View Full-Text
Keywords: energy efficiency; heating loads; heating ventilation; air conditioning; metaheuristic; consumption prediction; artificial intelligence; deep learning; machine learning; operational research; big data energy efficiency; heating loads; heating ventilation; air conditioning; metaheuristic; consumption prediction; artificial intelligence; deep learning; machine learning; operational research; big data
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MDPI and ACS Style

Moayedi, H.; Mosavi, A. Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings. Energies 2021, 14, 1331. https://doi.org/10.3390/en14051331

AMA Style

Moayedi H, Mosavi A. Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings. Energies. 2021; 14(5):1331. https://doi.org/10.3390/en14051331

Chicago/Turabian Style

Moayedi, Hossein, and Amir Mosavi. 2021. "Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings" Energies 14, no. 5: 1331. https://doi.org/10.3390/en14051331

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