@article {31670, title = {Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm}, journal = {Applied Energy}, volume = {248}, year = {2019}, month = {04/2019}, pages = {217 - 230}, abstract = {

With the development of data-driven techniques, district-scale building energy prediction has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66\% and a root mean square error of 0.36 kWh/m2, compared to 12.05\% and 0.63 kWh/m2 of the conventional artificial neural network model and to 11.06\% and 0.89 kWh/m2 for the support vector regression model.

}, keywords = {Building Energy Modeling, Building network, Data-driven prediction, District-scale, Long short-term memory networks}, issn = {03062619}, doi = {10.1016/j.apenergy.2019.04.085}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0306261919307494}, author = {Wei Wang and Tianzhen Hong and Xiaodong Xu and Jiayu Chen and Ziang Liu and Ning Xu} } @article {31663, title = {A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems}, journal = {ACM Transactions on Sensor Networks}, volume = {14}, year = {2018}, month = {12/2018}, pages = {1 - 22}, abstract = {

Cyber-physical systems have enabled the collection of massive amounts of data in an unprecedented level of spatial and temporal granularity. Publishing these data can prosper big data research, which, in turn, helps improve overall system efficiency and resiliency. The main challenge in data publishing is to ensure the usefulness of published data while providing necessary privacy protection. In our previous work (Jia et al. 2017a), we presented a privacy-preserving data publishing framework (referred to as PAD hereinafter), which can guarantee k-anonymity while achieving better data utility than traditional anonymization techniques. PAD learns the information of interest to data users or features from their interactions with the data publishing system and then customizes data publishing processes to the intended use of data. However, our previous work is only applicable to the case where the desired features are linear in the original data record. In this article, we extend PAD to nonlinear features. Our experiments demonstrate that for various data-driven applications, PAD can achieve enhanced utility while remaining highly resilient to privacy threats.

}, keywords = {cyber physical systems, deep learning, k-anonymity, Privacy preservation, Smart buildings}, issn = {15504859}, doi = {10.1145/329407010.1145/3275520}, author = {Fisayo Caleb Sangogboye and Ruoxi Jia and Tianzhen Hong and Costas Spanos and Mikkel Baun Kj{\ae}rgaard} } @article {29895, title = {A Framework for Quantifying the Impact of Occupant Behavior on Energy Savings of Energy Conservation Measures}, year = {2017}, abstract = {

To improve energy efficiency{\textemdash}during new buildings design or during a building retrofit{\textemdash}evaluating the energy savings potential of energy conservation measures (ECMs) is a critical task. In building retrofits, occupant behavior significantly impacts building energy use and is a leading factor in uncertainty when determining the effectiveness of retrofit ECMs. Current simulation-based assessment methods simplify the representation of occupant behavior by using a standard or representative set of static and homogeneous assumptions ignoring the dynamics, stochastics, and diversity of occupant{\textquoteright}s energy-related behavior in buildings. The simplification contributes to significant gaps between the simulated and measured actual energy performance of buildings.

This study presents a framework for quantifying the impact of occupant behaviors on ECM energy savings using building performance simulation. During the first step of the study, three occupant behavior styles (austerity, normal, and wasteful) were defined to represent different levels of energy consciousness of occupants regarding their interactions with building energy systems (HVAC, windows, lights and plug-in equipment). Next, a simulation workflow was introduced to determine a range of the ECM energy savings. Then, guidance was provided to interpret the range of ECM savings to support ECM decision making. Finally, a pilot study was performed in a real building to demonstrate the application of the framework. Simulation results show that the impact of occupant behaviors on ECM savings vary with the type of ECM. Occupant behavior minimally affects energy savings for ECMs that are technology-driven (the relative savings differ by less than 2\%) and have little interaction with the occupants; for ECMs with strong occupant interaction, such as the use of zonal control variable refrigerant flow system and natural ventilation, energy savings are significantly affected by occupant behavior (the relative savings differ by up to 20\%). The study framework provides a novel, holistic approach to assessing the uncertainty of ECM energy savings related to occupant behavior, enabling stakeholders to understand and assess the risk of adopting energy efficiency technologies for new and existing buildings.

}, author = {Kaiyu Sun and Tianzhen Hong} } @article {56236, title = {A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30-Year Actual Weather Data}, journal = {Applied Energy}, volume = {111}, year = {2013}, month = {11/2013}, pages = {333-350}, publisher = {Lawrence Berkeley National Laboratory}, abstract = {

Buildings consume more than one third of the world{\textquoteright}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{\textendash}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.

}, keywords = {Actual meteorological year, building simulation, energy use, Peak electricity demand, Typical meteorological year, weather data}, doi = {10.1016/j.apenergy.2013.05.019}, author = {Tianzhen Hong and Wen-Kuei Chang and Hung-Wen Lin} } @proceedings {57000, title = {Functional Mock-Up Unit Import in EnergyPlus For Co-Simulation}, journal = {13th Conference of International Building Performance Simulation}, year = {2013}, month = {08/2013}, address = {Chambery, France}, abstract = {

This paper describes how to use the recently implemented Functional Mock-up Unit (FMU) for co-simulation import interface in EnergyPlus to link EnergyPlus with simulation tools packaged as FMUs. The interface complies with the Functional Mock-up Interface (FMI) for co-simulation standard version 1.0, which is an open standard designed to enable links between different simulation tools that are packaged as FMUs. This article starts with an introduction of the FMI and FMU concepts. We then discuss the implementation of the FMU import interface in EnergyPlus. After that, we present two use cases. The first use case is to model a HVAC system in Modelica, export it as an FMU, and link it to a room model in EnergyPlus. The second use case is an extension of the first case where a shading controller is modeled in Modelica, exported as an FMU, and used in the EnergyPlus room model to control the shading device of one of its windows. In both cases, the FMUs are imported into EnergyPlus which models the building envelope and manages the data-exchange between the envelope and the systems in the FMUs during run-time.

}, author = {Thierry Stephane Nouidui and Michael Wetter and Wangda Zuo} } @article {2591, title = {A framework for simulation-based real-time whole building performance assessment}, journal = {Building and Environment}, volume = {54}, year = {2012}, month = {08/2012}, pages = {100-108}, chapter = {100}, abstract = {

Most commercial buildings do not perform as well in practice as intended by the design and their performances often deteriorate over time. Reasons include faulty construction, malfunctioning equipment, incorrectly configured control systems and inappropriate operating procedures. One approach to addressing this problems is to compare the predictions of an energy simulation model of the building to the measured performance and analyze significant differences to infer the presence and location of faults. This paper presents a framework that allows a comparison of building actual performance and expected performance in real time. The realization of the framework utilized the EnergyPlus, the Building Controls Virtual Test Bed (BCVTB) and the Energy Management and Control System (EMCS) was developed. An EnergyPlus model that represents expected performance of a building runs in real time and reports the predicted building performance at each time step. The BCVTB is used as the software platform to acquire relevant inputs from the EMCS through a BACnet interface and send them to the EnergyPlus and to a database for archiving. A proof-of-concept demonstration is also presented.

}, keywords = {building controls virtual test bed, building performance, energy modeling, energyplus, real-time building simulation}, issn = {0360-1323}, author = {Xiufeng Pang and Michael Wetter and Prajesh Bhattacharya and Philip Haves} } @article {212, title = {Fast and informative flow simulation in a building by using fast fluid dynamics model on graphics processing unit}, journal = {Building and Environment}, volume = {45}, year = {2010}, pages = {747-757}, author = {Wangda Zuo and Qingyan Chen} } @proceedings {216, title = {Fast simulation of smoke transport in buildings}, journal = {the 41st International HVAC\&R congress}, year = {2010}, address = {Beograd, Serbian}, author = {Wangda Zuo and Qingyan Chen} } @proceedings {220, title = {Fast parallelized flow simulations on graphic processing units}, journal = {the 11th International Conference on Air Distribution in Rooms (RoomVent 2009)}, year = {2009}, address = {Busan, Korea}, author = {Wangda Zuo and Qingyan Chen} } @proceedings {246, title = {Fault Diagnostics and Supervised Testing: How Fault Diagnostic tools can be Proactive?}, journal = {Eleventh International Conference on Intelligent Systems and Controls}, year = {2008}, month = {11/2008}, abstract = {The topic of fault detection and diagnostics (FDD) is studied from the perspective of proactive testing. Unlike most research focus in the diagnosis area in which system outputs are analyzed for diagnosis purposes, in this paper the focus is on the other side of the problem: manipulating system inputs for better diagnosis reasoning. In other words, the question of how diagnostic mechanisms can direct system inputs for better diagnosis analysis is addressed here. It is shown how the problem can be formulated as decision making problem coupled with a Bayesian Network based diagnostic mechanism. The developed mechanism is applied to the problem of supervised testing in HVAC systems.}, url = {http://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=503}, author = {Massieh Najafi and David M. Auslander and Peter L. Bartlett and Philip Haves} } @article {238, title = {Facade design optimization for naturally ventilated residential buildings in Singapore}, journal = {Energy and Buildings}, volume = {39}, year = {2007}, month = {08/2007}, pages = {954-961}, author = {Liping Wang and Nyuk Hien Wong and Shuo Li} } @proceedings {2826, title = {Flow in an Underfloor Plenum}, journal = {SimBuild 2004, Building Sustainability and Performance Through Simulation}, year = {2004}, month = {08/2004}, address = {Boulder, Colorado, USA}, author = {Paul F. Linden} } @article {2590, title = {Framework for Coupling Room Air Models to Heat Balance Model Load and Energy Calculations (RP-1222)}, journal = {HVAC\&R Research (ASHRAE)}, volume = {10}, year = {2004}, month = {04/2004}, author = {Brent T. Griffith and Qingyan Chen} } @article {288, title = {Field Testing Model-Based Condition Monitoring on a HVAC Cooling Coil Sub-System}, journal = {Building Services Engineering Research \& Technology}, volume = {24}, year = {2003}, pages = {103-116}, author = {Richard A. Buswell and Philip Haves and Jonathan A. Wright} } @conference {2955, title = {Field Testing of Component-Level Model-Based Fault Detection Methods for Mixing Boxes and VAV Fan Systems}, booktitle = {2002 American Council for an Energy-Efficient Economy Summer Study on Energy Efficiency in Buildings}, year = {2002}, month = {05/2002}, address = {Pacific Grove, California}, abstract = {

An automated fault detection and diagnosis tool for HVAC systems is being developed, based on an integrated, life-cycle, approach to commissioning and performance monitoring. The tool uses component-level HVAC equipment models implemented in the SPARK equation-based simulation environment. The models are configured using design information and component manufacturers{\textquoteright} data and then fine-tuned to match the actual performance of the equipment by using data measured during functional tests of the sort using in commissioning. This paper presents the results of field tests of mixing box and VAV fan system models in an experimental facility and a commercial office building. The models were found to be capable of representing the performance of correctly operating mixing box and VAV fan systems and detecting several types of incorrect operation.

}, author = {Peng Xu and Philip Haves} } @proceedings {299, title = {Field Testing of Component-Level Model-Based Fault Detection Methods for Mixing Boxes and VAV Fan Systems}, journal = {2002 ACEEE Summer Study on Energy Efficiency in Buildings}, year = {2002}, month = {08/2002}, address = {Asilomar, California, USA}, abstract = {

An automated fault detection and diagnosis tool for HVAC systems is being developed, based on an integrated, lifecycle, approach to commissioning and performance monitoring. The tool uses component-level HVAC equipment models implemented in the SPARK equation-based simulation environment. The models are configured using design information and component manufacturers{\textquoteright} data and then fine-tuned to match the actual performance of the equipment by using data measured during functional tests of the sort using in commissioning. This paper presents the results of field tests of mixing box and VAV fan system models in an experimental facility and a commercial office building. The models were found to be capable of representing the performance of correctly operating mixing box and VAV fan systems and detecting several types of incorrect operation.

}, author = {Peng Xu and Philip Haves} } @proceedings {322, title = {Fault Modelling in Component-based HVAC Simulation}, journal = {Building Simulation {\textquoteright}97}, year = {1997}, month = {09/1997}, address = {Prague, Czech Republic}, abstract = {Models of faulty components or processes may either be used on-line as part of a fault detec- tion and diagnosis (FDD) system or may be used in simulations to train or test FDD procedures. Some faults may be modelled by choosing suit- able values of the parameters of fault free models, whereas other faults require specific extensions to fault free models. An example of the modelling of various faults in a cooling coil subsystem is pre- sented and different methods of using simulation in testing and training are discussed.}, url = {http://www.ibpsa.org/proceedings/BS1997/BS97_P101.pdf}, author = {Philip Haves} } @proceedings {327, title = {A Fault Detection and Diagnosis Method Based on First Principles Models and Expert Rules}, journal = {Tsinghua HVAC-95}, year = {1995}, month = {09/1995}, address = {Bejing, China}, author = {Tim I. Salsbury and Philip Haves and Jonathan A. Wright} } @proceedings {333, title = {Fault Detection in Air-Conditioning Systems Using A.I. Techniques}, journal = {BEP{\textquoteright}94}, year = {1994}, address = {York, England}, author = {Arthur L. Dexter and Richard S. Fargus and Philip Haves} }