Model selection for continuous commissioning of HVAC-systems in office buildings: a review Renewable & Sustainable Energy Reviews

Abstract This paper presents an overview of literature and procedures about real-life, state-of-the-art implementations of model-based (MB) Continuous Commissioning (CCx) in office buildings. The focus is on the building- and HVAC-models used for each of three distinct CCx-domains: The identification of energy conserving opportunities (ECOs), fault detection, diagnosis, evaluation and overhaul (FDDe) and model-based control (MBC). For each domain, the relations between chosen model structure, model order, parameter estimation procedure, available sensor data quality and calculation power are highlighted. These insights are critical for office building managers, BEMS manufacturers and researchers involved or interested in the selection and implementation of MBCC strategies. The analyses indicate that the chosen model order and parameter estimation technique depend highly on available calculation power and data availability. Full model-sharing between different subtopics is rarely performed, presumably due to the diversity of model requirements for each CCx-domain. Several model structures and parameter estimation procedures -e.g. multi-step-ahead and subspace identification- are recurring frequently within one domain -e.g. MBC-. Also, both within and between CCx-domains, the exchange of available expert knowledge and measurements for parameter estimation improves the accuracy of the resulting models.

[1]  Samuel Prívara,et al.  Building modeling: Selection of the most appropriate model for predictive control , 2012 .

[2]  Arthur L. Dexter,et al.  The potential for energy saving in heating systems through improving boiler controls , 2004 .

[3]  Ruxu Du,et al.  Model-based Fault Detection and Diagnosis of HVAC systems using Support Vector Machine method , 2007 .

[4]  P. Torcellini,et al.  DOE Commercial Building Benchmark Models , 2008 .

[5]  C. Ghiaus Experimental estimation of building energy performance by robust regression , 2006 .

[6]  Byung-Cheon Ahn,et al.  Transient pattern analysis for fault detection and diagnosis of HVAC systems , 2005 .

[7]  Janine Belfast Fault Diagnostics Tools for Commercial Buildings—Applications, Algorithms and Barriers , 2014 .

[8]  Kurt Roth,et al.  Advanced Controls for Commercial Buildings: Barriers and Energy Savings Potential , 2006 .

[9]  Stéphane Bertagnolio Evidence-Based Model Calibration for Efficient Building Energy Services , 2012 .

[10]  Jonathan A. Wright,et al.  Demonstration of Fault Detection and Diagnosis Methods for Air-Handling Units , 2002 .

[11]  Evan Mills Building commissioning: a golden opportunity for reducing energy costs and greenhouse gas emissions in the United States , 2011 .

[12]  Marcus M. Keane,et al.  A performance assessment ontology for the environmental and energy management of buildings , 2015 .

[13]  Philip Haves,et al.  State-of-the-Art Review for Commissioning Low Energy Buildings: Existing Cost/Benefit and Persistence Methodologies and Data, State of Development of Automated Tools and Assessment of Needs for Commissioning ZEB | NIST , 2007 .

[14]  Srinivas Katipamula,et al.  Methods for Automated and Continuous Commissioning of Building Systems , 2003 .

[15]  Rasmus Lund Jensen,et al.  Experimental Analysis and Model Validation of an Opaque Ventilated Facade , 2012 .

[16]  Mingsheng Liu,et al.  Continuous Commissioning Leading Energy Project Process - An Industry Approach , 2005 .

[17]  Paul Raftery,et al.  Review of automated fault detection and diagnostic tools in air handling units , 2014 .

[18]  Thordur Runolfsson,et al.  Uncertainty propagation in dynamical systems , 2008, Autom..

[19]  Fu Xiao,et al.  A fault detection and diagnosis strategy with enhanced sensitivity for centrifugal chillers , 2011 .

[20]  Gilles Lefebvre,et al.  Using model size reduction techniques for thermal control applications in buildings , 2000 .

[21]  Mazen Alamir,et al.  USE OF SIMULATION FOR THE VALIDATION OF A MODEL PREDICTIVE CONTROL STRATEGY FOR ENERGY MANAGEMENT IN BUILDINGS , 2011 .

[22]  M. Effinger,et al.  Case Studies in Using Whole Building Interval Data to Determine Annualized Electrical Savings , 2009 .

[23]  Yeonsook Heo,et al.  Calibration of building energy models for retrofit analysis under uncertainty , 2012 .

[24]  Mingsheng Liu,et al.  Improving Building Control and System Operation Through the Continuous Commissioning® Process: A Case Study , 2003 .

[25]  James E. Braun,et al.  An overall performance index for characterizing the economic impact of faults in direct expansion cooling equipment , 2007 .

[26]  Francisco Rodríguez,et al.  A comparison of thermal comfort predictive control strategies , 2011 .

[27]  Lukas G. Swan,et al.  Opportunities for Implementation of MPC in Commercial Buildings , 2015 .

[28]  Johan Åkesson,et al.  Toolbox for development and validation of grey-box building models for forecasting and control , 2014 .

[29]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[30]  Zhenjun Ma,et al.  Supervisory and Optimal Control of Building HVAC Systems: A Review , 2008 .

[31]  Paul Raftery,et al.  A review of methods to match building energy simulation models to measured data , 2014 .

[32]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[33]  Mohammad Rasouli,et al.  Uncertainties in energy and economic performance of HVAC systems and energy recovery ventilators due to uncertainties in building and HVAC parameters , 2013 .

[34]  Geert Bauwens,et al.  Co-heating test: A state-of-the-art , 2014 .

[35]  Giorgio Pattarello,et al.  Model Predictive Control of HVAC Systems: Design and Implementation on a Real Case Study , 2013 .

[36]  Nelson Fumo,et al.  Methodology to estimate building energy consumption using EnergyPlus Benchmark Models , 2010 .

[37]  Shengwei Wang,et al.  A fault detection and diagnosis strategy of VAV air-conditioning systems for improved energy and control performances , 2005 .

[38]  Henrik Madsen,et al.  IEA EBC Annex 58 - Reliable building energy performance characterisation based on full scale dynamic measurements. Report of subtask 3, part 2: Thermal performance characterisation using time series data - statistical guidelines , 2016 .

[39]  Josh Wall,et al.  Trial results from a model predictive control and optimisation system for commercial building HVAC , 2014 .

[40]  Henrik Madsen,et al.  Identifying suitable models for the heat dynamics of buildings , 2011 .

[41]  William D'haeseleer,et al.  Control of heating systems in residential buildings: Current practice , 2008 .

[42]  Sih-Li Chen,et al.  Balancing adjustment of exhaust duct system using feedback simulation method , 2006 .

[43]  Mark S. Martinez,et al.  International performance measurement & verification protocol: Concepts and options for determining energy and water savings , 2001 .

[44]  Farrokh Janabi-Sharifi,et al.  Theory and applications of HVAC control systems – A review of model predictive control (MPC) , 2014 .

[45]  Enrico Fabrizio,et al.  Italian benchmark building models: the office building , 2011 .

[46]  David Hsu,et al.  Improving energy benchmarking with self-reported data , 2014 .

[47]  Rolf Isermann Model-based fault-detection and diagnosis - status and applications § , 2004 .

[48]  James E. Braun,et al.  Evaluating the Performance of Building Thermal Mass Control Strategies , 2001 .

[49]  M. P. Modera ELECTRIC CO-HEATING: A METHOD FOR EVALUATING SEASONAL HEATING EFFICIENCIES AND HEAT LOSS RATES IN DWELLINGS , 2012 .

[50]  Krishna R. Pattipati,et al.  Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers , 2007, IEEE Transactions on Automation Science and Engineering.

[51]  O. T. Masoso,et al.  The dark side of occupants’ behaviour on building energy use , 2010 .

[52]  David E. Claridge,et al.  Using synthetic data to evaluate multiple regression and principal component analyses for statistical modeling of daily building energy consumption , 1994 .

[53]  Fu Xiao,et al.  Quantitative energy performance assessment methods for existing buildings , 2012 .

[54]  Manfred Morari,et al.  Model Predictive Control of a Swiss Office Building , 2013 .

[55]  Yuebin Yu,et al.  A review of fault detection and diagnosis methodologies on air-handling units , 2014 .

[56]  Xiufeng Pang,et al.  Automated Continuous Commissioning of Commercial Buildings , 2013 .

[57]  David Fisk,et al.  Comparative review of building commissioning regulation: a quality perspective , 2016 .

[58]  Daniel A. Veronica Automatically detecting faulty regulation in HVAC controls , 2013, Automated Diagnostics and Analytics for Buildings.

[59]  T. Agami Reddy,et al.  Calibrating Detailed Building Energy Simulation Programs with Measured Data—Part I: General Methodology (RP-1051) , 2007 .

[60]  Lieve Helsen,et al.  Quantification of flexibility in buildings by cost curves – Methodology and application , 2016 .

[61]  J. D. Balcomb,et al.  Side-by-Side Thermal Tests of Modular Offices: A Validation Study of the STEM Method , 2001 .

[62]  Philip Haves,et al.  A nodal model for displacement ventilation and chilled ceiling systems in office spaces , 2001 .

[63]  Jeff Haberl,et al.  Review of Methods for Measuring and Verifying Savings from Energy Conservation Retrofits to Existing Buildings , 2003 .

[64]  Christopher Upchurch,et al.  Project Summary Report , 2011 .

[65]  Patxi Hernandez,et al.  Energy demands and potential savings in European office buildings: Case studies based on EnergyPlus simulations , 2013 .

[66]  Clara Verhelst,et al.  Building models for model predictive control of office buildings with concrete core activation , 2013 .

[67]  Vincent Lemort,et al.  iSERVcmb final report – july 2014: the inspection of building services through continuous monitoring and benchmarking – the iSERVcmb project , 2014 .