Efficiency-based Pareto Optimization of Building Energy Consumption and Thermal Comfort: A Case Study of a Residential Building in Bushehr, Iran

In Iran, the intensity of energy consumption in the building sector is almost 3 times the world average, and due to the consumption of fossil fuels as the main source of energy in this sector, as well as the lack of optimal design of buildings, it has led to excessive release of toxic gases into the environment. This research develops an efficient approach for the simulation-oriented Pareto optimization (SOPO) of building energy efficiency to assist engineers in optimal building design in early design phases. To this end, EnergyPlus, as one of the most powerful and well-known whole-building simulation programs, is combined with the Multi-objective Ant Colony Optimization (MOACO) algorithm through the JAVA programming language. As a result, the capabilities of JAVA programming are added to EnergyPlus without the use of other plugins and third parties. To evaluate the effectiveness of the developed method, it was performed on a residential building located in the hot and semi-arid region of Iran. To obtain the optimum configuration of the building under investigation, the building rotation, window-to-wall ratio, tilt angle of shading device, depth of shading device, color of the external walls, area of solar collector, tilt angle of solar collector, rotation of solar collector, cooling and heating setpoints of heating, ventilation, and air conditioning (HVAC) system are chosen as decision variables. Further, the building energy consumption (BEC), solar collector efficiency (SCE), and predicted percentage of dissatisfied (PPD) index as a measure of the occupants' thermal comfort level are chosen as the objective functions. The single-objective optimization (SO) and Pareto optimization (PO) are performed. The obtained results are compared to the initial values of the basic model. The optimization results depict that the PO provides optimal solutions more reliable than those obtained by the SOs, owing to the lower value of the deviation index. Moreover, the optimal solutions extracted through the PO are depicted in the form of Pareto fronts. Eventually, the Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) technique as one of the well-known multi-criteria decision-making (MCDM) methods is utilized to adopt the optimum building configuration from the set of Pareto optimal solutions. Further, the results of PO show that although BEC increases from 136 GJ to 140 GJ, PPD significantly decreases from 26% to 8% and SCE significantly increases from 16% to 25%. The introduced SOPO method suggests an effective and practical approach to obtain optimal solutions during the building design phase and provides an opportunity for building engineers to have a better picture of the range of options for decision-making. In addition, the method presented in this study can be applied to different types of buildings in different climates.