• Energy Research

Abstract The huge demand for energy and construction materials has become an issue of great concern recently. The energy usage of buildings accounts for a large percentage of the total primary energy consumption. The total energy requirement of buildings is influenced by various factors, including environmental and climatic conditions, building envelope materials, insulation, etc. In this respect, estimating the operational energy of buildings is potentially helpful for architects and engineers in the early design and construction stages. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate the energy consumption of buildings according to the main building envelope parameters, namely material thickness and insulation K-value. Up to 180 simulations using different material thickness values and insulation properties are carried out in EnergyPlus software in order to use for estimation. This soft computing methodology is implemented with Matlab/Simulink and the performance is investigated.

Abstract The huge demand for energy and construction materials has become an issue of great concern recently. The energy usage of buildings accounts for a large percentage of the total primary energy consumption. The total energy requirement of buildings is influenced by various factors, including environmental and climatic conditions, building envelope materials, insulation, etc. In this respect, estimating the operational energy of buildings is potentially helpful for architects and engineers in the early design and construction stages. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate the energy consumption of buildings according to the main building envelope parameters, namely material thickness and insulation K-value. Up to 180 simulations using different material thickness values and insulation properties are carried out in EnergyPlus software in order to use for estimation. This soft computing methodology is implemented with Matlab/Simulink and the performance is investigated.