@article {32165, title = {Learning occupants{\textquoteright} indoor comfort temperature through a Bayesian inference approach for office buildings in United States}, journal = {Renewable and Sustainable Energy Reviews}, year = {2019}, month = {Jan-11-2019}, pages = {109593}, abstract = {

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants{\textquoteright} 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Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset.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{\textdegree}C for the cooling mode with a median of 23.7{\textdegree}C, and between 20.5 and 24.9{\textdegree}C for the heating mode with a median of 22.7{\textdegree}C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2-3{\textdegree}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.

}, issn = {13640321}, doi = {10.1016/j.rser.2019.109593}, author = {Wang, Zhe and Hong, Tianzhen} } @article {31662, title = {Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification}, journal = {Applied Energy}, volume = {236}, year = {2019}, month = {02/2019}, pages = {55 - 69}, abstract = {

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.

}, keywords = {Building occupancy, Energy-Cyber-Physical Systems, ensemble algorithm, Wi-Fi probe technology}, issn = {03062619}, doi = {10.1016/j.apenergy.2018.11.079}, author = {Wei Wang and Tianzhen Hong and Nan Li and Ryan Qi Wang and Jiayu Chen} } @article {30489, title = {A Library of Building Occupant Behaviour Models Represented in a Standardised Schema}, journal = {Energy Efficiency}, year = {2018}, abstract = {

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 {\textquoteleft}occupant behaviour XML{\textquoteright} (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.

}, keywords = {building performance simulation, obXML, Occupant Behaviour, occupant behaviour model, XML schema}, doi = {10.1007/s12053-018-9658-0}, author = {Zsofia Belafi and Tianzhen Hong and Andras Reith} } @proceedings {269, title = {A Library of HVAC Component Models for use in Automated Diagnostics}, year = {2006}, address = {Boston, MA}, abstract = {

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.

}, url = {http://www.ibpsa.us/pub/simbuild2006/papers/SB06_034_041.pdf}, author = {Peng Xu and Philip Haves and Dimitri Curtil} } @proceedings {2796, title = {Low Energy Cooling Technologies for Sub-Tropical/Warm Humid Climate Building Systems}, year = {2006}, month = {08/2006}, address = {Cambridge, MA, USA}, author = {Ashfaque Ahmed Chowdhury and Mohammad Golam Rasul and Mohammad Masud Kamal} } @article {1563, title = {Library of component reference models for fault detection}, year = {2003}, author = {Peng Xu and Philip Haves} }