This paper presents a simulation study to reduce heating and cooling energy demand of a school building in Seoul Metropolitan Area, Korea. The aim of this study was to estimate the impact of passive vs. active approaches on energy savings in buildings using EnergyPlus simulation. By controlling lighting, the energy saving of the original school building design was found most significant, and increased by 32% when the design was improved. It is noteworthy that energy saving potential of each room varies significantly depending on the rooms' thermal characteristics and orientation. Thus, the analysis of energy saving should be introduced at the individual space level, not at the whole building level. Additionally, the simulation studies should be involved for rational decision-making. Finally, it was concluded that priority should be given to passive building design strategies, such as building orientation, as well as control and utilization of solar radiation. These passive energy saving strategies are related to urban, architectural design, and engineering issues, and are more beneficial in terms of energy savings than active strategies.
Abstract:With the increasing focus on low energy buildings and the need to develop sustainable built environments, Building Energy Performance Simulation (BEPS) tools have been widely used. However, many issues remain when applying BEPS tools to existing buildings. This paper presents the issues that need to be solved for the application of BEPS tools to an existing office building. The selected building is an office building with 33 stories above ground, six underground levels, and a total floor area of 91,898 m 2 . The issues to be discussed in this paper are as follows: (1) grey data not ready for simulation; (2) subjective assumptions and judgments on energy modeling; (3) stochastic characteristics of building performance and occupants behavior; (4) verification of model fidelity-comparison of aggregated energy; (5) verification of model fidelity-calibration by trial and error; and (6) use of simulation model for real-time energy management. This study investigates the aforementioned issues and explains the factors that should be considered to address these issues when developing a dynamic simulation model for existing buildings.
Abstract:The current Building Energy Performance Simulation (BEPS) tools are based on first principles. For the correct use of BEPS tools, simulationists should have an in-depth understanding of building physics, numerical methods, control logics of building systems, etc. However, it takes significant time and effort to develop a first principles-based simulation model for existing buildings-mainly due to the laborious process of data gathering, uncertain inputs, model calibration, etc. Rather than resorting to an expert's effort, a data-driven approach (so-called "inverse" approach) has received growing attention for the simulation of existing buildings. This paper reports a cross-comparison of three popular machine learning models (Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gaussian Process (GP)) for predicting a chiller's energy consumption in a virtual and a real-life building. The predictions based on the three models are sufficiently accurate compared to the virtual and real measurements. This paper addresses the following issues for the successful development of machine learning models: reproducibility, selection of inputs, training period, outlying data obtained from the building energy management system (BEMS), and validation of the models. From the result of this comparative study, it was found that SVM has a disadvantage in computation time compared to ANN and GP. GP is the most sensitive to a training period among the three models.
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