%0 Journal Article %J Applied Energy %D 2019 %T Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems %A David Blum %A K. Arendt %A Lisa Rivalin %A Mary Ann Piette %A Michael Wetter %A C.T. Veje %K building simulation %K hvac %K Model predictive control %K System identification %X

Model predictive control (MPC) for buildings is attracting significant attention in research and industry due to its potential to address a number of challenges facing the building industry, including energy cost reduction, grid integration, and occupant connectivity. However, the strategy has not yet been implemented at any scale, largely due to the significant effort required to configure and calibrate the model used in the MPC controller. While many studies have focused on methods to expedite model configuration and improve model accuracy, few have studied the impact a wide range of factors have on the accuracy of the resulting model. In addition, few have continued on to analyze these factors' impact on MPC controller performance in terms of final operating costs. Therefore, this study first identifies the practical factors affecting model setup, specifically focusing on the thermal envelope. The seven that are identified are building design, model structure, model order, data set, data quality, identification algorithm and initial guesses, and software tool-chain. Then, through a large number of trials, it analyzes each factor's influence on model accuracy, focusing on grey-box models for a single zone building envelope. Finally, this study implements a subset of the models identified with these factor variations in heating, ventilating, and air conditioning MPC controllers, and tests them in simulation of a representative case that aims to optimally cool a single-zone building with time-varying electricity prices. It is found that a difference of up to 20% in cooling cost for the cases studied can occur between the best performing model and the worst performing model. The primary factors attributing to this were model structure and initial parameter guesses during parameter estimation of the model.

%B Applied Energy %V 236 %P 410 - 425 %8 02/2019 %G eng %U https://linkinghub.elsevier.com/retrieve/pii/S0306261918318099https://api.elsevier.com/content/article/PII:S0306261918318099?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0306261918318099?httpAccept=text/plain %! Applied Energy %R 10.1016/j.apenergy.2018.11.093 %0 Conference Paper %B IBPSA Building Simulation 2019 %D 2019 %T Prototyping the BOPTEST Framework for Simulation-Based Testing of Advanced Control Strategies in Buildings %A David Blum %A Filip Jorissen %A Sen Huang %A Yan Chen %A Javier Arroyo %A Kyle Benne %A Yanfei Li %A Valentin Gavan %A Lisa Rivalin %A Lieve Helsen %A Draguna Vrabie %A Michael Wetter %A Marina Sofos %K benchmarking %K building simulation %K Model predictive control %K software development %X

Advanced control strategies are becoming increasingly necessary in buildings in order to meet and balance requirements for energy efficiency, demand flexibility, and occupant comfort. Additional development and widespread adoption of emerging control strategies, however, ultimately require low implementation costs to reduce payback period and verified performance to gain control vendor, building owner, and operator trust. This is difficult in an already first-cost driven and risk-averse industry. Recent innovations in building simulation can significantly aid in meeting these requirements and spurring innovation at early stages of development by evaluating performance, comparing state-of-the-art to new strategies, providing installation experience, and testing controller implementations. This paper presents the development of a simulation framework consisting of test cases and software platform for the testing of advanced control strategies (BOPTEST - Building Optimization Performance Test). The objectives and requirements of the framework, components of a test case, and proposed software platform architecture are described, and the framework is demonstrated with a prototype implementation and example test case.

%B IBPSA Building Simulation 2019 %C Rome, Italy %G eng %0 Journal Article %J Energy and Buildings %D 2018 %T Quantifying the benefits of a building retrofit using an integrated system approach: A case study %A Cynthia Regnier %A Kaiyu Sun %A Tianzhen Hong %A Mary Ann Piette %K Building retrofit %K building simulation %K Energy conservation measures %K energy savings %K integrated design %K integrated system %X

Building retrofits provide a large opportunity to significantly reduce energy consumption in the buildings sector. Traditional building retrofits focus on equipment upgrades, often at the end of equipment life or failure, and result in replacement with marginally improved similar technology and limited energy savings. The Integrated System (IS) retrofit approach enables much greater energy savings by leveraging interactive effects between end use systems, enabling downsized or lower energy technologies. This paper presents a case study in Hawaii quantifying the benefits of an IS retrofit approach compared to two traditional retrofit approaches: a Standard Practice of upgrading equipment to meet minimum code requirements, and an Improved Practice of upgrading equipment to a higher efficiency. The IS approach showed an energy savings of 84% over existing building energy use, much higher than the traditional approaches of 13% and 33%. The IS retrofit also demonstrated the greatest energy cost savings potential. While the degree of savings realized from the IS approach will vary by building and climate, these findings indicate that savings on the order of 50% and greater are not possible without an IS approach. It is therefore recommended that the IS approach be universally adopted to achieve deep energy savings.

%B Energy and Buildings %V 159 %G eng %R 10.1016/j.enbuild.2017.10.090 %0 Journal Article %J Applied Energy %D 2017 %T Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China %A Ying Cui %A Da Yan %A Tianzhen Hong %A Chan Xiao %A Xuan Luo %A Qi Zhang %K Actual weather data %K building simulation %K energy use %K Multiyear simulation %K Peak load   %K Typical year %X

Weather has significant impacts on the thermal environment and energy use in buildings. Thus, accurate weather data are crucial for building performance evaluations. Traditionally, typical year data inputs are used to represent long-term weather data. However, there is no guarantee that a single year represents the changing climate well. In this study, the long-term representation of a typical year was assessed by comparing it to a 55-year actual weather data set. To investigate the weather impact on building energy use, 559 simulation runs of a prototype office building were performed for 10 large cities covering all climate zones in China. The analysis results demonstrated that the weather data varied significantly from year to year. Hence, a typical year cannot reflect the variation range of weather fluctuations. Typical year simulations overestimated or underestimated the energy use and peak load in many cases. With the increase in computational power of personal computers, it is feasible and essential to adopt multiyear simulations for full assessments of long-term building performance, as this will improve decision-making by allowing for the full consideration of variations in building energy use.

%B Applied Energy %V 195 %P 890-904 %8 06/2017 %G eng %R 10.1016/j.apenergy.2017.03.113 %0 Journal Article %J Building Simulation %D 2017 %T Simulation and visualization of energy-related occupant behavior in office buildings %A Yixing Chen %A Xin Liang %A Tianzhen Hong %A Xuan Luo %K Behavior Modeling %K building performance %K building simulation %K energyplus %K occupant behavior %K visualization %X

In current building performance simulation programs, occupant presence and interactions with building systems are over-simplified and less indicative of real world scenarios, contributing to the discrepancies between simulated and actual energy use in buildings. Simulation results are normally presented using various types of charts. However, using those charts, it is difficult to visualize and communicate the importance of occupants’ behavior to building energy performance. This study introduced a new approach to simulating and visualizing energy-related occupant behavior in office buildings. First, the Occupancy Simulator was used to simulate the occupant presence and movement and generate occupant schedules for each space as well as for each occupant. Then an occupant behavior functional mockup unit (obFMU) was used to model occupant behavior and analyze their impact on building energy use through co-simulation with EnergyPlus. Finally, an agent-based model built upon AnyLogic was applied to visualize the simulation results of the occupant movement and interactions with building systems, as well as the related energy performance. A case study using a small office building in Miami, FL was presented to demonstrate the process and application of the Occupancy Simulator, the obFMU and EnergyPlus, and the AnyLogic module in simulation and visualization of energy-related occupant behaviors in office buildings. The presented approach provides a new detailed and visual way for policy makers, architects, engineers and building operators to better understand occupant energy behavior and their impact on energy use in buildings, which can improve the design and operation of low energy buildings.

%B Building Simulation %V 10 %P 785–798 %8 03/2017 %G eng %N 6 %R 10.1007/s12273-017-0355-2 %0 Journal Article %J Building and Environment %D 2017 %T Ten Questions Concerning Occupant Behavior in Buildings: The Big Picture %A Tianzhen Hong %A Da Yan %A Simona D'Oca %A Chien-Fei Chen %K Behavior Modeling %K building performance %K building simulation %K energy use %K interdisciplinary %K occupant behavior %X

Occupant behavior has significant impacts on building energy performance and occupant comfort. However, occupant behavior is not well understood and is often oversimplified in the building life cycle, due to its stochastic, diverse, complex, and interdisciplinary nature. The use of simplified methods or tools to quantify the impacts of occupant behavior in building performance simulations significantly contributes to performance gaps between simulated models and actual building energy consumption. Therefore, it is crucial to understand occupant behavior in a comprehensive way, integrating qualitative approaches and data- and model-driven quantitative approaches, and employing appropriate tools to guide the design and operation of low-energy residential and commercial buildings that integrate technological and human dimensions. This paper presents ten questions, highlighting some of the most important issues regarding concepts, applications, and methodologies in occupant behavior research. The proposed questions and answers aim to provide insights into occupant behavior for current and future researchers, designers, and policy makers, and most importantly, to inspire innovative research and applications to increase energy efficiency and reduce energy use in buildings.

 

%B Building and Environment %G eng %0 Journal Article %J Applied Energy %D 2016 %T A Comparative Study on Energy Performance of Variable Refrigerant Flow Systems and Variable Air Volume Systems in Office Buildings %A Xinqiao Yu %A Da Yan %A Kaiyu Sun %A Tianzhen Hong %A Dandan Zhu %K building simulation %K comparative analysis %K energy performance %K field measurement %K Variable Air Volume (VAV) Systems %K Variable Refrigerant Flow (VRF) Systems %X

Variable air volume (VAV) systems and variable refrigerant flow (VRF) systems are popularly used in office buildings. This study investigated VAV and VRF systems in five typical office buildings in China, and compared their air conditioning energy use. Site survey and field measurements were conducted to collect data of building characteristics and operation. Measured cooling electricity use was collected from sub-metering in the five buildings. The sub-metering data, normalized by climate and operating hours, show that VRF systems consumed much less air conditioning energy by up to 70% than VAV systems. This is mainly due to the different operation modes of both system types leading to much fewer operating hours of the VRF systems. Building simulation was used to quantify the impact of operation modes of VRF and VAV systems on cooling loads using a prototype office building in China. Simulated results show the VRF operation mode leads to much less cooling loads than the VAV operation mode, by 42% in Hong Kong and 53% in Qingdao. The VRF systems operated in the part-time-part-space mode enabling occupants to turn on air-conditioning only when needed and when spaces were occupied, while the VAV systems operated in the full-time-full-space mode limiting occupants’ control of operation. The findings provide insights into VRF systems operation and controls as well as its energy performance, which can inform HVAC designers on system selection and building operators or facility managers on improving VRF system operations.    


 

 

%B Applied Energy %G eng %0 Journal Article %J Energy %D 2015 %T Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance %A Sang Hoon Lee %A Tianzhen Hong %A Mary Ann Piette %A Geof Sawaya %A Yixing Chen %A Sarah C. Taylor-Lange %K building simulation %K Energy conservation measure %K energy modeling %K energyplus %K High Performance computing %K retrofit %X

Small and medium-sized commercial buildings can be retrofitted to significantly reduce their energy use, however it is a huge challenge as owners usually lack of the expertise and resources to conduct detailed on-site energy audit to identify and evaluate cost-effective energy technologies. This study presents a DEEP (database of energy efficiency performance) that provides a direct resource for quick retrofit analysis of commercial buildings. DEEP, compiled from the results of about ten million EnergyPlus simulations, enables an easy screening of ECMs (energy conservation measures) and retrofit analysis. The simulations utilize prototype models representative of small and mid-size offices and retails in California climates. In the formulation of DEEP, large scale EnergyPlus simulations were conducted on high performance computing clusters to evaluate hundreds of individual and packaged ECMs covering envelope, lighting, heating, ventilation, air-conditioning, plug-loads, and service hot water. The architecture and simulation environment to create DEEP is flexible and can expand to cover additional building types, additional climates, and new ECMs. In this study DEEP is integrated into a web-based retrofit toolkit, the Commercial Building Energy Saver, which provides a platform for energy retrofit decision making by querying DEEP and unearthing recommended ECMs, their estimated energy savings and financial payback.

%B Energy %8 07/2015 %2 LBNL-1004494 %& 738 %R 10.1016/j.energy.2015.07.107 %0 Journal Article %J Energy and Buildings %D 2015 %T Data Analysis and Stochastic Modeling of Lighting Energy Use in Large Office Buildings in China %A Xin Zhou %A Da Yan %A Tianzhen Hong %A Xiaoxin Ren %K building simulation %K energy use %K Lighting modeling %K occupant behavior %K office buildings %K Poisson distribution %K stochastic modeling %X

Lighting consumes about 20% to 40% of the total electricity use in large office buildings in China. Commonly in building simulations, static time schedules for typical weekdays, weekends and holidays are assumed to represent the dynamics of lighting energy use in buildings. This approach does not address the stochastic nature of lighting energy use, which can be influenced by occupant behavior in buildings. This study analyzes the main characteristics of lighting energy use over various timescales, based on the statistical analysis of measured lighting energy use data from 15 large office buildings in Beijing and Hong Kong. It was found that in these large office buildings, the 24-hourly variation in lighting energy use was mainly driven by the schedules of the building occupants. Outdoor illuminance levels had little impact on lighting energy use due to the lack of automatic daylighting controls (an effective retrofit measure to reduce lighting energy use) and the relatively small perimeter area exposed to natural daylight. A stochastic lighting energy use model for large office buildings was further developed to represent diverse occupant activities, at six different time periods throughout a day, and also the annual distribution of lighting power across these periods. The model was verified using measured lighting energy use from the 15 buildings. The developed stochastic lighting model can generate more accurate lighting schedules for use in building energy simulations, improving the simulation accuracy of lighting energy use in real buildings.

%B Energy and Buildings %V 86 %P 275-287 %8 01/2015 %2 LBNL-180389 %R 10.1016/j.enbuild.2014.09.071 %0 Journal Article %J Building and Environment %D 2015 %T Data Mining of Space Heating System Performance in Affordable Housing %A Xiaoxin Ren %A Da Yan %A Tianzhen Hong %K affordable housing %K building simulation %K clustering %K data mining %K decision tree %K occupant behavior %K space heating %X

The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems in terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. The results from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.

%B Building and Environment %V 89 %P 1-13 %8 07/2015 %2 LBNL-180239 %R 10.1016/j.buildenv.2015.02.009 %0 Journal Article %J Energy and Buildings %D 2015 %T Development and validation of a new variable refrigerant flow systemmodel in EnergyPlus %A Tianzhen Hong %A Kaiyu Sun %A Rongpeng Zhang %A Ryohei Hinokuma %A Shinichi Kasahara %A Yoshinori Yura %K building simulation %K energy modeling %K energyplus %K Heat pump %K model validation %K Variable refrigerant flow %X

Variable refrigerant flow (VRF) systems vary the refrigerant flow to meet the dynamic zone thermalloads, leading to more efficient operations than other system types. This paper introduces a new modelthat simulates the energy performance of VRF systems in the heat pump (HP) operation mode. Com-pared with the current VRF-HP models implemented in EnergyPlus, the new VRF system model has morecomponent models based on physics and thus has significant innovations in: (1) enabling advanced con-trols, including variable evaporating and condensing temperatures in the indoor and outdoor units, andvariable fan speeds based on the temperature and zone load in the indoor units, (2) adding a detailedrefrigerant pipe heat loss calculation using refrigerant flow rate, operational conditions, pipe length, andpipe insulation materials, (3) improving accuracy of simulation especially in partial load conditions, and(4) improving the usability of the model by significantly reducing the number of user input performancecurves. The VRF-HP model is implemented in EnergyPlus and validated with measured data from fieldtests. Results show that the new VRF-HP model provides more accurate estimate of the VRF-HP systemperformance, which is key to determining code compliance credits as well as utilities incentive for VRFtechnologies.

%B Energy and Buildings %V 117 %8 9/2015 %2 LBNL-1004499 %& 399 %R 10.1016/j.enbuild.2015.09.023 %0 Journal Article %J Energy and Buildings %D 2015 %T Occupancy Schedules Learning Process Through a Data Mining Framework %A Simona D'Oca %A Tianzhen Hong %K Behavioral Pattern %K building simulation %K data mining %K Occupancy schedule %K occupant behavior %K Office Building %X

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10 min interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. The identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.

%B Energy and Buildings %V 88 %P 395-408 %8 02/2015 %2 LBNL-180204 %R 10.1016/j.enbuild.2014.11.065 %0 Journal Article %J Energy and Buildings %D 2015 %T Occupant Behavior Modeling for Building Performance Simulation: Current State and Future Challenges %A Da Yan %A William O'Brien %A Tianzhen Hong %A Xiaohang Feng %A H. Burak Gunay %A Farhang Tahmasebi %A Ardeshir Mahdavi %K building simulation %K energy efficiency %K energy modeling %K energy use %K occupant behavior %X

Occupant behavior is now widely recognized as a major contributing factor to uncertainty of building performance. While a surge of research on the topic has occurred over the past four decades, and particularly the past few years, there are many gaps in knowledge and limitations to current methodologies. This paper outlines the state-of-the-art research, current obstacles and future needs and directions for the following four-step iterative process: (1) occupant monitoring and data collection, (2) model development, (3) model evaluation, and (4) model implementation into building simulation tools. Major themes include the need for greater rigor in experimental methodologies; detailed, honest, and candid reporting of methods and results; and development of an efficient means to implement occupant behavior models and integrate them into building energy modeling programs.

%B Energy and Buildings %V 107 %P 264-278 %8 11/2015 %2 LBNL-1004504 %R 10.1016/j.enbuild.2015.08.032 %0 Journal Article %J Building and Environment %D 2015 %T An Ontology to Represent Energy-Related Occupant Behavior in Buildings. Part II: Implementation of the DNAS framework using an XML schema %A Tianzhen Hong %A Simona D'Oca %A Sarah C. Taylor-Lange %A William J. N. Turner %A Yixing Chen %A Stefano P. Corgnati %K building energy consumption %K building simulation %K energy modeling %K obXML %K occupant behavior %K XML schema %X

Energy-related occupant behavior in buildings is difficult to define and quantify, yet critical to our understanding of total building energy consumption. Part I of this two-part paper introduced the DNAS (Drivers, Needs, Actions and Systems) framework, to standardize the description of energy-related occupant behavior in buildings. Part II of this paper implements the DNAS framework into an XML (eXtensible Markup Language) schema, titled ‘occupant behavior XML’ (obXML). The obXML schema is used for the practical implementation of the DNAS framework into building simulation tools. The topology of the DNAS framework implemented in the obXML schema has a main root element OccupantBehavior, linking three main elements representing Buildings, Occupants and Behaviors. Using the schema structure, the actions of turning on an air conditioner and closing blinds provide two examples of how the schema standardizes these actions using XML. The obXML schema has inherent flexibility to represent numerous, diverse and complex types of occupant behaviors in buildings, and it can also be expanded to encompass new types of behaviors. The implementation of the DNAS framework into the obXML schema will facilitate the development of occupant information modeling (OIM) by providing interoperability between occupant behavior models and building energy modeling programs.

%B Building and Environment %V 94 %P 196-205 %8 08/2015 %N 1 %2 LBNL-1004501 %R 10.1016/j.buildenv.2015.08.006 %0 Journal Article %J Energy and Buildings %D 2015 %T Simulation of Occupancy in Buildings %A Xiaohang Feng %A Da Yan %A Tianzhen Hong %K building simulation %K co-simulation %K occupancy %K occupant behavior %K software module %K stochastic modeling %X

Occupants are involved in a variety of activities in buildings, which drive them to move among rooms, enter or leave a building. In this study, occupancy is defined at four levels and varies with time: (1) the number of occupants in a building, (2) occupancy status of a space, (3) the number of occupants in a space, and (4) the space location of an occupant. Occupancy has a great influence on internal loads and ventilation requirement, thus building energy consumption. Based on a comprehensive review and comparison of literature on occupancy modeling, three representative occupancy models, corresponding to the levels 2–4, are selected and implemented in a software module. Main contributions of our study include: (1) new methods to classify occupancy models, (2) the review and selection of various levels of occupancy models, and (3) new methods to integrate these model into a tool that can be used in different ways for different applications and by different audiences. The software can simulate more detailed occupancy in buildings to improve the simulation of energy use, and better evaluate building technologies in buildings. The occupancy of an office building is simulated as an example to demonstrate the use of the software module.

%B Energy and Buildings %V 87 %P 348-359 %8 01/2015 %2 LBNL-180424 %R 10.1016/j.enbuild.2014.11.067 %0 Journal Article %D 2014 %T Stochastic Modeling of Overtime Occupancy and Its Application in Building Energy Simulation and Calibration %A Kaiyu Sun %A Tianzhen Hong %A Siyue Guo %K building energy use %K building simulation %K model calibration %K occupant behavior %K overtime occupancy %K stochastic modeling %X

Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.

%2 LBNL-6670E %0 Report %D 2014 %T A Technical Framework to Describe Occupant Behavior for Building Energy Simulations %A William J. N. Turner %A Tianzhen Hong %K building simulation %K energy efficiency %K framework %K occupant behavior %K XML schema %X

Green buildings that fail to meet expected design performance criteria indicate that technology alone does not guarantee high performance. Human influences are quite often simplified and ignored in the design, construction, and operation of buildings. Energy-conscious human behavior has been demonstrated to be a significant positive factor for improving the indoor environment while reducing the energy use of buildings. In our study we developed a new technical framework to describe energy-related human behavior in buildings. The energy-related behavior includes accounting for individuals and groups of occupants and their interactions with building energy services systems, appliances and facilities. The technical framework consists of four key components:

  1. the drivers behind energy-related occupant behavior, which are biological, societal, environmental, physical, and economical in nature
  2. the needs of the occupants are based on satisfying criteria that are either physical (e.g. thermal, visual and acoustic comfort) or non-physical (e.g. entertainment, privacy, and social reward)
  3. the actions that building occupants perform when their needs are not fulfilled
  4. the systems with which an occupant can interact to satisfy their needs

The technical framework aims to provide a standardized description of a complete set of human energy-related behaviors in the form of an XML schema. For each type of behavior (e.g., occupants opening/closing windows, switching on/off lights etc.) we identify a set of common behaviors based on a literature review, survey data, and our own field study and analysis. Stochastic models are adopted or developed for each type of behavior to enable the evaluation of the impact of human behavior on energy use in buildings, during either the design or operation phase. We will also demonstrate the use of the technical framework in assessing the impact of occupancy behavior on energy saving technologies. The technical framework presented is part of our human behavior research, a 5-year program under the U.S. - China Clean Energy Research Center for Building Energy Efficiency.

%2 LBNL-6671E %0 Report %D 2013 %T Data Analysis and Modeling of Lighting Energy Use in Large Office Buildings %A Xin Zhou %A Da Yan %A Xiaoxin Ren %A Tianzhen Hong %K building simulation %K energy use %K lighting %K modeling %K occupant behavior %K office buildings %K Poisson distribution %X

Lighting consumes about 20 to 40% of total electricity use in large office buildings in the U.S. and China. In order to develop better lighting simulation models it is crucial to understand the characteristics of lighting energy use. This paper analyzes the main characteristics of lighting energy use over various time scales, based on the statistical analysis of measured lighting energy use of 17 large office buildings in Beijing and Hong Kong. It was found that the daily 24-hour variations of lighting energy use were mainly driven by the schedule of the building occupants. Outdoor illumination levels have little impact on lighting energy use in large office buildings due to the lack of automatic daylighting controls and relatively small perimeter areas. A stochastic lighting energy use model was developed based on different occupant activities during six time periods throughout a day, and the annual distribution of lighting power across those periods. The model was verified using measured lighting energy use of one selected building. This study demonstrates how statistical analysis and stochastic modeling can be applied to lighting energy use. The developed lighting model can be adopted by building energy modeling programs to improve the simulation accuracy of lighting energy use.

%0 Journal Article %J Applied Energy %D 2013 %T A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30-Year Actual Weather Data %A Tianzhen Hong %A Wen-Kuei Chang %A Hung-Wen Lin %K Actual meteorological year %K building simulation %K energy use %K Peak electricity demand %K Typical meteorological year %K weather data %X

Buildings consume more than one third of the world’s total primary energy. Weather plays a unique and significant role as it directly affects the thermal loads and thus energy performance of buildings. The traditional simulated energy performance using Typical Meteorological Year (TMY) weather data represents the building performance for a typical year, but not necessarily the average or typical long-term performance as buildings with different energy systems and designs respond differently to weather changes. Furthermore, the single-year TMY simulations do not provide a range of results that capture yearly variations due to changing weather, which is important for building energy management, and for performing risk assessments of energy efficiency investments. This paper employs large-scale building simulation (a total of 3162 runs) to study the weather impact on peak electricity demand and energy use with the 30-year (1980–2009) Actual Meteorological Year (AMY) weather data for three types of office buildings at two design efficiency levels, across all 17 ASHRAE climate zones. The simulated results using the AMY data are compared to those from the TMY3 data to determine and analyze the differences. Besides further demonstration, as done by other studies, that actual weather has a significant impact on both the peak electricity demand and energy use of buildings, the main findings from the current study include: (1) annual weather variation has a greater impact on the peak electricity demand than it does on energy use in buildings; (2) the simulated energy use using the TMY3 weather data is not necessarily representative of the average energy use over a long period, and the TMY3 results can be significantly higher or lower than those from the AMY data; (3) the weather impact is greater for buildings in colder climates than warmer climates; (4) the weather impact on the medium-sized office building was the greatest, followed by the large office and then the small office; and (5) simulated energy savings and peak demand reduction by energy conservation measures using the TMY3 weather data can be significantly underestimated or overestimated. It is crucial to run multi-decade simulations with AMY weather data to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective.

%B Applied Energy %I Lawrence Berkeley National Laboratory %V 111 %P 333-350 %8 11/2013 %2 LBNL-6280E %R 10.1016/j.apenergy.2013.05.019 %0 Journal Article %J Building Simulation %D 2013 %T Statistical Analysis and Modeling of Occupancy Patterns in Open-Plan Offices using Measured Lighting-Switch Data %A Wen-Kuei Chang %A Tianzhen Hong %K building simulation %K occupancy model %K occupancy pattern %K occupant schedule %K office buildings %K statistical analysis %X

Occupancy profile is one of the driving factors behind discrepancies between the measured and simulated energy consumption of buildings. The frequencies of occupants leaving their offices and the corresponding durations of absences have significant impact on energy use and the operational controls of buildings. This study used statistical methods to analyze the occupancy status, based on measured lighting-switch data in five-minute intervals, for a total of 200 open-plan (cubicle) offices. Five typical occupancy patterns were identified based on the average daily 24-hour profiles of the presence of occupants in their cubicles. These statistical patterns were represented by a one-square curve, a one-valley curve, a two-valley curve, a variable curve, and a flat curve. The key parameters that define the occupancy model are the average occupancy profile together with probability distributions of absence duration, and the number of times an occupant is absent from the cubicle. The statistical results also reveal that the number of absence occurrences decreases as total daily presence hours decrease, and the duration of absence from the cubicle decreases as the frequency of absence increases. The developed occupancy model captures the stochastic nature of occupants moving in and out of cubicles, and can be used to generate a more realistic occupancy schedule. This is crucial for improving the evaluation of the energy saving potential of occupancy based technologies and controls using building simulations. Finally, to demonstrate the use of the occupancy model, weekday occupant schedules were generated and discussed.

%B Building Simulation %V 6 %P 23–32 %8 03/2013 %N 1 %2 LBNL-6080E %R 10.1007/s12273-013-0106-y %0 Conference Paper %B China Annual HVACR Conference %D 2012 %T Comparative research in building energy modeling programs %A Dandan Zhu %A Tianzhen Hong %A Da Yan %A Chuang Wang %K advanced building software: energyplus %K building energy modeling program %K building simulation %K comparison %K dest %K doe-2 %K energyplus %K simulation research group %K test %B China Annual HVACR Conference %C China (in Chinese) %8 06/2011 %G eng %0 Conference Paper %B ACEEE 2012 Summer Study %D 2012 %T An In-Depth Analysis of Space Heating Energy Use in Office Buildings %A Hung-Wen Lin %A Tianzhen Hong %K building energy performance %K building simulation %K simulation research %K simulation research group %K space heating %X

Space heating represents the largest end use in the U.S. buildings and consumes more than 7 trillion Joules of site energy annually [USDOE]. Analyzing building space heating performance and identifying methods for saving energy are quite important. Hence, it is crucial to identify and evaluate key driving factors to space heating energy use to support the design and operation of low energy buildings.

In this study, the prototypical small and large-size office buildings of the USDOE commercial reference buildings, which comply with ASHRAE Standard 90.1-2004, are selected. Key design and operation factors were identified to evaluate their degrees of impact for space heating energy use. Simulation results demonstrate that some of the selected building design and operation parameters have more significant impacts on space heating energy use than others, on the other hand, good operation practice can save more space heating energy than raising design efficiency levels of an office building. Influence of weather data used in simulations on space heating energy is found to be significant. The simulated space heating energy use is further benchmarked against those from similar office buildings in two U.S. commercial buildings databases to better understand the discrepancies.

Simulated results from this study and space heating energy use collected from building databases can both vary in two potentially well overlapped wide ranges depending on details of building design and operation, not necessarily that simulation always under-predicts the reality.

%B ACEEE 2012 Summer Study %I ACEEE %C Asilomar, CA %8 08/2012 %G eng %2 LBNL-5732E %0 Conference Paper %B ACEEE 2012 Summer Study %D 2012 %T A Retrofit Tool for Improving Energy Efficiency of Commercial Buildings %A Mark D. Levine %A Wei Feng %A Jing Ke %A Tianzhen Hong %A Nan Zhou %K building simulation %K buildings %K China %K commercial building %K energy efficiency measures %K retrofit tool %K simulation research group %X

Existing buildings will dominate energy use in commercial buildings in the United States for three decades or longer and even in China for the about two decades. Retrofitting these buildings to improve energy efficiency and reduce energy use is thus critical to achieving the target of reducing energy use in the buildings sector. However there are few evaluation tools that can quickly identify and evaluate energy savings and cost effectiveness of energy conservation measures (ECMs) for retrofits, especially for buildings in China. This paper discusses methods used to develop such a tool and demonstrates an application of the tool for a retrofit analysis. The tool builds on a building performance database with pre-calculated energy consumption of ECMs for selected commercial prototype buildings using the EnergyPlus program. The tool allows users to evaluate individual ECMs or a package of ECMs. It covers building envelope, lighting and daylighting, HVAC, plug loads, service hot water, and renewable energy. The prototype building can be customized to represent an actual building with some limitations. Energy consumption from utility bills can be entered into the tool to compare and calibrate the energy use of the prototype building. The tool currently can evaluate energy savings and payback of ECMs for shopping malls in China. We have used the tool to assess energy and cost savings for retrofit of the prototype shopping mall in Shanghai. Future work on the tool will simplify its use and expand it to cover other commercial building types and other countries. 

%B ACEEE 2012 Summer Study %C Asilomar, CA %8 08/2012 %G eng %U http://aceee.org/files/proceedings/2012/data/papers/0193-000098.pdf#page=1 %2 LBNL-6553E %0 Conference Paper %B China HVAC Simulation Conference %D 2011 %T A Comparison of DeST and EnergyPlus %A Dandan Zhu %A Chuang Wang %A Da Yan %A Tianzhen Hong %K building simulation %K comparison %K dest %K energy modeling %K energyplus %K simulation research %K simulation research group %K test cases %B China HVAC Simulation Conference %C Beijing %8 2011 %G eng %0 Journal Article %J Journal of Building Performance Simulation %D 2011 %T Co-simulation of building energy and control systems with the Building Controls Virtual Test Bed %A Michael Wetter %K building simulation %K co-simulation %K integrated analysis %K modular modelling %X

This article describes the implementation of the Building Controls Virtual Test Bed (BCVTB). The BCVTB is a software environment that allows connecting different simulation programs to exchange data during the time integration, and that allows conducting hardware in the loop simulation. The software architecture is a modular design based on Ptolemy II, a software environment for design and analysis of heterogeneous systems. Ptolemy II provides a graphical model building environment, synchronizes the exchanged data and visualizes the system evolution during run-time. The BCVTB provides additions to Ptolemy II that allow the run-time coupling of different simulation programs for data exchange, including EnergyPlus, MATLAB, Simulink and the Modelica modelling and simulation environment Dymola. The additions also allow executing system commands, such as a script that executes a Radiance simulation. In this article, the software architecture is presented and the mathematical model used to implement the co-simulation is discussed. The simulation program interface that the BCVTB provides is explained. The article concludes by presenting applications in which different state of the art simulation programs are linked for run-time data exchange. This link allows the use of the simulation program that is best suited for the particular problem to model building heat transfer, HVAC system dynamics and control algorithms, and to compute a solution to the coupled problem using co-simulation.

%B Journal of Building Performance Simulation %V 3 %8 11/2010 %G eng %N 4 %R 10.1080/19401493.2010.518631 %0 Conference Paper %B Building Simulation 2011 %D 2011 %T Modeling and simulation of HVAC faults in EnergyPlus %A Mangesh Basarkar %A Philip Haves %A Xiufeng Pang %A Liping Wang %A Tianzhen Hong %K advanced building software: energyplus %K building simulation %K energyplus %K faults %K fouling %K modeling %K sensor offset %K simulation research group %B Building Simulation 2011 %C Australia %8 11/2011 %G eng %0 Journal Article %J Building Simulation %D 2010 %T Assessment of Energy Savings Potential from the Use of Demand Control Ventilation Systems in General Office Spaces in California %A Tianzhen Hong %A William J. Fisk %K building simulation %K california building energy standard %K Commercial Building Ventilation and Indoor Environmental Quality Group %K demand controlled ventilation %K energy savings %K indoor environment department %K other %X

Demand controlled ventilation (DCV) was evaluated for general office spaces in California. A medium size office building meeting the prescriptive requirements of the 2008 California building energy efficiency standards (CEC 2008) was assumed in the building energy simulations performed with the EnergyPlus program to calculate the DCV energy savings potential in five typical California climates. Three design occupancy densities and two minimum ventilation rates were used as model inputs to cover a broader range of design variations. The assumed values of minimum ventilation rates in offices without DCV, based on two different measurement methods, were 81 and 28 cfm per occupant. These rates are based on the co‐author's unpublished analyses of data from EPA's survey of 100 U.S. office buildings. These minimum ventilation rates exceed the 15 to 20 cfm per person required in most ventilation standards for offices. The cost effectiveness of applying DCV in general office spaces was estimated via a life cycle cost analyses that considered system costs and energy cost reductions.

The results of the energy modeling indicate that the energy savings potential of DCV is largest in the desert area of California (climate zone 14), followed by Mountains (climate zone 16), Central Valley (climate zone 12), North Coast (climate zone 3), and South Coast (climate zone 6).

The results of the life cycle cost analysis show DCV is cost effective for office spaces if the typical minimum ventilation rates without DCV is 81 cfm per person, except at the low design occupancy of 10 people per 1000 ft2 in climate zones 3 and 6. At the low design occupancy of 10 people per 1000 ft2, the greatest DCV life cycle cost savings is a net present value (NPV) of $0.52/ft2 in climate zone 14, followed by $0.32/ft2 in climate zone 16 and $0.19/ft2 in climate zone 12. At the medium design occupancy of 15 people per 1000 ft2, the DCV savings are higher with a NPV $0.93/ft2 in climate zone 14, followed by $0.55/ft2 in climate zone 16, $0.46/ft2 in climate zone 12, $0.30/ft2 in climate zone 3, $0.16/ft2 in climate zone 3. At the high design occupancy of 20 people per 1000 ft2, the DCV savings are even higher with a NPV $1.37/ft2 in climate zone 14, followed by $0.86/ft2 in climate zone 16, $0.84/ft2 in climate zone 3, $0.82/ft2 in climate zone 12, and $0.65/ft2 in climate zone 6.

DCV was not found to be cost effective if the typical minimum ventilation rate without DCV is 28 cfm per occupant, except at high design occupancy of 20 people per 1000 ft2 in climate zones 14 and 16.

Until the large uncertainties about the base case ventilation rates in offices without DCV are reduced, the case for requiring DCV in general office spaces will be a weak case.

%B Building Simulation %I Lawrence Berkeley National Laboratory %C Berkeley %V 3 %P 117-124 %8 06/2010 %G eng %N 2 %9 Research Article %2 LBNL-3523E %R 10.1007/s12273-010-0001-8 %0 Journal Article %J Energy and Buildings %D 2009 %T Comparison of energy efficiency between variable refrigerant flow systems and ground source heat pump systems %A Xiaobing Liu %A Tianzhen Hong %K building simulation %K doe-2 %K energy efficiency %K gshp %K vrf %X

With the current movement towards net zero energy buildings, many technologies are promoted with emphasis on their superior energy efficiency. The variable refrigerant flow (VRF) and ground source heat pump (GSHP) systems are probably the most competitive technologies among these. However, there are few studies reporting the energy efficiency of VRF systems compared with GSHP systems. In this article, a preliminary comparison of energy efficiency between the air-source VRF and GSHP systems is presented. The computer simulation results show that GSHP system is more energy efficient than the air-source VRF system for conditioning a small office building in two selected US climates. In general, GSHP system is more energy efficient than the air-source VRV system, especially when the building has significant heating loads. For buildings with less heating loads, the GSHP system could still perform better than the air-source VRF system in terms of energy efficiency, but the resulting energy savings may be marginal.

%B Energy and Buildings %V 42 %P 584-589 %8 2009 %G eng %N 5 %9 Research Article %R 10.1016/j.enbuild.2009.10.028 %0 Report %D 2008 %T EnergyPlus Analysis Capabilities for Use in Title 24 %A Tianzhen Hong %A Walter F. Buhl %A Philip Haves %K building simulation %K code compliance %K energyplus %K title 24 %I LBNL %8 2008 %G eng %U http://www.escholarship.org/uc/item/0z78090x %2 LBNL-822E %0 Journal Article %J Building and Environment %D 2000 %T Building simulation: an overview of development and information sources %A Tianzhen Hong %A Siaw K. Chou %A T.Y. Bong %K building simulation %X

We review the state-of-the-art on the development and application of computer-aided building simulation by addressing some crucial questions in the field. Although the answers are not intended to be comprehensive, they are sufficiently varied to provide an overview ranging from the historical and technical development to choosing a suitable simulation program and performing building simulation. Popular icons of major interested agencies and simulation tools and key information sources are highlighted. Future trends in the design and operation of energy-efficient ‘green' buildings are briefly described.

%B Building and Environment %V 35 %P 347-361 %8 05/2000 %G eng %N 4 %9 Review Article %& 347 %R 10.1016/S0360-1323(99)00023-2 %0 Journal Article %J Building and Environment %D 1999 %T A design day for building load and energy estimation %A Tianzhen Hong %A Siaw K. Chou %A T.Y. Bong %K building simulation %K design day %K doe-2 %K peak load calculation %K weather data %X

We describe how a design day for building energy performance simulation can be selected from a typical meteorological year of a location. The advantages of the design day weather file are its simplicity and flexibility in use with simulation programs. The design day is selected using a weather parameter comprising the daily average dry bulb temperature and total solar insolation. The selection criterion addresses the balance between the need to minimise the part-load performance of the air-conditioning systems and plants and the number of hours of load not met. To validate the versatility of the design day weather file, we compare simulation results of the peak load and load profile of a building obtained from the DOE-2.1E code and a specially developed load estimation program, PEAKLOAD. PEAKLOAD is developed using the transfer function method and ASHRAE databases. Comparative results are in good agreement, indicating that a design day thus selected can be used when quick answers are required and simulations using a TMY file cannot be easily done or justified.

%B Building and Environment %V 34 %P 469-477 %8 07/1999 %G eng %N 4 %& 469 %R 10.1016/S0360-1323(98)00035-3 %0 Journal Article %J Building and Environment %D 1997 %T IISABRE: An integrated building simulation environment %A Tianzhen Hong %A Yi Jiang %K btp %K building simulation %K dest %K energy performance %K gui %X

An integrated building simulation environment, IISABRE, is introduced. IISABRE consists of CABD, BTP and IISPAM. CABD is an AutoCAD-based building descriptor enabling users to draw a building and define information. Some design tools are built into CABD, and a STEP-based building database can be generated, which provides an open mechanism to share the building database with other programs. BTP is a program for the detailed dynamic simulation of building thermal performance. With a PC 486DX50 (8M RAM) running in MS-Windows 3.11, BTP needs about 40 minutes to calculate the annual hourly energy demand for a building with 20 zones. IISPAM is a knowledge-based system for translating the STEP-based building database into ASCII-based data files for BTP. IISABRE can be widely employed in the field of building environmental engineering in order to improve the energy efficiency of buildings and the thermal comfort of the indoor environment.

%B Building and Environment %V 32 %P 219-224 %8 1997 %G eng %N 3 %9 Research Article %R 10.1016/S0360-1323(96)00057-1