@article {31665, title = {Incorporating machine learning with building network analysis to predict multi-building energy use}, journal = {Energy and Buildings}, volume = {186}, year = {2019}, month = {06/2019}, pages = {80 - 97}, abstract = {

Predicting multi-building energy use at campus or city district scale has recently gained more attention; and more researchers have started to define reference buildings and study inter-impact between building groups. However, how to integrate the relationship to define reference buildings and predict multi-building energy use, using significantly less amount of building data and reducing complexity of prediction models, remains an open research question. To resolve this, this study proposed a novel method to predict multi-building energy use by integrating a social network analysis (SNA) with an Artificial Neural Network (ANN) technique. The SNA method was used to establish a building network (BN) by identifying reference buildings and determine correlations between reference buildings and non-reference buildings. The ANN technique was applied to learn correlations and historical building energy use, and then used to predict multi-building energy use. To validate the SNA-ANN method, 17 buildings in the Southeast University campus, located in Nanjing, China, were studied. These buildings have three years of actual monthly electricity use data and were grouped into four types: office, educational, laboratory, and residential. The results showed the integrated SNA-ANN method achieved average prediction accuracies of 90.67\% for the office group, 90.79\% for the educational group, 92.34\% for the laboratory group, and 83.32\% for the residential group. The results demonstrated the proposed SNA-ANN method achieved an accuracy of 90.28\% for the predicted energy use for all building groups. Finally, this study provides insights into advancing the interdisciplinary research on multi-building energy use prediction.

}, keywords = {Artificial neural networks, Building network, cold winter and hot summer climate, Energy use prediction, Machine learning}, issn = {03787788}, doi = {10.1016/j.enbuild.2019.01.002}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0378778818319765}, author = {Xiaodong Xu and Wei Wang and Tianzhen Hong and Jiayu Chen} } @article {31671, title = {Inferring occupant counts from Wi-Fi data in buildings through machine learning}, journal = {Building and Environment}, volume = {158}, year = {2019}, month = {05/2019}, pages = {281 - 294}, abstract = {

An important approach to curtail building energy consumption is to optimize building control based on occupancy information. Various studies proposed to estimate occupant counts through different approaches and sensors. However, high cost and privacy concerns remain as major barriers, restricting the practice of occupant count detection. In this study, we propose a novel method utilizing data from widely deployed Wi-Fi infrastructure to infer occupant counts through machine learning. Compared with the current indirect measurement methods, our method improves the performance of estimating people count: (1) we avoid privacy concerns by anonymizing and reshuffling the MAC addresses on a daily basis; (2) we adopted a heuristic feature engineer approach to cluster connected devices into different types based on their daily connection duration. We tested the method in an office building located in California. In an area with an average occupancy of 22{\textendash}27 people and a peak occupancy of 48{\textendash}74 people, the root square mean error on the test set is less than four people. The error is within two people counts for more than 70\% of estimations, and less than six counts for more than 90\% of estimations, indicating a relatively high accuracy. The major contribution of this study is proposing a novel and accurate approach to detect occupant counts in a non-intrusive way, i.e., utilizing existing Wi-Fi infrastructure in buildings without requiring the installation of extra hardware or sensors. The method we proposed is generic and could be applied to other commercial buildings to infer occupant counts for energy efficient building control.

}, keywords = {Building control, Machine learning, Occupancy estimation, Occupant count, Random forest, Wi-Fi data}, issn = {03601323}, doi = {10.1016/j.buildenv.2019.05.015}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0360132319303336}, author = {Zhe Wang and Tianzhen Hong and Mary Ann Piette and Marco Pritoni} } @article {31668, title = {Integrating physics-based models with sensor data: An inverse modeling approach}, journal = {Building and Environment}, volume = {154}, year = {2019}, month = {05/2019}, pages = {23 - 31}, abstract = {

Physics-based building energy models (e.g., EnergyPlus) rely on some unknown input parameters (e.g., zone air infiltration) that are hard to measure, leading to uncertainty in simulation results especially for existing buildings with varying operating conditions. With the increasing deployment of smart thermostats, zone air temperature data are readily available, posing a new opportunity for building energy modeling if such data can be harnessed. This study presents a novel inverse modeling approach which inverses the zone air heat balance equation and uses the measured zone air temperature to analytically calculate the zone air infiltration rate and zone internal thermal mass (e.g., furniture, interior partitions), which are two important model parameters with great variability and difficult to measure. This paper introduces the technical concept and algorithms of the inverse models, their implementation in EnergyPlus, and verification using EnergyPlus simulated building performance data. The inverse modeling approach provides new opportunities for integrating data from massive IoT sensors and devices to enhance the accuracy of simulation results which are used to inform decision making on energy retrofits and efficiency improvements of existing buildings.

}, keywords = {building performance simulation, energyplus, infiltration, internal thermal mass, inverse model, sensor data}, issn = {03601323}, doi = {10.1016/j.buildenv.2019.03.006}, url = {https://linkinghub.elsevier.com/retrieve/pii/S036013231930160X}, author = {Tianzhen Hong and Sang Hoon Lee} } @article {32156, title = {An inverse approach to solving zone air infiltration rate and people count using indoor environmental sensor data}, journal = {Energy and Buildings}, volume = {198}, year = {2019}, month = {Jan-09-2019}, pages = {228 - 242}, abstract = {

Physics-based simulation of energy use in buildings is widely used in building design and performance rating, controls design and operations. However, various challenges exist in the modeling process. Model parameters such as people count and air infiltration rate are usually highly uncertain, yet they have significant impacts on the simulation accuracy. With the increasing availability and affordability of sensors and meters in buildings, a large amount of measured data has been collected including indoor environmental parameters, such as room air dry-bulb temperature, humidity ratio, and CO2 concentration levels. Fusing these sensor data with traditional energy modeling poses new opportunities to improve simulation accuracy. This study develops a set of physics-based inverse algorithms which can solve the highly uncertain and hard-to-measure building parameters such as zone-level people count and air infiltration rate. A simulation-based case study is conducted to verify the inverse algorithms implemented in EnergyPlus covering various sensor measurement scenarios and different modeling use cases. The developed inverse models can solve the zone people count and air infiltration at sub-hourly resolution using the measured zone air temperature, humidity and/or CO2 concentration given other easy-to-measure model parameters are known.

}, keywords = {energyplus, infiltration, Inverse problems, people count, sensor data, zone air parameters}, issn = {03787788}, doi = {10.1016/j.enbuild.2019.06.008}, author = {Li, Han and Hong, Tianzhen and Sofos, Marina} } @article {31308, title = {Impact of post-rainfall evaporation from porous roof tiles on building cooling load in subtropical China}, journal = {Applied Thermal Engineering}, volume = {142}, year = {2018}, month = {09/2018}, pages = {391 - 400}, abstract = {

Rainfall occurs frequently in subtropical regions of China, with the subsequent water evaporation from building roofs impacting the thermal performance and the energy consumption of buildings. We proposed a novel simulation method using actual meteorological data to evaluate this impact. New features were developed in EnergyPlus to enable the simulation: (1) an evaporation latent heat flux source term was added to the heat balance equation of the external surface and (2) algorithms for the evaporative cooling module (ECM) were developed and implemented into EnergyPlus. The ECM experimental results showed good agreement with the simulated results. The ECM was used to assess the impact of evaporation from porous roof tiles on the cooling load of a one-floor building in subtropical China. The results show that the evaporation process decreased the maximal values of the external and internal roof surface temperatures by up to 6.4 {\textdegree}C and 3.2 {\textdegree}C, respectively, while the lower internal surface temperature decreased the room accumulated cooling load by up to 14.8\% during the hot summer period. The enhanced EnergyPlus capability can be used to evaluate the evaporative cooling performance of roofs with water-storage mediums, as well as to quantify their impact on building cooling loads.

}, keywords = {Building energy simulation, cooling load, energyplus, Evaporative Cooling, Rainfall event, Subtropical China}, issn = {13594311}, doi = {10.1016/j.applthermaleng.2018.07.033}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1359431117356107https://api.elsevier.com/content/article/PII:S1359431117356107?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1359431117356107?httpAccept=text/plain}, author = {Lei Zhang and Rongpeng Zhang and Tianzhen Hong and Yu Zhang and Qinglin Meng} } @article {30485, title = {Impacts of Building Geometry Modeling Methods on the Simulation Results of Urban Building Energy Models}, journal = {Applied Energy}, volume = {215}, year = {2018}, abstract = {

Urban-scale building energy modeling (UBEM){\textemdash}using building modeling to understand how a group of buildings will perform together{\textemdash}is attracting increasing attention in the energy modeling field. Unlike modeling a single building, which will use detailed information, UBEM generally uses existing building stock data consisting of high-level building information. This study evaluated the impacts of three zoning methods and the use of floor multipliers on the simulated energy use of 940 office and retail buildings in three climate zones using City Building Energy Saver. The first zoning method, OneZone, creates one thermal zone per floor using the target building{\textquoteright}s footprint. The second zoning method, AutoZone, splits the building{\textquoteright}s footprint into perimeter and core zones. A novel, pixel-based automatic zoning algorithm is developed for the AutoZone method. The third zoning method, Prototype, uses the U.S. Department of Energy{\textquoteright}s reference building prototype shapes. Results show that simulated source energy use of buildings with the floor multiplier are marginally higher by up to 2.6\% than those modeling each floor explicitly, which take two to three times longer to run. Compared with the AutoZone method, the OneZone method results in decreased thermal loads and less equipment capacities: 15.2\% smaller fan capacity, 11.1\% smaller cooling capacity, 11.0\% smaller heating capacity, 16.9\% less heating loads, and 7.5\% less cooling loads. Source energy use differences range from -7.6\% to 5.1\%. When comparing the Prototype method with the AutoZone method, source energy use differences range from -12.1\% to 19.0\%, and larger ranges of differences are found for the thermal loads and equipment capacities. This study demonstrated that zoning methods have a significant impact on the simulated energy use of UBEM. One recommendation resulting from this study is to use the AutoZone method with floor multiplier to obtain accurate results while balancing the simulation run time for UBEM.

}, keywords = {CityBES, energyplus, Floor multiplier, Geometry Representation, Urban Building Energy Modeling, Zoning Method}, doi = {10.1016/j.apenergy.2018.02.073}, author = {Yixing Chen and Tianzhen Hong} } @article {30481, title = {IEA EBC Annex 53: Total Energy Use in Buildings {\textendash} Analysis and Evaluation Methods}, journal = {Energy and Buildings}, volume = {152}, year = {2017}, abstract = {

One of the most significant barriers to achieving deep building energy efficiency is a lack of knowledge about the factors determining energy use. In fact, there is often a significant discrepancy between designed and real energy use in buildings, which is poorly understood but are believed to have more to do with the role of human behavior than building design. Building energy use is mainly influenced by six factors: climate, building envelope, building services and energy systems, building operation and maintenance, occupants{\textquoteright} activities and behavior, and indoor environmental quality. In the past, much research focused on the first three factors. However, the next three human-related factors can have an influence as significant as the first three. Annex 53 employed an interdisciplinary approach, integrating building science, architectural engineering, computer modeling and simulation, and social and behavioral science to develop and apply methods to analyze and evaluate the real energy use in buildings considering the six influencing factors. Outcomes from Annex 53 improved understanding and strengthen knowledge regarding the robust prediction of total energy use in buildings, enabling reliable quantitative assessment of energy-savings measures, policies, and techniques.

}, keywords = {energy data definition, energy modeling, energy monitoring, occupant behavior, Performance Evaluation, real energy use}, doi = {10.1016/j.enbuild.2017.07.038}, author = {Hiroshi Yoshino and Tianzhen Hong and Natasa Nord} } @article {30482, title = {IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings}, journal = {Energy and Building}, volume = {156}, year = {2017}, abstract = {

More than 30\% of the total primary energy in the world is consumed in buildings. It is crucial to reduce building energy consumption in order to preserve energy resources and mitigate global climate change. Building performance simulations have been widely used for the estimation and optimization of building performance, providing reference values for the assessment of building energy consumption and the effects of energy-saving technologies. Among the various factors influencing building energy consumption, occupant behavior has drawn increasing attention. Occupant behavior includes occupant presence, movement, and interaction with building energy devices and systems. However, there are gaps in occupant behavior modeling as different energy modelers have employed varied data and tools to simulate occupant behavior, therefore producing different and incomparable results. Aiming to address these gaps, the International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66 has established a scientific methodological framework for occupant behavior research, including data collection, behavior model representation, modeling and evaluation approaches, and the integration of behavior modeling tools with building performance simulation programs. Annex 66 also includes case studies and application guidelines to assist in building design, operation, and policymaking, using interdisciplinary approaches to reduce energy use in buildings and improve occupant comfort and productivity. This paper highlights the key research issues, methods, and outcomes pertaining to Annex 66, and offers perspectives on future research needs to integrate occupant behavior with the building life cycle.

}, keywords = {building performance, energy modeling, energy use, IEA EBC Annex 66, Interdisciplinary approach, occupant behavior}, doi = {10.1016/j.enbuild.2017.09.084}, author = {Da Yan and Tianzhen Hong and Bing Dong and Ardeshir Mahdavi and Simona D{\textquoteright}Oca and Isabella Gaetani and Xiaohang Feng} } @article {30297, title = {The Impact of Evaporation Process on Thermal Performance of Roofs - Model Development and Numerical Analysis}, journal = {Energy and Buildings}, year = {2016}, keywords = {Evaporative Cooling, model development, Net zero energy building, Numerical analysis, Passive techniques, Porous building material, Roof thermal performance}, author = {Lei Zhang and Rongpeng Zhang and Yu Zhang and Tianzhen Hong and Qinglin Meng and Yanshan Feng} } @article {30317, title = {Improving the accuracy of energy baseline models for commercial buildings with occupancy data}, journal = {Applied Energy}, year = {2016}, abstract = {

More than 80\% of energy is consumed during operation phase of a building{\textquoteright}s life cycle, so energy efficiency retrofit for existing buildings is considered a promising way to reduce energy use in buildings. The investment strategies of retrofit depend on the ability to quantify energy savings by {\textquotedblleft}measurement and verification{\textquotedblright} (M\&V), which compares actual energy consumption to how much energy would have been used without retrofit (called the {\textquotedblleft}baseline{\textquotedblright} of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essential for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. The results from this study can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M\&V and facilitate investment strategies of energy efficiency retrofit.

}, keywords = {baseline model, building energy use, Energy Efficiency Retrofit, Measurement and verification, occupancy}, author = {Xin Liang and Tianzhen Hong and Geoffrey Qiping Shen} } @article {60963, title = {Introduction to an occupant behavior motivation survey framework}, year = {2016}, abstract = {

An increasing body of research is underlying the need to foster energy behaviors and interaction with technology as a way to achieve energy savings in office buildings. However, engaging office users into more {\textquotedblleft}forgiving{\textquotedblright} comfort-adaptive behavior is not a trivial task, since neither consequences nor benefits for changing behavior have visible or tangible effects on them personally. Since the 70{\textquoteright}s, survey studies in the field of building science have been used to gain better understanding of multidisciplinary drivers of occupant behavior with respect to comfort and energy requirements in buildings. Rather than focusing on individual behaviors {\textendash} and influencing factors {\textendash} purpose of this survey research is to provide quantitative descriptions on the collective and social motivations within the complexity of different social groups in working environment, under different geographical context, culture and norms. The resultant questionnaire survey emerges as a combination of traditional and adaptive comfort theories, merged with social science theory. The questionnaire explores to what extent the occupant energy-related behavior in working spaces is driven by a motivational sphere influenced by i) comfort requirements, ii) habits, iii) intentions and iv) actual control of building systems. The key elements of the proposed occupant behavior motivational framework are grounded on the Driver Need Action System framework for energy-related behaviors in buildings. Goal of the study is to construct an additional layer of standardized knowledge to enrich the state-of-the-art on energy-related behavior in office buildings.

}, keywords = {DNAs framework, energy-related occupant behavior, motivation, office buildings, questionnaire survey}, author = {Simona D{\textquoteright}Oca and Stefano P. Corgnati and Anna Laura Pisello and Tianzhen Hong} } @article {59957, title = {An Insight into Actual Energy Use and Its Drivers in High-Performance Buildings}, year = {2015}, abstract = {

Using portfolio analysis and individual detailed case studies, we studied the energy performance and drivers of energy use in 51 high-performance office buildings in the U.S., Europe, China, and other parts of Asia. Portfolio analyses revealed that actual site energy use intensity (EUI) of the study buildings varied by a factor of as much as 11, indicating significant variation in real energy use in HPBs worldwide. Nearly half of the buildings did not meet the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) Standard 90.1-2004 energy target, raising questions about whether a building{\textquoteright}s certification as high performing accurately indicates that a building is energy efficient and suggesting that improvement in the design and operation of HPBs is needed to realize their energy-saving potential. We studied the influence of climate, building size, and building technologies on building energy performance and found that although all are important, none are decisive factors in building energy use. EUIs were widely scattered in all climate zones. There was a trend toward low energy use in small buildings, but the correlation was not absolute; some small HPBs exhibited high energy use, and some large HPBs exhibited low energy use. We were unable to identify a set of efficient technologies that correlated directly to low EUIs. In two case studies, we investigated the influence of occupant behavior as well as operation and maintenance on energy performance and found that both play significant roles in realizing energy savings. We conclude that no single factor determines the actual energy performance of HPBs, and adding multiple efficient technologies does not necessarily improve building energy performance; therefore, an integrated design approach that takes account of climate, technology, occupant behavior, and operations and maintenance practices should be implemented to maximize energy savings in HPBs. These findings are intended to help architects, engineers, operators, and policy makers improve the design and operation of HPBs.

}, keywords = {actual energy use, building technologies, driving factors, high-performance buildings, integrated design, performance rating}, author = {Cheng Li and Tianzhen Hong and Da Yan} } @article {59969, title = {Integrated Design for High Performance Buildings}, year = {2014}, author = {Tianzhen Hong and Cheng Li and Richard C. Diamond and Da Yan and Qi Zhang and Xin Zhou and Siyue Guo and Kaiyu Sun and Jingyi Wang} } @conference {3442, title = {An Improved Simple Chilled Water Cooling Coil Model}, booktitle = {SimBuild 2012 IBPSA Conference}, year = {2012}, month = {08/2012}, abstract = {

The accurate prediction of cooling and dehumidification coil performance is important in model-based fault detection and in the prediction of HVAC system energy consumption for support of both design and operations. It is frequently desirable to use a simple cooling coil model that does not require detailed specification of coil geometry and material properties. The approach adopted is to match the overall UA of the coil to the rating conditions and to estimate the air-side and water-side components of the UA using correlations developed by Holmes (1982). This approach requires some geometrical information about the coil and the paper investigates the sensitivity of the overall performance prediction to uncertainties in this information, including assuming a fixed ratio of air-side to water-side UA at the rating condition. Finally, simulation results from different coil models are compared, and experimental data are used to validate the improved cooling coil model.

}, author = {Liping Wang and Philip Haves and Walter F. Buhl} } @conference {2670, title = {An In-Depth Analysis of Space Heating Energy Use in Office Buildings}, booktitle = {ACEEE 2012 Summer Study}, year = {2012}, month = {08/2012}, publisher = {ACEEE}, organization = {ACEEE}, address = {Asilomar, CA}, abstract = {

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.

}, keywords = {building energy performance, building simulation, simulation research, simulation research group, space heating}, author = {Hung-Wen Lin and Tianzhen Hong} } @proceedings {217, title = {Impact of time-splitting schemes on the accuracy of FFD simulations}, year = {2010}, pages = {55-60}, address = {Syracuse, NY}, author = {Jianjun Hu and Wangda Zuo and Qingyan Chen} } @article {249, title = {Impacts of Static Pressure Reset on VAV System Air Leakage, Fan Power, and Thermal Energy}, journal = {ASHRAE Transactions}, volume = {116}, year = {2010}, pages = {428-436}, author = {Mingsheng Liu and Jingjuan Feng and Zhan Wang and Keke Zheng and Xiufeng Pang} } @article {211, title = {Improvements on FFD modeling by using different numerical schemes}, journal = {Numerical Heat Transfer, Part B Fundamentals}, volume = {58}, year = {2010}, pages = {1-16}, author = {Wangda Zuo and Jianjun Hu and Qingyan Chen} } @proceedings {218, title = {Improvements on the fast fluid dynamics model for indoor airflow simulation}, year = {2010}, pages = {539-546}, address = {New York, NY}, author = {Wangda Zuo and Qingyan Chen} } @proceedings {60, title = {An implementation of co-simulation for performance prediction of innovative integrated HVAC systems in buildings}, year = {2009}, month = {07/2009}, pages = {724-731}, address = {Glasgow, Scotland}, abstract = {Integrated performance simulation of buildings and heating, ventilation and air-conditioning (HVAC)systems can help reducing energy consumption and increasing level of occupant comfort. However, no singe building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to accommodate the ever-increasing complexity and rapid innovations in building and system technologies. One way to alleviate this problem is to use co-simulation. The co-simulation approach represents a particular case of simulation scenario where at least two simulators solve coupled differential-algebraic systems of equations and exchange data that couples these equations during the time integration. This paper elaborates on issues important for co-simulation realization and discusses multiple possibilities to justify the particular approach implemented in a co-simulation prototype. The prototype is verified and validated against the results obtained from the traditional simulation approach. It is further used in a case study for the proof-of-concept, to demonstrate the applicability of the method and to highlight its benefits. Stability and accuracy of different coupling strategies are analyzed to give a guideline for the required coupling frequency. The paper concludes by defining requirements and recommendations for generic co-simulation implementations.}, url = {http://www.ibpsa.org/proceedings/BS2009/BS09_0724_731.pdf}, author = {Marija Trcka and Michael Wetter and Jan Hensen} } @article {251, title = {Improving Control and Operation of a Single Duct VAV System through CCLEP}, journal = {ASHRAE Transactions}, volume = {115}, year = {2009}, month = {07/2009}, pages = {760-768}, abstract = {

With the energy crisis of the early 1970s came the realization that buildings could be made much more efficient without sacrificing comfort. Over the last 30 years, use of variable air volume systems has become common practice. Many variable air volume (VAV) systems with pneumatic controls were installed in the 1980s and are still in use. However, these systems often have outdated control strategies and deficient mechanical systems are deficient, which may cause occupant discomfort and excess energy consumption.

An ASHRAE committee proposed building commissioning in 1988 to ensure that system performance met design specifications. Continuous Commissioning (CC[R]) technology was developed and implemented in 1992. CC is an ongoing process to resolve operating problems, improve comfort, optimize energy use and identify retrofits for existing commercial and institutional buildings and central plant facilities [1-5]. Since 1999, the Energy Systems Laboratory (ESL) at the University of Nebraska has conducted extensive research to implement optimal system control during the design phase and finalize the optimal setpoints after system installation. ESL researchers have developed and implemented the Continuous Commissioning Leading Energy Project (CCLEP) process with federal and industry support. The CCLEP process has two stages: the contracting stage and the implementation stage. During the contracting stage, a comprehensive technical evaluation is performed. The CCLEP implementation stage involves planning, retrofit and trouble shooting, and optimization and follow-up. The CCLEP process, procedures and seven case study results are presented in [6].

This paper presents information on the case study facility, existing and improved control sequences, and building performance improvement and energy consumption measures before and after CCLEP implementation

}, author = {Young-Hum Cho and Mingsheng Liu and Xiufeng Pang} } @conference {2648, title = {IFC BIM-based Methodology for Semi-Automated Building Energy Performance Simulation}, booktitle = {CIB W78, Proc. 25th conf}, year = {2008}, month = {07/2008}, address = {Santiago, Chile}, author = {Vladimir Bazjanac} } @conference {2640, title = {Integrating the Specification, Acquisition and Processing of Building Performance Information}, booktitle = {12th International Conference on Computing in Civil and Building Engineering}, year = {2008}, month = {10/2008}, address = {Beijing, China}, author = {Martin Keller and James O{\textquoteright}Donnell and Karsten Menzel and Marcus Keane and Ufuk G{\"o}k{\c c}e} } @article {261, title = {The impacts of facade and ventilation strategies on indoor thermal environment for a naturally ventilated residential building in Singapore}, journal = {Building and Environment}, volume = {42}, year = {2007}, month = {12/2007}, pages = {4006-4015}, author = {Liping Wang and Nyuk Hien Wong} } @conference {254, title = {Integrated Static Pressure Reset with Fan Air Flow Station in Dual-duct VAV System Control}, booktitle = {ASME Energy Sustainability}, year = {2007}, month = {2007}, address = {Long Beach, CA}, author = {Lixia Wu and Mingsheng Liu and Gang Wang and Xiufeng Pang} } @article {275, title = {Investigation of the possibility of applying natural ventilation for thermal comfort in residential buildings in Singapore}, journal = {Architectural Science Review}, volume = {50}, year = {2007}, month = {2007}, pages = {190-199}, author = {Liping Wang and Nyuk Hien Wong} } @conference {417, title = {IFC to CONTAM Translator}, booktitle = {SimBuild 2006}, year = {2006}, month = {08/2006}, address = {Boston, MA}, author = {Mangesh Basarkar and Muthasamy Swami} } @conference {286, title = {The impacts of facade designs: orientations, window to wall ratios and shading devices on indoor environment for naturally ventilated residential buildings in Singapore}, booktitle = {the 23st International conference on Passive and Low energy architecture, Geneva}, year = {2006}, month = {09/2006}, author = {Liping Wang and Nyuk Hien Wong} } @proceedings {2797, title = {Implementation of an Earth Tube System Into EnergyPlus Program}, year = {2006}, month = {08/2006}, address = {Cambridge, MA, USA}, author = {Kwang Ho Lee and Richard K. Strand} } @conference {292, title = {The impacts of facade and ventilation strategies on indoor thermal environment for a naturally ventilated residential building in Singapore}, booktitle = {the 10th International conference on Indoor Air Quality and Climate, Beijing}, year = {2005}, month = {2005}, author = {Liping Wang and Nyuk Hien Wong} } @conference {2667, title = {Improving the Data Available to Simulation Programs}, booktitle = {IBPSA Building Simulation 2005}, year = {2005}, month = {08/2005}, address = {Montreal, Canada}, abstract = {

Building performance simulation tools have significantly improved in quality and depth of analysis capability over the past thirty-five years. Yet despite these increased capabilities, simulation programs still depend on user entry for significant data about building components, loads, and other typically scheduled inputs. This often forces users to estimate values or find previously compiled sets of data for these inputs. Often there is little information about how the data were derived, what purposes it is fit for, which standards apply, uncertainty associated with each data field as well as a general description of the data.

A similar problem bedeviled access to weather data and Crawley, Hand, and Lawrie (1999) described a generalized weather data format developed for use with two energy simulation programs which has subsequently lead to a repository which is accessed by thousands of practitioners each year.

This paper describes a generalized format and data documentation for user input{\textemdash}whether it is building envelope components, scheduled loads, or environmental emissions{\textemdash}the widgets upon which all models are dependant. We present several examples of the new input data format including building envelope component, a scheduled occupant load, and environmental emissions.

}, author = {Jon W. Hand and Drury B. Crawley and Michael Donn and Linda K. Lawrie} } @conference {11938, title = {IFC HVAC interface to EnergyPlus - A case of expanded interoperability for energy simulation}, booktitle = {SimBuild 2004}, year = {2004}, month = {08/2004}, address = {Boulder, CO}, author = {Vladimir Bazjanac and Tobias Maile} } @proceedings {3401, title = {IFC HVAC Interface to EnergyPlus: A Case of Expanded Interoperability for Energy Simulation}, year = {2004}, month = {08/2004}, address = {Boulder, Colorado, USA}, author = {Vladimir Bazjanac and Tobias Maile} } @proceedings {2829, title = {Improvement of the ASHRAE Secondary HVAC Toolkit Simple Cooling Coil Model for Simulation}, year = {2004}, month = {08/2004}, address = {Boulder, Colorado, USA}, author = {Rahul Chillar and Richard J. Liesen} } @conference {11947, title = {Improving building energy performance simulation with software interoperability}, booktitle = {Building Simulation 2003}, volume = {1}, year = {2003}, month = {08/2003}, address = {Eindhoven, Netherlands}, author = {Vladimir Bazjanac} } @proceedings {300, title = {The Integration of Engineering and Architecture: a Perspective on Natural Ventilation for the new San Francisco Federal Building}, year = {2002}, month = {08/2002}, address = {Asilomar, California, USA}, abstract = {

A description of the in-progress design of a new Federal Office Building for San Francisco is used to illustrate a number of issues arising in the design of large, naturally ventilated office buildings. These issues include the need for an integrated approach to design involving the architects, mechanical and structural engineers, lighting designers and specialist simulation modelers. In particular, the use of natural ventilation, and the avoidance of air-conditioning, depends on the high degree of exposed thermal mass made possible by the structural scheme and by the minimization of solar heat gains while maintaining the good daylighting that results from optimization of the fa{\c c}ade. Another issue was the need for a radical change in interior space planning in order to enhance the natural ventilation; all the individual enclosed offices are located along the central spine of each floorplate rather than at the perimeter. The role of integration in deterring the undermining of the design through value engineering is discussed. The comfort criteria for the building were established based on the recent extension to the ASHRAE comfort standard based on the adaptive model for naturally ventilated buildings. The building energy simulation program EnergyPlus was used to compare the performance of different natural ventilation strategies. The results indicate that, in the San Francisco climate, wind-driven ventilation provides sufficient nocturnal cooling to maintain comfortable conditions and that external chimneys do not provide significant additional ventilation at times when it when it would be beneficial.

}, author = {Erin McConahey and Philip Haves and Tim Chirst} } @article {426, title = {IISABRE: An integrated building simulation environment}, journal = {Building and Environment}, volume = {32}, year = {1997}, month = {1997}, pages = {219-224}, type = {Research Article}, abstract = {

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.

}, keywords = {btp, building simulation, dest, energy performance, gui}, doi = {10.1016/S0360-1323(96)00057-1}, author = {Tianzhen Hong and Yi Jiang} } @article {2577, title = {Integrated building design system}, journal = {HV\&AC, in Chinese}, year = {1995}, month = {1995}, author = {Yi Jiang and Tianzhen Hong} } @article {334, title = {Investigation of the Reliability of Building Emulators for Testing Energy Management and Control Systems}, journal = {ASHRAE Transactions}, volume = {100}, year = {1994}, author = {Henk C. Peitsman and Shengwei Wang and Philip Haves and Satu H. K{\"a}rki and Cheol P. Park} } @proceedings {339, title = {Impacts of Climate Change}, year = {1992}, month = {05/1992}, address = {Solihull, England}, author = {Mike Hulme and Philip Haves and Boardman, B.} } @proceedings {345, title = {The Influence of Tuning on the Performance of a Building Control System}, year = {1990}, month = {12/1990}, address = {Li{\`e}ge, Belgium}, author = {Arthur L. Dexter and Philip Haves} } @conference {55942, title = {Implications of Office Building Thermal Mass and Multi-day Temperature Profiles for Cooling Strategies}, booktitle = {ASME/AIChe National Heat Transfer Conference}, year = {1985}, month = {08/1985}, address = {Denver, CO}, abstract = {

This paper describes a study of the cooling energy requirements that result from thermal storage in building mass, and suggests methods for predicting and controlling its energy cost implications. The study relies on computer simulations of energy use for a large office building prototype in El Paso, TX using the DOE-2 building energy analysis program. Increased Monday cooling energy requirements resulting from the weekend shut-down of HVAC systems are documented. Predictors of energy use and peak demands, which account for thermal storage in building mass, are described. Load-shifting, sub-cooling and pre-cooling equipment operating strategies are evaluated with explicit reference to utility rate schedules.

}, keywords = {commercial buildings, cooling energy, energy conservation, peak demand, thermal mass}, author = {Joseph H. Eto and Gay Powell} } @proceedings {355, title = {The Integration of Graphic and Thermal Simulation Models}, year = {1985}, month = {10/1985}, publisher = {Wembley, Online Publications Ltd}, address = {Pinner, Middlesex, UK}, author = {Cedric Green and Philip Haves and Paul Huddy} }