Linking Design and Operation Phase Energy Performance Analysis Through Regression-Based Approaches

The reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long-term assets. In a life cycle perspective, both operational and embodied energy and carbon emissions have to be considered for appropriate comparability and decision-making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed, and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase’s energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels.

[1]  U. Berardi.  A cross-country comparison of the building energy consumptions and their trends , 2017 .

[2]  Massimiliano Manfren,et al.  Probabilistic behavioral modeling in building performance simulation: A Monte Carlo approach , 2017 .

[3]  Massimiliano Manfren,et al.  Multi-commodity network flow models for dynamic energy management – Mathematical formulation , 2012 .

[4]  Afshin Afshari,et al.  Inverse modeling of the urban energy system using hourly electricity demand and weather measurements, Part 1: Black-box model , 2017 .

[5]  Massimiliano Manfren,et al.  Multi-commodity network flow models for dynamic energy management – Smart Grid applications , 2012 .

[6]  Martin Fischer,et al.  Parametric analysis of design stage building energy performance simulation models , 2018, Energy and Buildings.

[7]  Pablo Aparicio-Ruiz,et al.  Applying Renewable Energy Technologies in an Integrated Optimization Method for Residential Building’s Design , 2019, Applied Sciences.

[8]  Lamberto Tronchin,et al.  Energy efficiency, demand side management and energy storage technologies – A critical analysis of possible paths of integration in the built environment , 2018, Renewable and Sustainable Energy Reviews.

[9]  Derek Clements-Croome,et al.  What is an intelligent building? Analysis of recent interpretations from an international perspective , 2016 .

[10]  Monjur Mourshed,et al.  Degree-day based non-domestic building energy analytics and modelling should use building and type specific base temperatures , 2017 .

[11]  Kristina Orehounig,et al.  Integration of decentralized energy systems in neighbourhoods using the energy hub approach , 2015 .

[12]  I. Hamilton,et al.  Using epidemiological methods in energy and buildings research to achieve carbon emission targets , 2017 .

[13]  Do Domestic Heating Controls Save Energy? A Review of the Evidence , 2018 .

[14]  V. I. Hanby,et al.  UK office buildings archetypal model as methodological approach in development of regression models for predicting building energy consumption from heating and cooling demands , 2013 .

[15]  Joseph Virgone,et al.  Development and validation of regression models to predict monthly heating demand for residential buildings , 2008 .

[16]  Christoph F. Reinhart,et al.  Shoeboxer: An algorithm for abstracted rapid multi-zone urban building energy model generation and simulation , 2017 .

[17]  Mitchell T. Paulus.  Algorithm for explicit solution to the three parameter linear change-point regression model , 2017 .

[18]  David E. Claridge,et al.  Algorithm for automating the selection of a temperature dependent change point model , 2015 .

[19]  Clayton Miller,et al.  The Building Data Genome Project: An open, public data set from non-residential building electrical meters , 2017 .

[20]  David E. Claridge,et al.  A temperature-based approach to detect abnormal building energy consumption , 2015 .

[21]  Zhiqiang John Zhai,et al.  Review on stochastic modeling methods for building stock energy prediction , 2017 .

[22]  Christian Thuesen,et al.  Organising Sustainable Transition: Understanding the Product, Project and Service Domain of the Built Environment , 2016 .

[23]  William J. Tolone,et al.  Energy modeling and data structure framework for Sustainable Human-Building Ecosystems (SHBE) — a review , 2018 .

[24]  Arno Schlueter,et al.  Automated daily pattern filtering of measured building performance data , 2015 .

[25]  Robert J. Brecha,et al.  Targeting Residential Energy Reduction for City Utilities Using Historical Electrical Utility Data and Readily Available Building Data , 2011 .

[26]  Francesco Pomponi,et al.  Measuring embodied carbon dioxide equivalent of buildings: A review and critique of current industry practice , 2017 .

[27]  A. Gasparella,et al.  Development of sets of simplified building models for building simulation , 2014 .

[28]  Arno Schlueter,et al.  Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data , 2020 .

[29]  Alan H. Tkaczyk,et al.  Case Study of Multiple Regression as Evaluation Tool for the Study of Relationships between Energy Demand, Air Tightness, and Associated Factors , 2017 .

[30]  Ray Galvin,et al.  Introducing the prebound effect: the gap between performance and actual energy consumption , 2012 .

[31]  Ian Walker,et al.  The building performance gap: Are modellers literate? , 2017 .

[32]  Mohammad Mottahedi,et al.  On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design , 2014 .

[33]  Emile J.L. Chappin,et al.  Multi-model ecologies for shaping future energy systems: Design patterns and development paths , 2018 .

[34]  Karsten Voss,et al.  Load Matching and Grid Interaction of Net Zero Energy Buildings , 2010 .

[35]  Dino Bouchlaghem,et al.  Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap , 2012 .

[36]  Henrik Madsen,et al.  Characterizing the energy flexibility of buildings and districts , 2018, Applied Energy.

[37]  S. Firth,et al.  AN OPEN SCIENCE APPROACH FOR BUILDING PERFORMANCE STUDIES , 2018 .

[38]  Ram D. Sriram,et al.  Modeling the Internet of Things: A Foundational Approach , 2016, WoT.

[39]  H. Herring,et al.  Technological innovation, energy efficient design and the rebound effect , 2007 .

[40]  Rasmus Lund Jensen,et al.  The best way to perform building simulations? One-at-a-time optimization vs. Monte Carlo sampling , 2020 .

[41]  S. Ranji Ranjithan,et al.  An enhanced linear regression-based building energy model (LRBEM+) for early design , 2016 .

[42]  Paris A. Fokaides,et al.  Key Performance Indicators (KPIs) approach in buildings renovation for the sustainability of the built environment: A review , 2016 .

[43]  Benedetto Nastasi.  Hydrogen policy, market, and R&D projects , 2019, Solar Hydrogen Production.

[44]  Jian Zhang,et al.  Achieving the 30% Goal: Energy and Cost Savings Analysis of ASHRAE Standard 90.1-2010 , 2011 .

[45]  Philippe Rigo,et al.  A review on simulation-based optimization methods applied to building performance analysis , 2014 .

[46]  Patrick James,et al.  Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates , 2013 .

[47]  Ralph Evins,et al.  Surrogate modelling for sustainable building design – A review , 2019, Energy and Buildings.

[48]  Philipp Geyer,et al.  Linking BIM and Design of Experiments to balance architectural and technical design factors for energy performance , 2018 .

[49]  Filippo Busato,et al.  Energy and economic analysis of different heat pump systems for space heating , 2012 .

[50]  Gireesh Nair,et al.  Energy evaluation of residential buildings: Performance gap analysis incorporating uncertainties in the evaluation methods , 2018 .

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

[52]  Lamberto Tronchin,et al.  On the “cost-optimal levels” of energy performance requirements and its economic evaluation in Italy , 2014 .

[53]  Francesco Pomponi,et al.  Circular economy for the built environment: A research framework , 2017 .

[54]  David J. Spiegelhalter,et al.  A hierarchical Bayesian framework for calibrating micro-level models with macro-level data , 2013 .

[55]  Dan Brown,et al.  Estimating Industrial Building Energy Savings using Inverse Simulation , 2011 .

[56]  Massimiliano Manfren,et al.  Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation , 2013 .

[57]  Nadège Blond,et al.  A city scale degree-day method to assess building space heating energy demands in Strasbourg Eurometropolis (France) , 2016 .

[58]  José Manuel Cejudo López,et al.  Uncertainties and sensitivity analysis in building energy simulation using macroparameters , 2013 .

[59]  Andrea Gasparella,et al.  A stepwise approach integrating feature selection, regression techniques and cluster analysis to identify primary retrofit interventions on large stocks of buildings , 2019, Sustainable Cities and Society.

[60]  Rr Rajesh Kotireddy,et al.  A methodology for performance robustness assessment of low-energy buildings using scenario analysis , 2018 .

[61]  I. Hamilton,et al.  Energy epidemiology: a new approach to end-use energy demand research , 2013 .

[62]  Kristian Fabbri,et al.  Real Estate market, energy rating and cost. Reflections about an Italian case study , 2011 .

[63]  Christoph F. Reinhart,et al.  Autozoner: an algorithm for automatic thermal zoning of buildings with unknown interior space definitions , 2016 .

[64]  Massimiliano Manfren,et al.  Optimization concepts in district energy design and management – A case study , 2012 .

[65]  Christian Inard,et al.  Fast method to predict building heating demand based on the design of experiments , 2009 .

[66]  Jan Hensen,et al.  Estimating the influence of occupant behavior on building heating and cooling energy in one simulation run , 2018 .

[67]  Massimiliano Manfren,et al.  A Case Study of Solar Technologies Adoption: Criteria for BIPV Integration in Sensitive Built Environment , 2012 .

[68]  Juan-Carlos Baltazar,et al.  Analysis methods for characterizing energy saving opportunities from home automation devices using smart meter data , 2020 .

[69]  Franz-Josef Ulm,et al.  Data analytics for simplifying thermal efficiency planning in cities , 2016, Journal of The Royal Society Interface.

[70]  Bass Abushakra,et al.  An hourly hybrid multi-variate change-point inverse model using short-term monitored data for annual prediction of building energy performance, part III: Results and analysis (1404-RP) , 2016 .

[71]  D. E. Claridge,et al.  Inclusion of Building Envelope Thermal Lag Effects in Linear Regression Models of Daily Basis Building Energy Use Data , 2012 .

[72]  P. Wells,et al.  An agenda for sustainability transitions research: State of the art and future directions , 2019, Environmental Innovation and Societal Transitions.

[73]  Stephen P. Boyd,et al.  Dynamic Network Energy Management via Proximal Message Passing , 2013, Found. Trends Optim..

[74]  Patrick James,et al.  Climate change future proofing of buildings—Generation and assessment of building simulation weather files , 2008 .

[75]  Iain Staffell,et al.  Opening the black box of energy modelling: strategies and lessons learned , 2017, ArXiv.

[76]  V. Hoffmann,et al.  Analysis of complementarities: Framework and examples from the energy transition , 2016 .

[77]  Jian Zhang,et al.  Enhancements to ASHRAE Standard 90.1 Prototype Building Models , 2014 .

[78]  Yacine Rezgui,et al.  A simplified geo-cluster definition for energy system planning in Europe , 2019 .

[79]  Lamberto Tronchin,et al.  Optimization of building energy performance by means of multi-scale analysis – Lessons learned from case studies , 2016 .

[80]  Francesco Mancini,et al.  Energy Retrofitting Effects on the Energy Flexibility of Dwellings , 2019, Energies.

[81]  Massimiliano Manfren,et al.  Building Automation and Control Systems and performance optimization: A framework for analysis , 2017 .

[82]  Uwe Krien,et al.  The Open Energy Modelling Framework (oemof) - A new approach to facilitate open science in energy system modelling , 2018, Energy Strategy Reviews.

[83]  Bing Liu,et al.  U.S. Department of Energy Commercial Reference Building Models of the National Building Stock , 2011 .

[84]  Johannes Dorfner,et al.  Open Source Modelling and Optimisation of Energy Infrastructure at Urban Scale , 2016 .

[85]  Patrick Burns,et al.  Chapter 22: Compressed Air Evaluation Protocol. The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures, September 2011 - August 2020 , 2017 .

[86]  Philip Price,et al.  Methods for Analyzing Electric Load Shape and its Variability , 2010 .

[87]  Bass Abushakra,et al.  An hourly hybrid multi-variate change-point inverse model using short-term monitored data for annual prediction of building energy performance, part II: Methodology (1404-RP) , 2016 .

[88]  Massimiliano Manfren,et al.  Thermal inertia and energy efficiency – Parametric simulation assessment on a calibrated case study , 2015 .

[89]  Burcin Becerik-Gerber,et al.  A model calibration framework for simultaneous multi-level building energy simulation , 2015 .

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

[91]  Henrik Madsen,et al.  Implementing flexibility into energy planning models: Soft-linking of a high-level energy planning model and a short-term operational model , 2020 .

[92]  Paul Beagon,et al.  Control Strategies for Building Energy Systems to Unlock Demand Side Flexibility – A Review , 2017, Building Simulation Conference Proceedings.

[93]  David E. Claridge,et al.  Statistical modeling of the building energy balance variable for screening of metered energy use in large commercial buildings , 2014 .

[94]  Patrick Schalbart,et al.  Energy Performance Contracting Methodology Based upon Simulation and Measurement , 2017 .

[95]  Lamberto Tronchin,et al.  Energy analytics for supporting built environment decarbonisation , 2019 .

[96]  Oleksii Pasichnyi,et al.  Data-driven building archetypes for urban building energy modelling , 2019, Energy.

[97]  Laurent Mora,et al.  Monitoring System Analysis for Evaluating a Building’s Envelope Energy Performance through Estimation of Its Heat Loss Coefficient , 2018, Sensors.

[98]  David E. Claridge,et al.  Estimation of Building Parameters Using Simplified Energy Balance Model and Metered Whole Building Energy Use , 2012 .

[99]  J. Cipriano,et al.  Developing indicators to improve energy action plans in municipalities: An accounting framework based on the fund-flow model , 2017 .

[100]  Elena Baralis,et al.  Energy Signature Analysis: Knowledge at Your Fingertips , 2015, 2015 IEEE International Congress on Big Data.

[101]  Massimiliano Manfren,et al.  Local energy efficiency programs: A monitoring methodology for heating systems , 2014 .

[102]  an Rosenowa,et al.  Evaluating the evaluations: Evidence from energy efficiency programmes in Germany and the UK , 2013 .

[103]  Johanna L. Mathieu,et al.  Quantifying Changes in Building Electricity Use, With Application to Demand Response , 2011, IEEE Transactions on Smart Grid.

[104]  P Pieter-Jan Hoes,et al.  Occupant behavior in building energy simulation: towards a fit-for-purpose modeling strategy , 2016 .

[105]  MEASURING PROGRESS , 2007 .

[106]  Saurabh Jalori,et al.  A unified inverse modeling framework for whole-building energy interval data: Daily and hourly baseline modeling and short-term load forecasting , 2015 .

[107]  Francesco Pomponi,et al.  Scrutinising embodied carbon in buildings: The next performance gap made manifest , 2018 .

[108]  Benedetto Nastasi,et al.  On the link between energy performance of building and thermal comfort: An example , 2019, XIAMEN-CUSTIPEN WORKSHOP ON THE EQUATION OF STATE OF DENSE NEUTRON-RICH MATTER IN THE ERA OF GRAVITATIONAL WAVE ASTRONOMY.

[109]  Mohammad Yusri Hassan,et al.  Energy efficiency index as an indicator for measuring building energy performance: A review , 2015 .

[110]  Jan Carmeliet,et al.  Towards an energy sustainable community: An energy system analysis for a village in Switzerland , 2014 .

[111]  Yong Shi,et al.  A review of data-driven approaches for prediction and classification of building energy consumption , 2018 .

[112]  L. Tronchin,et al.  Analysis of buildings' energy consumption by means of exergy method , 2008 .

[113]  Anna Joanna Marszal,et al.  Annex 67 – Energy Flexible Buildings , 2018 .

[114]  Massimiliano Manfren,et al.  Parametric Performance Analysis and Energy Model Calibration Workflow Integration—A Scalable Approach for Buildings , 2020, Energies.

[115]  J. Casillas,et al.  Suitability analysis of modeling and assessment approaches in energy efficiency in buildings , 2018 .

[116]  Brian Ó Gallachóir,et al.  Soft-linking of a power systems model to an energy systems model , 2012 .

[117]  Guglielmina Mutani,et al.  A supporting method for selecting cost-optimal energy retrofit policies for residential buildings at the urban scale , 2016 .

[118]  Ardeshir Mahdavi,et al.  Reductive bottom-up urban energy computing supported by multivariate cluster analysis , 2017 .

[119]  Patrick James,et al.  Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building , 2018, Energy.

[120]  Iain Staffell,et al.  The importance of open data and software: Is energy research lagging behind? , 2017 .

[121]  Yang Li,et al.  Change-point multivariable quantile regression to explore effect of weather variables on building energy consumption and estimate base temperature range , 2020 .

[122]  J. Terés-Zubiaga,et al.  In-use office building energy characterization through basic monitoring and modelling , 2016 .

[123]  Michel Noussan,et al.  Data Analysis of Heating Systems for Buildings—A Tool for Energy Planning, Policies and Systems Simulation , 2018 .

[124]  Kelly Kissock,et al.  Measuring Progress with Normalized Energy Intensity , 2011 .

[125]  Amaia Uriarte,et al.  Mathematical development of an average method for estimating the reduction of the Heat Loss Coefficient of an energetically retrofitted occupied office building , 2019, Energy and Buildings.

[126]  J. Cipriano,et al.  Approaches to evaluate building energy performance from daily consumption data considering dynamic and solar gain effects , 2013 .

[127]  Filippo Busato,et al.  Two years of recorded data for a multisource heat pump system: A performance analysis , 2013 .

[128]  Dennis L. Loveday,et al.  First evidence for the reliability of building co-heating tests , 2018 .

[129]  Hiroshi Yoshino,et al.  IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation methods , 2017 .

[130]  Ralph Evins,et al.  A review of computational optimisation methods applied to sustainable building design , 2013 .

[131]  Elena Garbarino,et al.  Identifying macro-objectives for the life cycle environmental performance and resource efficiency of EU buildings , 2015 .

[132]  Kelly Kissock,et al.  Understanding Industrial Energy Use through Lean Energy Analysis , 2011 .

[133]  Ian Paul Knight,et al.  Daily energy consumption signatures and control charts for air-conditioned buildings , 2016 .

[134]  Pieter de Wilde,et al.  Building Performance Analysis , 2018 .

[135]  Enrico Fabrizio,et al.  Reference buildings for cost optimal analysis: Method of definition and application , 2013 .

[136]  Enrico Fabrizio,et al.  Methodologies and Advancements in the Calibration of Building Energy Models , 2015 .

[137]  François Maréchal,et al.  Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built Environment , 2018, Front. Energy Res..

[138]  Alberto Bemporad,et al.  Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities , 2018 .

[139]  Massimiliano Manfren,et al.  From in-situ measurement to regression and time series models : An overview of trends and prospects for building performance modelling , 2019 .

[140]  Rasmus Lund Jensen,et al.  A comparison of six metamodeling techniques applied to building performance simulations , 2018 .

[141]  Ilaria Ballarini,et al.  A New Methodology for Assessing the Energy Consumption of Building Stocks , 2017 .

[142]  Massimiliano Manfren,et al.  Cost optimal analysis of heat pump technology adoption in residential reference buildings , 2013 .

[143]  Sang Hoon Lee,et al.  Reconstructing building stock to replicate energy consumption data , 2016 .

[144]  Michael D. Sohn,et al.  A regression-based approach to estimating retrofit savings using the Building Performance Database , 2016 .

[145]  Lamberto Tronchin,et al.  Indoor Environmental Quality in Low Energy Buildings , 2015 .

[146]  Enrico Fabrizio,et al.  Cost-Optimal Analysis for Nearly Zero Energy Buildings Design and Optimization: A Critical Review , 2018, Energies.

[147]  Massimiliano Manfren,et al.  Probabilistic behavioural modeling in building performance simulation—The Brescia eLUX lab , 2016 .

[148]  A. Poulin,et al.  Daily load profiles clustering : a powerful tool for demand side management in medium-sized industries , 2017 .

[149]  G. C. Graça,et al.  Comparison of methodologies for generation of future weather data for building thermal energy simulation , 2020 .

[150]  Philippe Goffin,et al.  Low exergy building systems implementation , 2012 .

[151]  Jason Brown,et al.  Assessment of linear emulators in lightweight Bayesian calibration of dynamic building energy models for parameter estimation and performance prediction , 2016 .

[152]  Ram Rajagopal,et al.  Data-Driven Benchmarking of Building Energy Efficiency Utilizing Statistical Frontier Models , 2014, J. Comput. Civ. Eng..

[153]  S. Ranji Ranjithan,et al.  Multivariate regression as an energy assessment tool in early building design , 2012 .

[154]  Karine Pollier,et al.  Support for setting up an observatory of the building stock and related policies , 2016 .

[155]  Robert J. Brecha,et al.  Establishing Building Recommissioning Priorities and Potential Energy Savings from Utility Energy Data , 2011 .

[156]  Tomislav Dragicevic,et al.  Future effectual role of energy delivery: A comprehensive review of Internet of Things and smart grid , 2018, Renewable and Sustainable Energy Reviews.

[157]  Saurabh Jalori,et al.  A new clustering method to identify outliers and diurnal schedules from building energy interval data , 2015 .