Abstract
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.
Highlights
A large portion of energy is used in the building sector globally [1]
This study addresses applicationofoftwo twooptimization optimization algorithms of of genetic algorithm (GA)(GA)
We employed two metaheuristic algorithms to overcome the drawbacks of the backpropagation algorithms for this aim
Summary
A large portion of energy (more than 30%) is used in the building sector globally [1]. Thermal energy involves two measures of cooling load (CL), and heating load (HL) [2] and these measures are regulated by heating ventilation and air conditioning (HVAC) system. The HVAC system is designed to compute the HL and CL of the space and thereby, provide a desirable indoor air condition. In this sense, some studies have focused on evaluating comfortable, yet energy-saving spaces [3]. Required cooling and heating capacities are estimated mainly according to the basic factors, including building properties, its utilization, and climate conditions. Caniato et al [5] studied the expectations of the thermal and acoustic performance of wooden buildings against a heavyweight structure
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