optimization tools for building energy model calibration

Abstract Different optimization tools have been developed to find the best trade-off between competitive goals. The optimization problem is typical of the design process, where different design solutions have to be compared to achieve one or more objectives, often in contrast with each other. A quite novel application of optimization is building energy model calibration. The use of well-calibrated energy simulation models is key for successful buildings' retrofit or operation management and the optimization techniques can improve the reliability of the results. The typical optimization method consists in the analysis of all the alternatives' performances, developing a full factorial plan and simulating all the possible options (brute-force approach). However, this process could take unsustainable long time. That is why some optimization tools, based on evolutionary algorithms have been developed to speed up the process. This study compares results obtained through the brute-force approach and the evolutionary optimization methods applied on the calibration of a large educational building model located in the province of Treviso, north of Italy. The total design space consists of about 72 000 EnergyPlus building models. Two optimization-based calibrations have been repeated using a genetic algorithm by means of jEPlus+EA on a local computer and through parametric simulations implemented by jEPlus on a cloud service. The quality of results from the evolutionary optimization tools as compared to a full parametric study applied on calibration have been discussed. Scenarios of applicability are drafted. On a practical level, the research is a contribution for the selection of methods and tools for the preparation of models that can lead to optimized retrofit interventions and rationalization of building management and operation

Defining The Energy Saving Potential of Architectural Design

AbstractDesigners, in response to codes or voluntary ⬓green building⬽ programs, are increasingly concerned with building energy demand reduction, but they are not fully aware of the energy saving potential of architectural design. According to literature, building form, construction and material choices may be powerful drivers of energy efficiency ⬜ but a very few studies have quantified their actual effect in different climate, and none of the study is based on today computational possibilities. This research was inspired by, and attempts to verify, the ideas from two of the most influential books on sustainable design: ⬓Design With Climate⬽ by Olgyay (1963), which discussed strategies for climate-adapted architecture, and Lechner̽s ⬓Heating, Cooling and Lighting⬽ (1991), on how to reduce building energy needs by as much as 60 ⬜ 80 percent with proper architectural design decisions. Both books used results from building energy simulations made with limited computational resources available at the time. The research presented in this paper uses a genetic algorithms based approach for the optimization of heating, cooling and lighting energy demands of different building designs. In total, over 25 million different buildings constitute the optimization search space, and the most energy efficient design solutions were explored for 8 different climate zones. The building designs are varied by shape, orientation, window to wall ratio, component and construction types, materials, and different occupant behaviour. The research shows the best solution for each of the climates and compares them with Olgyay̽s findings. Finally, for each climate the energy saving potential is defined and then compared to Lechner's conclusions

Optimization Tools for Building Energy Model Calibration

Different optimization tools have been developed to find the best trade-off between competitive goals. The optimization problem is typical of the design process, where different design solutions have to be compared to achieve one or more objectives, often in contrast with each other. A quite novel application of optimization is building energy model calibration. The use of well-calibrated energy simulation models is key for successful buildings' retrofit or operation management and the optimization techniques can improve the reliability of the results. The typical optimization method consists in the analysis of all the alternatives' performances, developing a full factorial plan and simulating all the possible options (brute-force approach). However, this process could take unsustainable long time. That is why some optimization tools, based on evolutionary algorithms have been developed to speed up the process. This study compares results obtained through the brute-force approach and the evolutionary optimization methods applied on the calibration of a large educational building model located in the province of Treviso, north of Italy. The total design space consists of about 72 000 EnergyPlus building models. Two optimization-based calibrations have been repeated using a genetic algorithm by means of jEPlus+EA on a local computer and through parametric simulations implemented by jEPlus on a cloud service. The quality of results from the evolutionary optimization tools as compared to a full parametric study applied on calibration have been discussed. Scenarios of applicability are drafted. On a practical level, the research is a contribution for the selection of methods and tools for the preparation of models that can lead to optimized retrofit interventions and rationalization of building management and operation