Idioma:

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

Le, Le Thi ; Nguyen, Hoang ; Dou, Jie ; Zhou, Jian

Applied sciences, 2019-07, Vol.9 (13), p.2630 [Periódico revisado por pares]

Basel: MDPI AG

Texto completo disponível

Citações Citado por

Enviar para

Título:
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
Autor: Le, Le Thi ; Nguyen, Hoang ; Dou, Jie ; Zhou, Jian
Assuntos: Accuracy ; Adaptation ; Algorithms ; Artificial intelligence ; Buildings ; Comparative studies ; Data collection ; Datasets ; Deep learning ; Energy consumption ; Energy demand ; Energy efficiency ; Estimation ; Evolutionary algorithms ; Genetic algorithms ; Heating load ; Heuristic ; hybrid model ; meta-heuristic algorithm ; Mutation ; Optimization techniques ; Population ; Problem solving ; Regression analysis ; smart building ; Smart cities ; smart city ; Studies ; Urban planning ; Values
É parte de: Applied sciences, 2019-07, Vol.9 (13), p.2630
Descrição: 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.
Editor: Basel: MDPI AG
Idioma: Inglês

Voltar para lista de resultados

Resultado 1 Avançar Ir para próxima página

Realização: Logos de Redes Sociais:

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

Le, Le Thi ; Nguyen, Hoang ; Dou, Jie ; Zhou, Jian

Basel: MDPI AG

Buscando em bases de dados remotas. Favor aguardar.