기계학습 적용을 위한 건물에너지 데이터의 생성 방법론 개발
Data Generation for Machine Learning Applications in Building Operation
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초록
Utilizing the machin learning (ML) algorithm for the building cooling/heating operations requires the actual building data. The robustness of the ML-based controller depends on the amount and quality of the data. However, reserving actual building operation data is challenging because of the cost and time required. This study proposes the methodology for generating the synthetic simulation data the development of the ML (specifically, reinforcement learning) algorithm when the actual data are lacking or unavailable. As the first step toward investigating this methodology, we built two building models, an EnergyPlus simulation model and a grey-box model. The prediction performances of two models were quantified and evaluated.
Abstract
Utilizing the machin learning (ML) algorithm for the building cooling/heating operations requires the actual building data. The robustness of the ML-based controller depends on the amount and quality of the data. However, reserving actual building operation data is challenging because of the cost and time required. This study proposes the methodology for generating the synthetic simulation data the development of the ML (specifically, reinforcement learning) algorithm when the actual data are lacking or unavailable. As the first step toward investigating this methodology, we built two building models, an EnergyPlus simulation model and a grey-box model. The prediction performances of two models were quantified and evaluated.
참고문헌 (6)
- "Input Output Reference The Encyclopedic Reference to EnergyPlus Input and Output"[URL]
- Lee, K. H., "EnergyPlus Modeling Techniques in Conjunction with the Theory of Air-Conditioning Facilities" (2018), HansolAcademy
- Joe, J., "A Model Predictive Control Strategy to Optimize the Performance of Radiant Floor Heating and Cooling Systems in Office Buildings", Applied Energy, 245, pp.65~77
- BuildingEnergy Performence Analysis Using DesignBuilder, "Jang, H. -I.", Annex Research Institute of Mirae Environment Plan, ~ (2017)
- "Ministry of Land, Infrastructure and Transport Notice No. 2017-881", Energy Saving Design Standards for Buildings, Ministry of Land, Infrastructure and Transport (Department of Green Architecture), ~ (2017)
- Guideline 14-2014, Measurement of Energy and Demand Savings; Technical Report, "American Society of Heating, Ventilating, and Air Conditioning Engineers", American Society of Heating, Ventilating, and Air Conditioning Engineers, ~ (2014)