A multi-objective optimization of a building’s total heating and cooling loads and total costs in various climatic situations using response surface methodology

Excessive rise in energy consumption has been one of the major predicaments of recent decades. Among all the sectors, residential buildings are one of the main consumers of energy resources. Because air conditioning systems are the main ground for using energy inside houses, researchers have proposed diverse methods of reducing energy loss such as encapsulating insulators in wall structures. In this paper, the main focus is to calculate and then optimize the total heating and cooling loads as well as the total costs. The building model was simulated in cities with different climatic situations using EnergyPlus software. For optimization, five design variables were determined and 300 Design of Experiment points were considered for each city to measure the Objective Functions, which are the building’s total load and cost. To find the optimal states, Response Surface Methodology (RSM) is utilized to predict continuous functions from discrete data of experiments. Consequently, total load and total costs of building in various climatic conditions were improved by a range of 2%–16%, and the Static Payback Period and Human Heating Comfort were ameliorated dramatically.

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