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Improvement of inverse change-point modeling of electricity consumption in residential buildings across multiple climate zones

  • Huyen Do
  • Kristen S CetinEmail author
Research Article
  • 5 Downloads

Abstract

Inverse modeling is a common method to predict electricity consumption in buildings. Residential building electricity consumption patterns can vary significantly due to occupants and their sporadic energy-consuming behaviors, as well as due to variations in HVAC system types and characteristics across climate zones. However most data-driven methods in this area have been developed and evaluated using limited datasets. This points to the need for an understanding of how well data-driven models perform using residential energy consumption data from a range of locations and home types using a diverse dataset. Thus in this research, first, inverse change-point modeling methods are used to develop predictive models of monthly electricity use for 3,643 houses in four U.S. cities in three ASHRAE climate zones (2A, 4A, 5A), to evaluate the model performance. However, approximately 40% of homes did not fit within recommended criteria for change-point model development following a common model development sequence. Therefore, a modified version of the sequence, including a segmented change-point model, is then developed and evaluated. Change-point models with relaxed prerequisite criteria are also used to enable the fitting of models to a larger number of homes’ data. As a result of these modifications, the number of homes with models increased from 60% to 71%, with a goodness-of-fit improvement of 13% (RMSE) and 8% (CV-RMSE) across the datasets evaluated. The results of this work enable improved prediction of energy use across a diversity of buildings and climate zones.

Keywords

inverse modeling change-point electricity use residential buildings 

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Notes

Acknowledgements

This study was supported, in part, by The Vietnam Ministry of Education and Training, and Earth Networks, provided data. However, any results, opinions, and conclusions in this paper do not reflect the views of these organizations and are strictly those of the authors.

References

  1. Abushakra B, Paulus MT (2016a). An hourly hybrid multi-variate change-point inverse model using short-term monitored data for annual prediction of building energy performance, part I: Background (1404-RP). Science and Technology for the Built Environment, 22: 976–983.CrossRefGoogle Scholar
  2. Abushakra B, Paulus MT (2016b). 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). Science and Technology for the Built Environment, 22: 984–995.CrossRefGoogle Scholar
  3. Abushakra B, Paulus MT (2016c). 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). Science and Technology for the Built Environment, 22: 996–1009.CrossRefGoogle Scholar
  4. Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 33: 102–109.CrossRefGoogle Scholar
  5. ASHRAE (2014). ASHRAE Guideline 14 - 2014. Measurement of Energy and Demand Savings. Atlanta, GA, USA: The American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  6. ASHRAE (2017a). ASHRAE Handbook—Fundamentals. Atlanta, GA, USA: The American Society of Heating, Refrigerating and Air- Conditioning Engineers.Google Scholar
  7. ASHRAE (2017b). ASHRAE Standard 140 - 2017. Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Atlanta, GA, USA: The American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  8. Bassamzadeh N, Ghanem R (2017). Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks. Applied Energy, 193: 369–380.CrossRefGoogle Scholar
  9. Biswas MAR, Robinson MD, Fumo N (2016). Prediction of residential building energy consumption: A neural network approach. Energy, 117: 84–92.CrossRefGoogle Scholar
  10. Boston (2017). Building energy reporting and disclosure ordinance (BERDO). The city of Boston. Available at https://www.boston.gov/environment-and-energy/building-energy-reporting-and-disclosureordinance. Accessed Jun 2018.Google Scholar
  11. Castelli M, Trujillo L, Vanneschi L, Popovič A (2015). Prediction of energy performance of residential buildings: A genetic programming approach. Energy and Buildings, 102: 67–74.CrossRefGoogle Scholar
  12. Cetin KS, Novoselac A (2015). Single and multi-family residential central all-air HVAC system operational characteristics in coolingdominated climate. Energy and Buildings, 96: 210–220.CrossRefGoogle Scholar
  13. Cetin KS, Siemann M, Sloop C (2016). Disaggregation and future prediction of monthly residential building energy use data using localized weather data network. In: Proceedings of ACEEE Summer Study on Energy Efficient Buildings, Pacific Grove, CA, USA.Google Scholar
  14. Chen Z, Freihaut J, Lin B, Wang CD (2018). Inverse energy model development via high-dimensional data analysis and sub-metering priority in building data monitoring. Energy and Buildings, 172: 116–124.CrossRefGoogle Scholar
  15. Department of Energy & Environment (2018). Washington D.C. Available at https://doee.dc.gov/energybenchmarking. Accessed Jun 2018.Google Scholar
  16. Do H, Cetin KS (2018a). Evaluation of the causes and impact of outliers on residential building energy use prediction using inverse modeling. Building and Environment, 138: 194–206.CrossRefGoogle Scholar
  17. Do H, Cetin KS (2018b). Impact of occupant behavior in data-driven energy use modeling in diverse residential buildings across multiple climates. In: Proceedings of the 4th Residential Building Design & Construction Conference, State College, PA, USA.Google Scholar
  18. Do H, Cetin KS (2018c). Residential building energy consumption: A review of energy data availability, characteristics, and energy performance prediction methods. Current Sustainable/Renewable Energy Reports, 5: 76–85.CrossRefGoogle Scholar
  19. Do H, Cetin K, Andersen T (2018). Characteristics and causes of outliers in inverse modeling of residential building energy use data. In: Proceedings of the ASHRAE Winter Conference, Chicago, USA.Google Scholar
  20. Grace-Martin K (2012). Assessing the fit of regression models. Cornell University. Cornell Statistical Consulting Unit. Available at https://www.cscu.cornell.edu/news/statnews/stnews68.pdf. Accessed Jun 2018.Google Scholar
  21. Haberl JS, Claridge DE, Sreshthaputra A, Kissock JK (2003). Inverse modeling toolkit: Application and testing. ASHRAE Transactions, 109(2): 435–448.Google Scholar
  22. Hong T, D’Oca S, Turner WJN, Taylor-Lange SC (2015a). An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework. Building and Environment, 92: 764–777.Google Scholar
  23. Hong T, D’Oca S, Taylor-Lange SC, Turner WJN, Chen Y, Corgnati SP (2015b). An ontology to represent energy-related occupant behavior in buildings. Part II: Implementation of the DNAS framework using an XML schema. Building and Environment, 94: 196–205.Google Scholar
  24. Jain RK, Smith KM, Culligan PJ, Taylor JE (2014). Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy, 123: 168–178.CrossRefGoogle Scholar
  25. Jovanović RŽ, Sretenović AA, Živković BD (2015). Ensemble of various neural networks for prediction of heating energy consumption. Energy and Buildings, 94: 189–199.CrossRefGoogle Scholar
  26. Jung HC, Kim JS, Heo H (2015). Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach. Energy and Buildings, 90: 76–84.CrossRefGoogle Scholar
  27. Kim KH, Haberl JS (2015a). Development of methodology for calibrated simulation in single-family residential buildings using three-parameter change-point regression model. Energy and Buildings, 99: 140–152.CrossRefGoogle Scholar
  28. Kim KH, Haberl JS (2015b). Development of a home energy audit methodology for determining energy-efficient, cost-effective measures in existing single-family houses using an easy-to-use simulation. Building Simulation, 8: 515–528.CrossRefGoogle Scholar
  29. Kim KH, Haberl JS (2016). Development of a home energy audit methodology for determining energy and cost efficient measures using an easy-to-use simulation: Test results from single-family houses in Texas, USA. Building Simulation, 9: 617–628.CrossRefGoogle Scholar
  30. Kissock JK, Haberl JS, Claridge DE (2003). Inverse modeling toolkit: Numerical algorithms. ASHRAE Transactions, 109(2): 438–448.Google Scholar
  31. Li R, Wang Z, Gu C, Li F, Wu H (2016). A novel time-of-use tariff design based on Gaussian Mixture Model. Applied Energy, 162: 1530–1536.CrossRefGoogle Scholar
  32. New York (2018). NYC Resource. Office of Sustainability. The city of New York. Available at http://www.nyc.gov/html/gbee/html/home/home.shtml. Accessed Jun 2018.Google Scholar
  33. Niu F, O’Neill Z, O’Neill C (2018). Data-driven based estimation of HVAC energy consumption using an improved Fourier series decomposition in buildings. Building Simulation, 11: 633–645.CrossRefGoogle Scholar
  34. O’Neill Z, O’Neill C (2016). Development of a probabilistic graphical model for predicting building energy performance. Applied Energy, 164: 650–658.CrossRefGoogle Scholar
  35. Pachauri RK, Meyer LA (2014). Climate change 2014. Synthesis report. Intergovernmental Panel on Climate Change and World Meteorological Organization, Geneva, Switzerland.Google Scholar
  36. Paulus MT, Claridge DE, Culp C (2015). Algorithm for automating the selection of a temperature dependent change point model. Energy and Buildings, 87: 95–104.CrossRefGoogle Scholar
  37. US DOE (2002). International performance measurement & verification protocol (IPMVP). Concepts and options for determining energy and water savings. United States Department of Energy. Available at https://www.nrel.gov/docs/fy02osti/31505.pdf. Accessed Jun 2018.Google Scholar
  38. US DOE (2018). Energy savings performance contracting. Office of Energy Efficiency & Renewable Energy. United States Department of Energy. Available at https://www.energy.gov/eere/femp/energysavings- performance-contracts-federal-agencies. Accessed Jun 2018.Google Scholar
  39. US EIA (2016). How much energy is consumed in residential and commercial buildings in the United States? United States Energy Information Administration, U.S. Department of Energy. Available at https://www.eia.gov/tools/faqs/faq.php?id=86&t=1. Accessed Jun 2018.Google Scholar
  40. US EPA (2019). Energy Star. Use Portfolio Manager. Available at https://www.energystar.gov/buildings/facility-owners-and-managers/existing-buildings/use-portfolio-manager. Accessed Jan 2019.Google Scholar
  41. Zhang Y, O’Neill Z, Dong B, Augenbroe G (2015). Comparisons of inverse modeling approaches for predicting building energy performance. Building and Environment, 86: 177–190.CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Project ManagementThe University of Danang - University of Science and TechnologyDanangVietnam
  2. 2.Department of Civil Construction and Environmental EngineeringIowa State UniversityAmesUSA

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