A physics-based linear parametric model of room temperature in office buildings
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HVAC(Heating, Ventilation, and Air Conditioning: 공기조화기기) 시스템은 전력 소모가 크기에, 최소의 전력으로 지정된 실내온도를 유지하도록 운전하는 것이 중요한 문제이다. 최근 HVAC 전력 최소화 문제에 대한 기계학습 기법들의 응용 시도가 증가하고 있으나 기계학습 기반 전력 HVAC 운전 기법의 개발을 위해 사용할 수 있는 시뮬레이션 환경은 매우 찾기가 어려운 실정이다. 본 논문에서는 MATLAB/Simulink 기반의 시뮬레이션 환경을 소개 및 공개하고 이를 바탕으로 개발된 강화학습 기반의 에어컨 자동 운전 알고리즘의 성능을 평가한다. 시뮬레이션 및 학습 관련 코드는 (https://github.com/intplatlab/hvac_sim.git)에서 다운로드 받을 수 있다.
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탄소중립, RE100과 같이 자발적 친환경 에너지원에 대한 동참과 더불어 에너지원에 대한 안보논란까지 겹쳐 효율적 에너지사용에 관한 논란이 대두되고 있다. 국내 연간 에너지소비량 중 건물로부터 발생하는 에너지소비량은 총 40%에 육박하며 공조부하로부터 발생하는 에너지소비량은 30%를 상회한다. 현재 국내 HVAC (Heat Ventilation Air Conditioning)의 열원설비의 종류와 공조설비 운영전략은 건물의 특성에 따라 상이하다. Smart City 구현 관점에서 국내 건물부하의 상당부분을 차지하고 있는 사무공간, 의료시설 및 창고의 경우 운영 신뢰성 요소로 인하여 정풍량 HVAC 제어가 많은데 에너지절감 가능 요소가 상대적으로 적다. 따라서 본 논문에서는 정풍량 제어 HVAC이 설치된 서울대학교 시흥분원 교육협력동 건물을 대상으로 설계단계에서 고려되는 Passive 요소의 재설정을 통하여 발견 가능한 에너지 절감 기회에 관하여 EnergyPlus를 활용하여 논하였다.
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Heating, ventilation and air-conditioning (HVAC) systems account for about 40% of energy used in buildings in the United States in 2008. Analysis of the energy efficiency of HVAC systems requires building-related information, such as building design, HVAC configurations, indoor environment measurements and load requirements. However, since building projects involve multiple disciplines, the needed information is stored in heterogeneous sources, such as design drawings, equipment manuals and sensor networks. It is hence difficult for system operators to collect and integrate the information required for analyzing the performance of HVAC systems. This paper explores the approaches to overcome this challenge of integrating heterogeneous information sources to support the information required for analyzing the performance of HVAC systems. The discussions include a synthesis of the characteristics of integration approaches from various domains, and an analysis of the relationships between the characteristics and the information sources required for analyzing performance of HVAC systems.
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HVAC systems are milestones of building mechanical systems that provide thermal comfort for occupants accompanied with indoor air quality. HVAC systems can be classified into central and local systems according to multiple zones, location, and distribution. Primary HVAC equipment includes heating equipment, ventilation equipment, and cooling or air-conditioning equipment. Central HVAC systems locate away from buildings in a central equipment room and deliver the conditioned air by a delivery ductwork system. Central HVAC systems contain all-air, air-water, all-water systems. Two systems should be considered as central such as heating and cooling panels and water-source heat pumps. Local HVAC systems can be located inside a conditioned zone or adjacent to it and no requirement for ductwork. Local systems include local heating, local air-conditioning, local ventilation, and split systems.
Central air conditioning
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In this book, various aspects of heating, ventilation, and air-conditioning (HVAC) systems are investigated. HVAC systems are milestones of building mechanical systems that provide thermal comfort for occupants accompanied with indoor air quality. HVAC systems can be classified into central and local systems according to multiple zones, location, and distribution. Primary HVAC equipment includes heating equipment, ventilation equipment, and cooling or air-conditioning equipment. Central HVAC systems are located away from buildings in a central equipment room and deliver the conditioned air by a delivery ductwork system. Central HVAC systems contain all-air, air-water, or all-water systems. Two systems should be considered as central such as heating and cooling panels and water-source heat pumps.
Central air conditioning
Energy recovery ventilation
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This paper presents a new fault detection and isolation scheme for dealing with simultaneous additive and parametric faults. The new design integrates a system for additive fault detection based on (Castillo and Zufiria, (2009)) and a new parametric fault detection and isolation scheme inspired in (Münz and Zufiria, (2008)). It is shown that the so far existing schemes do not behave correctly when both additive and parametric faults occur simultaneously; to solve the problem a new integrated scheme is proposed. Computer simulation results are presented to confirm the theoretical studies.
Isolation
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Energy consumed in buildings takes significant portions of the total global energy usage. A large amount of building energy is used for heating, cooling, ventilation, and air-conditioning (HVAC). However, compared to its importance, building energy management systems nowadays are limited in controlling HVAC based on simple rule-based control (RBC) technologies. The ability to design systems that can efficiently manage HVAC can reduce energy usage and greenhouse gas emissions, and, all in all, it can help us to mitigate climate change. This paper proposes predictive time-series models of occupancy patterns using LSTM. Prediction signal for future room occupancy status on the next time span (e.g., next 30 minutes) can be directly used to operate HVAC. For example, based on the prediction and considering the time for cooling or heating, HVAC can be turned on before the room is being used (e.g., turn on 10 minutes earlier). Also, based on the next room empty prediction timing, HVAC can be turned off earlier, and it can help us increase the efficiency of HVAC while not decreasing comfort. We demonstrate our approach's capabilities using real-world energy data collected from multiple rooms of a university building. We show that LSTM's room occupancy prediction based HVAC control could save energy usage by 50% compared to conventional RBC based control.
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Model Predictive Control
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Isolation
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