@article {27265, title = {Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications}, journal = {Energy and Buildings}, volume = {224}, year = {2020}, abstract = {Occupants are active participants in their built environment, affecting its performance while simultaneously being affected by its design and indoor environmental conditions. With recent advances in computer modeling, simulation tools, and analysis techniques, topics such as human-building interactions and occupant behavior have gained significant interest in the literature given their premise of improving building design processes and operating strategies. In practice, the focus of occupant-centric literature has been mostly geared towards the latter (i.e., operation), leaving the implications on building design practices underexplored. This paper fills the gap by providing a critical review of existing studies applying computer-based modeling and simulation to guide occupant-centric building design. The reviewed papers are organized along four main themes, namely occupant-centric: (i) metrics of building performance, (ii) modeling and simulation approaches, (iii) design methods and applications, and (iv) supporting practices and mechanisms. Important barriers are identified for a more effective application of occupant-centric building design practices, including the limited consideration of metrics beyond energy efficiency (e.g., occupant well-being and space planning), the limited implementation and validation of the proposed methods, and the lack of integration of occupant behavior modeling in existing building performance simulation tools. Future research directions are discussed, covering large-scale international data collection efforts to move from generic assumptions about occupant behavior to specific/localized knowledge, improved metrics of measuring building performance, and improved industry practices, such as building codes, to promote an occupant-in-the-loop approach to the building design process.}, url = {https://doi.org/10.1016/j.enbuild.2020.110292}, author = {Elie Azar and William O{\textquoteright}Brien and Salvatore Carlucci and Tianzhen Hong and Andrew Sonta and Joyce Kim and Maedot S Andargie and Tareq Abuimara and Mounir El Asmar and Rishee Jain and Mohamed M Ouf and Farhang Tahmasebi and Jenny Jin Zhou} } @article {27262, title = {Occupant comfort and behavior: High-resolution data from a 6-month field study of personal comfort systems with 37 real office workers}, journal = {Building and Environment}, volume = {148}, year = {2019}, pages = {348-360}, abstract = {Personal Comfort Systems (PCS) provide individual occupants local heating and cooling to meet their comfort needs without affecting others in the same space. It saves energy by relaxing ambient temperature requirements for the HVAC system. Aside from these benefits, PCS offers a wealth of data that can describe how individuals interact with heating/cooling devices in their own environment. Recently developed Internet-connected PCS chairs have unlocked this opportunity by generating continuous streams of heating and cooling usage data, along with occupancy status and environmental measurements via embedded sensors. The data allow individuals{\textquoteright} comfort and behavior to be learned, and can inform centralized systems to provide {\textquoteleft}just the right{\textquoteright} amount of conditioning to meet occupant needs. In summer 2016, we carried out a study with PCS chairs involving 37 occupants in an office building in California. During the study period, we collected \>5 million chair usage data-points and 4500 occupant survey responses, as well as continuous measurements of environmental and HVAC system conditions. The data analysis shows that (1) local temperatures experienced by individual occupants vary quite widely across different parts of the building, even within the same thermal zone; (2) occupants often have different thermal preferences even under the same thermal conditions; (3) PCS control behavior can dynamically describe individuals{\textquoteright} thermal preferences; (4) PCS chairs produce much higher comfort satisfaction (96\%) than typically achieved in buildings. We conclude that PCS not only provide personalized comfort solutions but also offer individualized feedback that can improve comfort analytics and control decisions in buildings.}, url = {https://doi.org/10.1016/j.buildenv.2018.11.012}, author = {Joyce Kim and Fred Bauman and Paul Raftery and Edward Arens and Hui Zhang and Gabe Fierro and Michael Andersen and David Culler} } @mastersthesis {27274, title = {Advancing comfort technology and analytics to personalize thermal experience in the built environment}, year = {2018}, type = {Doctor of Philosophy Dissertation}, abstract = {Nearly 60\% of global energy consumption in buildings is used for space heating and cooling to provide occupant comfort. Yet, a large portion of occupants are dissatisfied with the buildings{\textquoteright} thermal environment. There are many reasons for thermal dissatisfaction in buildings, but a fundamental cause is the current practice of delivering uniform thermal conditions based on universal rules, without accounting for individual differences in comfort requirements. To address these issues, a growing body of research has emerged to better reflect individual{\textquoteright} comfort requirements. This dissertation contributes to this research by providing the following primary innovations: 1) Internet-connected personal comfort system (PCS) and 2) personal comfort models that can help to deliver personalized comfort experiences in occupied spaces. In particular, I developed and field-tested the new capabilities of PCS (data reporting, wireless connectivity) that could support individualized learning and coordinated controls with other building systems. I also proposed a new framework for thermal comfort modeling {\textendash} personal comfort models that can predict individuals{\textquoteright} thermal comfort, instead of the average response of a large population, using Internet of Things and machine learning. As a practical use case, I developed a set of personal comfort models using the PCS field study data to demonstrate how the proposed framework can be implemented. The results showed that personal comfort models produced superior accuracy over conventional comfort models (PMV, adaptive) and that PCS heating and cooling control behavior was a strong predictor of individuals{\textquoteright} thermal preference and could be used as an individualized comfort feedback for HVAC controls. The results of this dissertation showed a synergistic effect between PCS and personal comfort models that could enable occupant-centric comfort management in buildings.}, url = {https://scholar.google.ca/scholar?oi=bibs\&cluster=1182575387411839698\&btnI=1\&hl=en}, author = {Joyce Kim} } @article {27263, title = {Design automation for smart building systems}, journal = {Proceedings of the IEEE}, volume = {106}, year = {2018}, pages = {1680-1699}, abstract = {Smart buildings today are aimed at providing safe, healthy, comfortable, affordable, and beautiful spaces in a carbon and energy-efficient way. They are emerging as complex cyber-physical systems with humans in the loop. Cost, the need to cope with increasing functional complexity, flexibility, fragmentation of the supply chain, and time-to-market pressure are rendering the traditional heuristic and ad hoc design paradigms inefficient and insufficient for the future. In this paper, we present a platform-based methodology for smart building design. Platform-based design (PBD) promotes the reuse of hardware and software on shared infrastructures, enables rapid prototyping of applications, and involves extensive exploration of the design space to optimize design performance. In this paper, we identify, abstract, and formalize components of smart buildings, and present a design flow that maps high-level specifications of desired building applications to their physical implementations under the PBD framework. A case study on the design of on-demand heating, ventilation, and air conditioning (HVAC) systems is presented to demonstrate the use of PBD.}, url = {https://doi.org/10.1109/JPROC.2018.2856932}, author = {Ruoxi Jia and Baihong Jin and Ming Jin and Yuxun Zhou and Ioannis C Konstantakopoulos and Han Zou and Joyce Kim and Dan Li and Weixi Gu and Reza Arghandeh and Pierluigi Nuzzo and Stefano Schiavon and Alberto L Sangiovanni-Vincentelli and Costas J Spanos} } @article {27260, title = {Personal comfort models \– A new paradigm in thermal comfort for occupant-centric environmental control}, journal = {Building and Environment}, volume = {132}, year = {2018}, pages = {114-124}, abstract = {A\ personal comfort model\ is a new approach to thermal comfort modeling that predicts an individual{\textquoteright}s thermal comfort response, instead of the average response of a large population. It leverages the\ Internet of Things\ and machine learning to learn individuals{\textquoteright} comfort requirements directly from the data collected in their everyday environment. Its results could be aggregated to predict comfort of a population. To provide guidance on future efforts in this emerging research area, this paper presents a unified framework for personal comfort models. We first define the problem by providing a brief discussion of existing thermal comfort models and their limitations for\ real-world applications, and then review the current state of research on personal comfort models including a summary of key advances and gaps. We then describe a modeling framework to establish fundamental concepts and methodologies for developing and evaluating personal comfort models, followed by a discussion of how such models can be integrated into indoor environmental controls. Lastly, we discuss the challenges and opportunities for applications of personal comfort models for\ building design, control, standards, and future research.}, url = {https://doi.org/10.1016/j.buildenv.2018.01.023}, author = {Joyce Kim and Stefano Schiavon and Gail Brager} } @article {27261, title = {Personal comfort models: Predicting individuals\&$\#$39; thermal preference using occupant heating and cooling behavior and machine learning}, journal = {Building and Environment}, volume = {129}, year = {2017}, pages = {96-106}, abstract = {A\ personal comfort model\ is a new approach to thermal comfort modeling that predicts individuals{\textquoteright} thermal comfort responses, instead of the average response of a large population. However, securing consistent occupant feedback for model development is challenging as the current methods of data collection rely on individuals{\textquoteright} survey participation. We explored the use of a new type of feedback, occupants{\textquoteright} heating and cooling behavior with a personal comfort system (PCS) for the development of personal comfort models to predict individuals{\textquoteright} thermal preference. The model development draws from field data including PCS control behavior, environmental conditions and mechanical system settings collected from 38 occupants in an office building, and employs six\ machine learning algorithms. The results showed that (1) personal comfort models based on all field data produced the median accuracy of 0.73 among all subjects and improved predictive accuracy compared to conventional models (PMV, adaptive) which produced a median accuracy of 0.51; (2) the PMV and adaptive models produced individual comfort predictions only slightly better than random guessing under the relatively mild\ indoor environment\ observed in the field study; and (3) the models based on PCS control behavior produced the best prediction accuracy when individually assessing all categories of field data acquired in the study. We conclude that personal comfort models based on occupants{\textquoteright} heating and cooling behavior can effectively predict individuals{\textquoteright} thermal preference and can therefore be used in everyday comfort management to improve occupant satisfaction and energy use in buildings.}, url = {https://doi.org/10.1016/j.buildenv.2017.12.011}, author = {Joyce Kim and Yuxun Zhou and Stefano Schiavon and Paul Raftery and Gail Brager} } @proceedings {27279, title = {Getting into the zone: how the internet of things can improve energy efficiency and demand response in a commercial building}, journal = {2016 ACEE Summer Study on Energy Efficiency in Buildings}, year = {2016}, abstract = {

While building automation and controls have long used data and analytics to improve building performance, the proliferation of connected devices and sensors adds a new dimension with greater insight to the end user{\textquoteright}s impact on energy consumption. The application of the Internet of Things (IoT) in building systems allows connected devices to communicate and provide enhanced control functions. The increasingly improved visibility at the zonal/room level suggests several ways to improve both energy efficiency and demand response.

UC Berkeley researchers have worked to interface wireless networks with existing building automation systems (BAS) as well as creating virtual BASs by interconnecting control and sensor hardware. The simple Monitoring and Actuation Profile (sMAP) delivers and labels data from various sources into a single compact database; the eXtensible Building Operating System (XBOS) provides the platform for applications to access this data.

This paper describes a collaborative research project between Electric Power Research Institute (EPRI) and UC Berkeley that builds on this work by examining data from several sources: the BAS of a commercial building on campus, wireless indoor environmental sensors (temperature, light, humidity, motion, carbon dioxide), browser-based thermal comfort voting, a networked heated-and-cooled chair, and connected plugload sensors. We describe the results of testing the individual tools, and next steps.

}, url = {https://scholar.google.ca/scholar?oi=bibs\&cluster=1549879750169830149\&btnI=1\&hl=en}, author = {Peng Zhao and Therese Peffer and Ram Narayanamurthy and Gabe Fierro and Paul Raftery and Soazig Kaam and Joyce Kim} } @proceedings {27270, title = {Writing controls sequences for buildings: from HVAC industry enclave to hacker\’s weekend project}, journal = {2016 ACEE Summer Study on Energy Efficiency in Buildings}, year = {2016}, abstract = {

The advent of net zero buildings and increasingly stringent energy codes for new construction points us in the right direction towards reducing our carbon footprint, but what about the energy performance of the vast numbers of existing buildings? Utilizing advanced control sequences in existing buildings can be a cost-effective way of reducing energy use. Smaller commercial buildings (less than 50,000 square feet (sf)) typically have manually controlled systems (e.g., thermostats and light switches), and traditional controls for large commercial buildings{\textemdash}while typically digital (e.g., Building Automation Systems){\textemdash} nevertheless use 20-30 year old technology. These systems were designed to be robust and perform simple tasks such as maintaining temperature and pressure setpoints. However, the controls cannot be easily reprogrammed to incorporate new control strategies. Since the controls are proprietary to each vendor, changing the control logic is expensive. A simple change in ventilation rate in each zone of a building may require hours of programming, rendering the task impractical.

In the past few years at UC Berkeley, computer scientists have been developing innovative software tools and platforms. The simple Monitoring and Actuation Profile (sMAP) software provides a consistent interface to data from various sources such as building sensors, networked devices, websites and other programs allowing people to query and use this data to write flexible and extensible control code. The community around this software has steadily grown and now includes new users with domain-specific knowledge (e.g., mechanical engineers and architects) but with limited background in computer science. This paper explores a few examples of how this new community used these tools to write advanced control sequences in real buildings and test innovative energy efficiency algorithms and components.

}, url = {https://escholarship.org/content/qt3671b82b/qt3671b82b.pdf}, author = {Therese Peffer and Marco Pritoni and Gabe Fierro and Soazig Kaam and Joyce Kim and Paul Raftery} } @proceedings {27264, title = {Well-connected microzones for increased building efficiency and occupant comfort}, journal = {2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments}, year = {2015}, pages = {121-122}, abstract = {Personal Comfort Systems (PCS) are capable of maintaining occupant comfort in buildings despite large deviations from recommended "comfortable" temperatures. We present a novel digital controller for a well-studied (previously analog) PCS, allowing it to report real-time telemetry and respond to programmatic actuation requests. This enables the established capabilities of a PCS to be synergistically combined with occupant-aware building applications, providing new methods of comfort and energy efficiency maximization.}, url = {https://doi.org/10.1145/2821650.2830312}, author = {Michael P Andersen and Gabe Fierro and Sam Kumar and Michael Chen and Leonard Truong and Joyce Kim and Edward A Arens and Hui Zhang and Paul Raftery and David E Culler} } @conference {27292, title = {Effects of Granular Control on Customers\’ Perspective and Behavior with Automated Demand Response Systems}, booktitle = {2014 ACEE Summer Study on Energy Efficiency in Buildings}, year = {2014}, abstract = {

Automated demand response (Auto-DR) is expected to close the loop between buildings and the grid by providing machine-to-machine communications to curtail loads without the need for human intervention. Hence, it can offer more reliable and repeatable demand response results to the grid than the manual approach and make demand response participation a hassle-free experience for customers. However, many building operators misunderstand Auto-DR and are afraid of losing control over their building operation. To ease the transition from manual to AutoDR, we designed and implemented granular control of Auto-DR systems so that building operators could modify or opt out of individual load-shed strategies whenever they wanted.

This paper reports the research findings from this effort demonstrated through a field study in large commercial buildings located in New York City. We focused on (1) understanding how providing granular control affects building operators{\textquoteright} perspective on Auto-DR, and (2) evaluating the usefulness of granular control by examining their interaction with the Auto-DR user interface during test events. Through trend log analysis, interviews, and surveys, we found that: (1) the opt-out capability during Auto-DR events can remove the feeling of being forced into load curtailments and increase their willingness to adopt Auto-DR; (2) being able to modify individual load-shed strategies allows flexible Auto-DR participation that meets the building{\textquoteright}s changing operational requirements; (3) a clear display of automation strategies helps building operators easily identify how Auto-DR is functioning and can build trust in Auto-DR systems.

}, url = {https://scholar.google.ca/scholar?oi=bibs\&cluster=5637755435687393624\&btnI=1\&hl=en}, author = {Oren Schetrit and Joyce Kim and Rongxin Yin and Sila Kiliccote} } @report {27289, title = {Automated Demand Response Technologies and Demonstration in New York City using OpenADR}, year = {2013}, institution = {Lawrence Berkeley National Laboratory}, abstract = {Demand response (DR) {\textendash} allowing customers to respond to reliability requests and market prices by changing electricity use from their normal consumption pattern {\textendash} continues to be seen as an attractive means of demand-side management and a fundamental smart-grid improvement that links supply and demand. Since October 2011, the Demand Response Research Center at Lawrence Berkeley National Laboratory and New York State Energy Research and Development Authority have conducted a demonstration project enabling Automated Demand Response (Auto-DR) in large commercial buildings located in New York City using Open Automated Demand Response (OpenADR) communication protocols. In particular, this project focuses on demonstrating how OpenADR can automate and simplify interactions between buildings and various stakeholders in New York State including the independent system operator, utilities, retail energy providers, and curtailment service providers. In this paper, we present methods to automate control strategies via building management systems to provide event-driven demand response, price response and demand management based on OpenADR signals. We also present cost control opportunities under day-ahead hourly pricing for large customers and Auto-DR control strategies developed for demonstration buildings. Lastly, we discuss the communication architecture and Auto-DR system designed for the demonstration project to automate price response and DR participation.}, url = {https://scholar.google.ca/scholar?oi=bibs\&cluster=7848692629114446579\&btnI=1\&hl=en}, author = {Joyce Jihyun Kim and Sila Kiliccote and Rongxin Yin} } @conference {27285, title = {Automated Price and Demand Response Demonstration for Large Customers in New York City using OpenADR}, booktitle = {International Conference for Enhanced Building Operations}, year = {2013}, abstract = {Open Automated Demand Response (OpenADR), an XML-based information exchange model, is used to facilitate continuous price-responsive operation and demand response participation for large commercial buildings in New York who are subject to the default day-ahead hourly pricing. We summarize the existing demand response programs in New York and discuss OpenADR communication, prioritization of demand response signals, and control methods. Building energy simulation models are developed and field tests are conducted to evaluate continuous energy management and demand response capabilities of two commercial buildings in New York City. Preliminary results reveal that providing machine-readable prices to commercial buildings can facilitate both demand response participation and continuous energy cost savings. Hence, efforts should be made to develop more sophisticated algorithms for building control systems to minimize customer{\textquoteright}s utility bill based on price and reliability information from the electricity grid.}, url = {https://scholar.google.ca/scholar?oi=bibs\&cluster=14090558336326680630\&btnI=1\&hl=en}, author = {Joyce Jihyun Kim and Rongxin Yin and Sila Kiliccote} } @report {27282, title = {Price Responsive Demand in New York Wholesale Electricity Market using OpenADR}, year = {2012}, institution = {Lawrence Berkeley National Laboratory}, abstract = {In New York State, the default electricity pricing for large customers is Mandatory Hourly Pricing (MHP), which is charged based on zonal day-ahead market price for energy. With MHP, retail customers can adjust their building load to an economically optimal level according to hourly electricity prices. Yet, many customers seek alternative pricing options such as fixed rates through retail access for their electricity supply. Open Automated Demand Response (OpenADR) is an XML (eXtensible Markup Language) based information exchange model that communicates price and reliability information. It allows customers to evaluate hourly prices and provide demand response in an automated fashion to minimize electricity costs. This document shows how OpenADR can support MHP and facilitate price responsive demand for large commercial customers in New York City.}, url = {https://doi.org/10.2172/1223011}, author = {Joyce Jihyun Kim and Sila Kiliccote} }