Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models

Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing th...

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Bibliographic Details
Published in:Applied sciences 2019-09, Vol.9 (17), p.3543
Main Authors: Tien Bui, Dieu, Moayedi, Hossein, Anastasios, Dounis, Kok Foong, Loke
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Buildings
Cooling
cooling load
Cooling loads
Cooling systems
Design
Energy conservation
Energy consumption
Energy efficiency
energy efficient residential buildings
Evolutionary algorithms
Genetic algorithms
Glazing
Green buildings
Heating
Heating load
HVAC
Load
Neural networks
Regression analysis
Residential areas
Residential buildings
Residential energy
Simulation
Support vector machines
Wavelet transforms
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Summary:Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9173543