A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning
Source abstract
Abstract
Energy-efficiency is one of the critical issues in smart cities. It is an essential basis for optimizing smart cities planning. This study proposed four new artificial intelligence (AI) techniques for forecasting the heating load of buildings’ energy efficiency based on the potential of artificial neural network (ANN) and meta-heuristics algorithms, including artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA). They were abbreviated as ABC-ANN, PSO-ANN, ICA-ANN, and GA-ANN models; 837 buildings were considered and analyzed based on the influential parameters, such as glazing area distribution (GLAD), glazing area (GLA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), relative compactness (RC), for estimating heating load (HL). Three statistical criteria, such as root-mean-squared error (RMSE), coefficient determination (R2), and mean absolute error (MAE), were used to assess the potential of the aforementioned models. The results indicated that the GA-ANN model provided the highest performance in estimating the heating load of buildings’ energy efficiency, with an RMSE of 1.625, R2 of 0.980, and MAE of 0.798. The remaining models (i.e., PSO-ANN, ICA-ANN, ABC-ANN) yielded lower performance with RMSE of 1.932, 1.982, 1.878; R2 of 0.972, 0.970, 0.973; MAE of 1.027, 0.980, 0.957, respectively.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Building Energy and Comfort Optimization · Urban Heat Island Mitigation · Energy Load and Power Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Thanh Hoa University of Culture, Sports and Tourism, Thanh Hoa 440000, Vietnam
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Civil and Environmental Engineering, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata 940-2188, Japan
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
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