01978nas a2200145 4500008004100000022001300041245012900054210006900183260001600252300001100268520142500279100001401704700001901718856009501737 2019 eng d a1364032100aLearning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States0 aLearning occupants indoor comfort temperature through a Bayesian cJan-11-2019 a1095933 a

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4°C for the cooling mode with a median of 23.7°C, and between 20.5 and 24.9°C for the heating mode with a median of 22.7°C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2-3°C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking.

1 aWang, Zhe1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/learning-occupants%E2%80%99-indoor-comfort02424nas a2200241 4500008004100000022001300041245013600054210006900190260001200259300001200271490000800283520161400291653002301905653003401928653002301962653002701985100001402012700001902026700001202045700001902057700001602076856009002092 2019 eng d a0306261900aLinking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification0 aLinking energycyberphysical systems with occupancy prediction an c02/2019 a55 - 690 v2363 a

With rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most prominent platforms to improve building design and management. Researchers investigated the possibility of integrating energy management system with cyber-physical systems as energy-cyber-physical systems to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging due to the dynamics of building occupants. As occupant behavior is one major source of uncertainties for energy management, ignoring it often results in energy wastes caused by overheating and overcooling as well as discomfort due to insufficient thermal and ventilation services. To mitigate such uncertainties, this study proposed an occupancy linked energy-cyber-physical system that incorporates WiFi probe-based occupancy detection. The proposed framework utilized ensemble classification algorithms to extract three types of occupancy information. It creates a data interface to link energy management system and cyber-physical systems and allows automated occupancy detection and interpretation through assembling multiple weak classifiers for WiFi signals. A validation experiment in a large office room was conducted to examine the performance of the proposed occupancy linked energy-cyber-physical systems. The experiment and simulation results suggest that, with a proper classifier and occupancy type, the proposed model can potentially save about 26.4% of energy consumption from the cooling and ventilation demands.

10aBuilding occupancy10aEnergy-Cyber-Physical Systems10aensemble algorithm10aWi-Fi probe technology1 aWang, Wei1 aHong, Tianzhen1 aLi, Nan1 aWang, Ryan, Qi1 aChen, Jiayu uhttps://simulationresearch.lbl.gov/publications/linking-energy-cyber-physical-systems02103nas a2200181 4500008004100000245008900041210006900130520146500199653003601664653001001700653002301710653002901733653001501762100001901777700001901796700001801815856008801833 2018 eng d00aA Library of Building Occupant Behaviour Models Represented in a Standardised Schema0 aLibrary of Building Occupant Behaviour Models Represented in a S3 a

Over the past four decades, a substantial body of literature has explored the impacts of occupant behaviour (OB) on building technologies, operation, and energy consumption. A large number of data-driven behavioural models have been developed based on field data. These models lack standardisation and consistency, leading to difficulties in applications and comparison. To address this problem, an ontology was developed using the drivers-needs-actions-systems (DNAS) framework. Recent work has been carried out to implement the theoretical DNAS framework into an eXtensible Markup Language (XML) schema, titled ‘occupant behaviour XML’ (obXML) which is a practical implementation of OB models that can be integrated into building performance simulation (BPS) programs. This paper presents a newly developed library of OB models represented in the standardised obXML schema format. This library provides ready-to-use examples for BPS users to employ more accurate occupant representation in their energy models. The library, which contains an initial effort of 52 OB models, was made publicly available for the BPS community. As part of the library development process, limitations of the obXML schema were identified and addressed, and future improvements were proposed. Authors hope that by compiling this library building, energy modellers from all over the world can enhance their BPS models by integrating more accurate and robust OB patterns.

10abuilding performance simulation10aobXML10aOccupant Behaviour10aoccupant behaviour model10aXML schema1 aBelafi, Zsofia1 aHong, Tianzhen1 aReith, Andras uhttps://simulationresearch.lbl.gov/publications/library-building-occupant-behaviour00853nas a2200133 4500008004100000245007200041210006900113260001500182520040600197100001300603700001800616700002000634856006500654 2006 eng d00aA Library of HVAC Component Models for use in Automated Diagnostics0 aLibrary of HVAC Component Models for use in Automated Diagnostic aBoston, MA3 a

The paper describes and documents a library of equipment reference models developed for automated fault detection and diagnosis of secondary HVAC system (air handling units and air distribution systems). The models are used to predict the performance that would be expected in the absence of faults. The paper includes a description of the use of automatic documentation methods in the library.

1 aXu, Peng1 aHaves, Philip1 aCurtil, Dimitri uhttp://www.ibpsa.us/pub/simbuild2006/papers/SB06_034_041.pdf00526nas a2200121 4500008004100000245008900041210006900130260003200199100003100231700002700262700002700289856008800316 2006 eng d00aLow Energy Cooling Technologies for Sub-Tropical/Warm Humid Climate Building Systems0 aLow Energy Cooling Technologies for SubTropicalWarm Humid Climat aCambridge, MA, USAc08/20061 aChowdhury, Ashfaque, Ahmed1 aRasul, Mohammad, Golam1 aKamal, Mohammad, Masud uhttps://simulationresearch.lbl.gov/publications/low-energy-cooling-technologies-sub00408nas a2200109 4500008004100000050001500041245006200056210006200118100001300180700001800193856008700211 2003 eng d aLBNL-5350500aLibrary of component reference models for fault detection0 aLibrary of component reference models for fault detection1 aXu, Peng1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/library-component-reference-models