Review of developments in whole-building statistical energy consumption models for commercial buildings

Abstract A significant portion of energy consumption occurs in buildings today. Accurate and easy-to-implement methods are needed to calculate building energy consumption for a wide range of applications. These areas have attracted research interest as early as the 1980's. Among a number of approaches for building energy analysis, the statistical methods have remained popular because they are simple to use and able to provide accurate prediction of building energy consumption. As the availability and quality of building energy data continue to improve, the methodologies behind building energy calculation also evolved over time. Although relevant areas such as calibrated simulation and machine learning methods have had numerous recent literature reviews, the statistical methods have not been reviewed in depth. This work aims to fill this knowledge gap for whole-building energy consumption modelling. This work will discuss how the methodology developed through time and summarise the applications of this approach in various areas of building energy analysis. This work has identified that the statistical methods have rarely been applied to model the electric demand, power factor, or domestic water use. The use of an occupancy variable and novel model forms are also areas with limited literature.

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

[2]  David E. Claridge,et al.  A Four-Parameter Change-Point Model for Predicting Energy Consumption in Commercial Buildings , 1992 .

[4]  A. Wright,et al.  Longitudinal analysis of energy metering data from non-domestic buildings , 2010 .

[5]  David E. Claridge,et al.  Development of a Toolkit for Calculating Linear, Change–Point Linear and Multiple–Linear Inverse Building Energy Analysis Models, ASHRAE Research Project 1050-RP, Detailed Test Results , 2001 .

[6]  Jessica Granderson,et al.  Application of automated measurement and verification to utility energy efficiency program data , 2017 .

[7]  Arun Kumar,et al.  A review on modeling and simulation of building energy systems , 2016 .

[8]  Jessica Granderson,et al.  Development and Application of a Statistical Methodology to Evaluate the Predictive Accuracy of Building Energy Baseline Models , 2014 .

[9]  Stig Hammarsten,et al.  A critical appraisal of energy-signature models , 1987 .

[10]  David E. Claridge,et al.  Effect of time resolution on statistical modeling of cooling energy use in large commercial buildings , 1995 .

[11]  Bass Abushakra,et al.  Predicting annual energy use in buildings using short-term monitoring and utility bills: The hybrid inverse model using daily data (HIM-D) , 2013 .

[12]  Daniel O. Stram,et al.  The applicability of PRISM to electric heating and cooling , 1986 .

[13]  Jeff Haberl,et al.  Evaluation of the energy performance of a net zero energy building in a hot and humid climate , 2019 .

[14]  David E. Claridge,et al.  Inverse Model Toolkit: Application and Testing , 2003 .

[15]  David E. Claridge,et al.  ASHRAE's Guideline 14-2002 for Measurement of Energy and Demand Savings: How to Determine What Was Really Saved by the Retrofit , 2005 .

[16]  Sabaratnam Thamilseran.  An Inverse BIN Methodology to Measure the Savings from Energy Conservation Retrofits in Commercial Buildings , 1999 .

[17]  Michael Baldea,et al.  Development and analysis of residential change-point models from smart meter data , 2017 .

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

[19]  J. S. Haberl,et al.  Development of Pre-Screening Methods for Analyzing Energy Use in K-12 Public Schools , 1998 .

[20]  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 .

[21]  Weiming Shen,et al.  Detection and interpretation of anomalies in building energy use through inverse modeling , 2019, Science and Technology for the Built Environment.

[22]  Joseph H. Eto,et al.  On using degree-days to account for the effects of weather on annual energy use in office buildings , 1988 .

[23]  T. Agami Reddy,et al.  Literature review on calibration of building energy simulation programs : Uses, problems, procedures, uncertainty, and tools , 2006 .

[24]  David E. Claridge,et al.  Bias in Predicting Annual Energy Use in Commercial Buildings with Regression Models Developed from Short Data Sets , 1994 .

[25]  J. Haberl,et al.  Development and application of weather-normalized monthly building water use model , 2014 .

[26]  Nora El-Gohary,et al.  A review of data-driven building energy consumption prediction studies , 2018 .

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

[28]  Michael D. Sohn,et al.  Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings , 2016 .

[29]  Xiaoli Li,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 1 Classification of Energy Consumption in Buildings with Outlier Detection , 2022 .

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

[31]  David Shea Landman.  Development of Pre-Screening Indices to Improve Energy Analysis of Public K-12 Schools , 1998 .

[32]  A. Mahdavi,et al.  IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings , 2017 .

[33]  P. D. Reiter.  Early results from commercial ELCAP buildings: schedules as a primary determinant of load shapes in the commercial sector , 1986 .

[34]  J. Kelly Kissock,et al.  Measuring industrial energy savings , 2008 .

[35]  John A Shonder,et al.  Estimating energy, demand, and cost savings from a geothermal heat pump ESPC project through utility bill analysis , 2006 .

[36]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[37]  Margaret F. Fels.  PRISM: An Introduction , 1986 .

[38]  David E. Claridge,et al.  Multivariate Regression Modeling , 1998 .

[39]  David E. Claridge,et al.  Uncertainty of “Measured” Energy Savings from Statistical Baseline Models , 2000 .

[40]  D. Claridge,et al.  Break-point statistical model for building heating hot water consumption with constant volume reheat systems , 2020 .

[41]  Olena Kalyanova Larsen,et al.  Household electricity demand profiles – A high-resolution load model to facilitate modelling of energy flexible buildings , 2016 .

[42]  P. Lerman.  Fitting Segmented Regression Models by Grid Search , 1980 .

[43]  M. Shin,et al.  Prediction of cooling energy use in buildings using an enthalpy-based cooling degree days method in a hot and humid climate , 2016 .

[44]  Jeff Haberl,et al.  A Bin Method for Calculating Energy Conservation Retrofit Savings in Commercial Buildings , 1994 .

[45]  John E. Seem,et al.  Using intelligent data analysis to detect abnormal energy consumption in buildings , 2007 .

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

[47]  Margaret F. Fels,et al.  The stability of PRISM estimates , 1986 .

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

[49]  David E. Claridge,et al.  Development of Data Quality Control Limits for Data Screening through the "Energy Balance" Method , 2008 .

[50]  Miriam L. Goldberg,et al.  Using the scorekeeping approach to monitor aggregate energy conservation , 1986 .

[51]  Miguel Molina-Solana,et al.  Data science for building energy management: A review , 2017 .

[52]  Piljae Im,et al.  Bayesian Analysis of Savings from Retrofit Projects , 2012 .

[53]  David E. Claridge,et al.  Program Overview: The Texas LoanSTAR Program; 1989-October 1999, A 10-Year Experience , 2000 .

[54]  David E. Claridge,et al.  Modeling Hourly Energy Use in Commercial Buildings With Fourier Series Functional Forms , 1998 .

[55]  David E. Claridge,et al.  Development of an Inverse Method to Estimate Overall Building and Ventilation Parameters of Large Commercial Buildings , 1999 .

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

[57]  V. Geros,et al.  Modeling and predicting building's energy use with artificial neural networks: Methods and results , 2006 .

[58]  John E. Seem,et al.  Pattern recognition algorithm for determining days of the week with similar energy consumption profiles , 2005 .

[59]  David E. Claridge,et al.  An Energy Delivery Efficiency Index to Evaluate Simultaneous Heating and Cooling Effects in Large Commercial Buildings , 1994 .

[60]  J. McBride,et al.  The effects of varying indoor air temperature and heat gain on the measurement of retrofit savings , 1998 .

[61]  D. E. Claridge,et al.  USE OF FIRST LAW ENERGY BALANCE AS A SCREENING TOOL FOR BUILDING ENERGY USE DATA: EXPERIENCES ON THE INCLUSION OF OUTSIDE AIR ENTHALPY VARIABLE , 2009 .

[62]  Eric Hirst,et al.  Household Energy Conservation: A Review of the Federal Residential Conservation Service , 1984 .

[63]  David E. Claridge,et al.  Baselining methodology for facility-level monthly energy use - Part 2: application to eight army installations , 1997 .

[64]  John Seryak,et al.  LEAN ENERGY ANALYSIS: IDENTIFYING, DISCOVERING AND TRACKING ENERGY SAVINGS POTENTIAL , 2004 .

[65]  J. S. Haberl,et al.  Use of metered data analysis to improve building operation and maintenance: Early results from two federal complexes , 1988 .

[66]  Carlos Christensen.  Digital and color energy maps for graphic display of hourly data , 1984 .

[67]  Nelson Fumo,et al.  A review on the basics of building energy estimation , 2014 .

[68]  David E. Claridge,et al.  A Perspective on Methods for Analysis of Measured Energy Data from Commercial Buildings , 1998 .

[69]  Geert Van Ham,et al.  Model selection for continuous commissioning of HVAC-systems in office buildings: a review Renewable & Sustainable Energy Reviews , 2017 .

[70]  David E. Claridge,et al.  A Change-Point Principal Component Analysis (CP/PCA) Method for Predicting Energy Usage in Commercial Buildings: The PCA Model , 1993 .

[71]  Oar,et al.  International Performance Measurement and Verification Protocol , 2014 .

[72]  David E. Claridge,et al.  Baselining methodology for facility-level monthly energy use. Part 1: Theoretical aspects , 1997 .

[73]  Michael D. Sohn,et al.  Uncertainty estimation improves energy measurement and verification procedures , 2014 .

[74]  Juan-Carlos Baltazar,et al.  ANALYSIS OF COOLING REGRESSION MODELS FOR HOT AND HUMID CLIMATES BASED ON "OPERATIONAL EFFECTIVE ENTHALPY" , 2013 .

[75]  W. O’Brien,et al.  An international review of occupant-related aspects of building energy codes and standards , 2020 .

[76]  J. F. Kreider.  Prediction Hourly Building Energy Use : The Great Energy Predictor Shootout - Overview and Discussion of Results , 1994 .

[77]  John Kissock,et al.  Modeling commercial building energy use with artificial neural networks , 1994 .

[78]  Xiaofeng Guo,et al.  Modeling and forecasting building energy consumption: A review of data-driven techniques , 2019, Sustainable Cities and Society.

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

[80]  T. Tereshchenko,et al.  Analysis of energy signatures and planning of heating and domestic hot water energy use in buildings in Norway , 2019, E3S Web of Conferences.

[81]  David E. Claridge,et al.  Ambient-temperature regression analysis for estimating retrofit savings in commercial buildings , 1998 .

[82]  David E. Claridge,et al.  Baseline calculations for measurement and verification of energy and demand savings in a revolving loan program in Texas , 1998 .

[83]  Jeff Haberl,et al.  Measurement and Verification of Energy Savings , 2020 .

[84]  David E. Claridge,et al.  Overview of Existing Literature On Diversity Factors and Schedules for Energy and Cooling Load Calculations , 2004 .

[85]  Jeff Haberl,et al.  The Great Energy Predictor Shootout II : Measuring Retriofit Savings-Overview and Discussion of Results , 1996 .

[86]  David E. Claridge,et al.  A development and comparison of NAC estimates for linear and change-point energy models for commercial buildings , 1993 .

[87]  Michael D. Sohn,et al.  Automated measurement and verification: Performance of public domain whole-building electric baseline models , 2015 .

[88]  G. J. Levermore.  Performance lines and energy signatures: Analysis with reference to the CIBSE Building Energy Code , 1995 .

[89]  Victor M. Zavala,et al.  Gaussian process modeling for measurement and verification of building energy savings , 2012 .

[90]  Bass Abushakra,et al.  Predicting annual energy use in buildings using short-term monitoring: The dry-bulb temperature analysis (DBTA) method , 2014 .

[91]  D. E. Claridge,et al.  Impact of Nighttime Shut Down on the Prediction Accuracy of Monthly Regression Models for Energy Consumption in Commercial Buildings , 1998 .

[92]  T. A. Reddy,et al.  Methodology Development for Determining Long-Term Performance of Cool Storage Systems from Short-Term Tests , 2002 .

[93]  Hyojin Kim,et al.  Field-test of the ASHRAE/CIBSE/USGBC performance measurement protocols: Part II advanced level energy protocols , 2018 .

[94]  Fan Zhang,et al.  A review on time series forecasting techniques for building energy consumption , 2017 .

[95]  T. Agami Reddy,et al.  Prediction uncertainty of linear building energy use models with autocorrelated residuals , 1999 .

[96]  Manuel Herrera,et al.  A review of current and future weather data for building simulation , 2017 .

[97]  J. Kelly Kissock,et al.  Measuring Plant-Wide Energy Savings , 2006 .

[98]  J. M. MacDonald,et al.  Investigation of metered data analysis methods for commercial and related buildings , 1989 .

[99]  David E. Claridge,et al.  Electricity Diversity Profiles for Energy Simulation of Office Buildings , 2004 .

[100]  Vojislav Novakovic,et al.  Identifying important variables of energy use in low energy office building by using multivariate analysis , 2012 .

[101]  Krishnan Gowri,et al.  The ASHRAE Great Energy Predictor III competition: Overview and results , 2020, ArXiv.

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

[103]  D. E. Claridge,et al.  Use of first law energy balance as a screening tool for building energy data, Part I-Methodology , 2006 .

[104]  Jeff Haberl,et al.  Development of Graphical Indices for Viewing Building Energy Data: Part II , 1998 .

[105]  T. A. Reddy,et al.  Uncertainty in baseline regression modeling and in determination of retrofit savings , 1998 .

[106]  A. Rabl,et al.  Energy signature models for commercial buildings: test with measured data and interpretation , 1992 .

[107]  David E. Claridge,et al.  The functional basis of steady-state thermal energy use in air-side HVAC equipment , 1995 .