Next Article in Journal
Optimal Combination of External Wall Insulation Thickness and Surface Solar Reflectivity of Non-Residential Buildings in the Korean Peninsula
Next Article in Special Issue
Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction
Previous Article in Journal
Electric Vehicles in Jordan: Challenges and Limitations
Previous Article in Special Issue
Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment
Article

Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load 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
Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, 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: Mehdi Seyedmahmoudian
Sustainability 2021, 13(6), 3198; https://doi.org/10.3390/su13063198
Received: 11 January 2021 / Revised: 13 February 2021 / Accepted: 17 February 2021 / Published: 15 March 2021
The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis. View Full-Text
Keywords: energy-efficient building; heating load; neural computing; biogeography-based optimization; big data; machine learning; artificial intelligence; deep learning; building energy; smart buildings, IoT; smart city energy-efficient building; heating load; neural computing; biogeography-based optimization; big data; machine learning; artificial intelligence; deep learning; building energy; smart buildings, IoT; smart city
Show Figures

Figure 1

MDPI and ACS Style

Moayedi, H.; Mosavi, A. Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings. Sustainability 2021, 13, 3198. https://doi.org/10.3390/su13063198

AMA Style

Moayedi H, Mosavi A. Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings. Sustainability. 2021; 13(6):3198. https://doi.org/10.3390/su13063198

Chicago/Turabian Style

Moayedi, Hossein, and Amir Mosavi. 2021. "Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings" Sustainability 13, no. 6: 3198. https://doi.org/10.3390/su13063198

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop