skip to main content
research-article
Open Access

WattScale: A Data-driven Approach for Energy Efficiency Analytics of Buildings at Scale

Published:20 January 2021Publication History
Skip Abstract Section

Abstract

Buildings consume over 40% of the total energy in modern societies, and improving their energy efficiency can significantly reduce our energy footprint. In this article, we present WattScale, a data-driven approach to identify the least energy-efficient buildings from a large population of buildings in a city or a region. Unlike previous methods such as least-squares that use point estimates, WattScale uses Bayesian inference to capture the stochasticity in the daily energy usage by estimating the distribution of parameters that affect a building. Further, it compares them with similar homes in a given population. WattScale also incorporates a fault detection algorithm to identify the underlying causes of energy inefficiency. We validate our approach using ground truth data from different geographical locations, which showcases its applicability in various settings. WattScale has two execution modes—(i) individual and (ii) region-based, which we highlight using two case studies. For the individual execution mode, we present results from a city containing >10,000 buildings and show that more than half of the buildings are inefficient in one way or another indicating a significant potential from energy improvement measures. Additionally, we provide probable cause of inefficiency and find that 41%, 23.73%, and 0.51% homes have poor building envelope, heating, and cooling system faults, respectively. For the region-based execution mode, we show that WattScale can be extended to millions of homes in the U.S. due to the recent availability of representative energy datasets.

References

  1. Alliance to Save Energy. 2020. Energy Use in Buildings. (visited on December 2020). Retrieved from https://www.ase.org/initiatives/buildings.Google ScholarGoogle Scholar
  2. ENGIE Resources. 2017. Whitepaper: Advanced Metering Infrastructure - AMI - is a fundamental part of the grid's evolution. Retrieved from https://tinyurl.com/y9sn8r9s.Google ScholarGoogle Scholar
  3. Pecan Street. 2017. Dataport dataset. Retrieved from https://dataport.cloud/.Google ScholarGoogle Scholar
  4. Green Button Alliance. 2017. Green Button Data. Retrieved from http://www.greenbuttondata.org/.Google ScholarGoogle Scholar
  5. Baltimore Gas and Electric. 2019. Load Profiles. Retrieved from https://supplier.bge.com/electric/load/profiles.asp.Google ScholarGoogle Scholar
  6. Lawrence Berkeley National Lab. 2019. Building Performance Database. Retrieved from https://bpd.lbl.gov/.Google ScholarGoogle Scholar
  7. Energy Upgrade California. 2019. California: Rebates and Incentives. Retrieved from https://www.energyupgradeca.org/home-energy-efficiency/rebates-incentives/.Google ScholarGoogle Scholar
  8. US Department of Energy. 2019. Energy Saver: Incentives and Financing for Energy Efficient Homes. Retrieved from https://www.energy.gov/energysaver/services/incentives-and-financing-energy-efficient-homes.Google ScholarGoogle Scholar
  9. NorthWestern Energy. 2019. Customer Load Profiles. Retrieved from http://www.northwesternenergy.com/for-suppliers/customer-load-profiles.Google ScholarGoogle Scholar
  10. San Diego Gas and Electric. 2019. Customer Load Profiles. Retrieved from https://www.sdge.com/more-information/doing-business-with-us/energy-service-providers/customer-load-profiles.Google ScholarGoogle Scholar
  11. Efficiency Vermont. 2019. Find Your Rebates. Retrieved from https://www.efficiencyvermont.com/rebates.Google ScholarGoogle Scholar
  12. M. Aftab, C. K. Chau, and M. Khonji. 2017. Real-time appliance identification using smart plugs: Demo abstract. In Proceedings of the 8th International Conference on Future Energy Systems.Google ScholarGoogle Scholar
  13. J. C. Allen. 1976. A modified sine wave method for calculating degree days. Environ. Entomol. 5, 3 (1976).Google ScholarGoogle Scholar
  14. FUNIP ASHRAE. 2013. Fundamentals handbook. IP Edit. (2013).Google ScholarGoogle Scholar
  15. N. Batra, O. Parson, M. Berges, A. Singh, and A. Rogers. 2014. A comparison of non-intrusive load monitoring methods for commercial and residential buildings. arXiv preprint arXiv:1408.6595.Google ScholarGoogle Scholar
  16. G. Bellala, M. Marwah, M. Arlitt, G. Lyon, and C. Bash. 2012. Following the electrons: Methods for power management in commercial buildings. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Datac Mining.Google ScholarGoogle Scholar
  17. M. A. Brown, M. Cox, B. Staver, and P. Baer. 2014. Climate change and energy demand in buildings. Proceedings of the American Council for an Energy Efficient Economy (ACEEE’14).Google ScholarGoogle Scholar
  18. William Chung, Y. V. Hui, and Y. Miu Lam. 2006. Benchmarking the energy efficiency of commercial buildings. Appl. Energy 83, 1 (2006), 1--14.Google ScholarGoogle Scholar
  19. S. De Wit. 1997. Influence of modeling uncertainties on the simulation of building thermal comfort performance. In Building Simulation, Vol. 5.Google ScholarGoogle Scholar
  20. C. Fan, F. Xiao, and S. Wang. 2014. Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques. Appl. Energy (2014).Google ScholarGoogle Scholar
  21. H. Fei, Y. Kim, S. Sahu, M. Naphade, S. K. Mamidipalli, and J. Hutchinson. 2013. Heat pump detection from coarse grained smart meter data with positive and unlabeled learning. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Google ScholarGoogle Scholar
  22. M. Fels. 1986. PRISM: An Introduction. Energy Build. (1986).Google ScholarGoogle Scholar
  23. R. Fontugne, J. Ortiz, N. Tremblay, P. Borgnat, P. Flandrin, K. Fukuda, D. Culler, and H. Esaki. 2013. Strip, bind, and search: A method for identifying abnormal energy consumption in buildings. In Proceedings of the 12th International Conference on Information Processing in Sensor Networks.Google ScholarGoogle Scholar
  24. Andrew Gelman et al. 2006. Prior distributions for variance parameters in hierarchical models (comment on article by browne and draper). Bayesian Anal. 1, 3 (2006), 515--534.Google ScholarGoogle Scholar
  25. ASHRAE Guideline. 2014. Guideline 14-2014. Measure. Energy Demand Water Sav. (2014).Google ScholarGoogle Scholar
  26. G. Hart. 1992. Nonintrusive appliance load monitoring. Proc. IEEE (1992).Google ScholarGoogle Scholar
  27. J. S. Hygh, J. F. DeCarolis, D. B. Hill, and S. R. Ranjithan. 2012. Multivariate regression as an energy assessment tool in early building design. Build. Environ. (2012).Google ScholarGoogle Scholar
  28. S. Iyengar, S. Lee, D. Irwin, and P. Shenoy. 2016. Analyzing energy usage on a city-scale using utility smart meters. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments.Google ScholarGoogle Scholar
  29. P. Jacobs and H. Henderson. 2002. State-of-the-art review of whole building, building envelope, and HVAC component and system simulation and design tools. Architect. Energy Corp. (2002).Google ScholarGoogle Scholar
  30. H. Janetzko, F. Stoffel, S. Mittelstädt, and D. A. Keim. 2014. Anomaly detection for visual analytics of power consumption data. Comput. Graphics (2014).Google ScholarGoogle Scholar
  31. S. Katipamula and M. Brambley. 2005. Review article: Methods for fault detection, diagnostics, and prognostics for building systems—A review, Part I. HVAC8R Research.Google ScholarGoogle Scholar
  32. J. Kelso (Ed.). 2012. Buildings Energy Data Book. Department of Energy.Google ScholarGoogle Scholar
  33. J. Kissock, J. Haberl, and D. Claridge. 2002. Development of a Toolkit for Calculating Linear, Change-Point Linear and Multiple-Linear Inverse Building Energy Analysis Models. Technical Report. Texas A8M University.Google ScholarGoogle Scholar
  34. H. Levy. 2015. Stochastic Dominance: Investment Decision Making Under Uncertainty. Springer.Google ScholarGoogle Scholar
  35. J. E. Seem. 2007. Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy Build. (2007).Google ScholarGoogle Scholar
  36. HCS Thom. 1954. The rational relationship between heating degree days and temperature. Monthly Weather Rev. (1954).Google ScholarGoogle Scholar
  37. S. Wang, C. Yan, and F. Xiao. 2012. Quantitative energy performance assessment methods for existing buildings. Energy Build. (2012).Google ScholarGoogle Scholar
  38. Zeyu Wang and Ravi S. Srinivasan. 2017. A review of artificial intelligence-based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renew. Sustain. Energy Rev. 75 (2017), 796--808.Google ScholarGoogle ScholarCross RefCross Ref
  39. C. Yan, S. Wang, and F. Xiao. 2012. A simplified energy performance assessment method for existing buildings based on energy bill disaggregation. Energy Build. (2012).Google ScholarGoogle Scholar
  40. H. Zhao and F. Magoulès. 2012. A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. (2012).Google ScholarGoogle Scholar
  41. Q. Zhou, S. Wang, and Z. Ma. 2009. A model-based fault detection and diagnosis strategy for HVAC systems. Int. J. Energy Res. (2009).Google ScholarGoogle Scholar

Index Terms

  1. WattScale: A Data-driven Approach for Energy Efficiency Analytics of Buildings at Scale

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          • Published in

            cover image ACM/IMS Transactions on Data Science
            ACM/IMS Transactions on Data Science  Volume 2, Issue 1
            Survey Paper, Special Issue on Urban Computing and Smart Cities and Regular Paper
            February 2021
            167 pages
            ISSN:2691-1922
            DOI:10.1145/3446658
            Issue’s Table of Contents

            Copyright © 2021 ACM

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 20 January 2021
            • Accepted: 1 June 2020
            • Revised: 1 February 2020
            • Received: 1 June 2019
            Published in tds Volume 2, Issue 1

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format .

          View HTML Format
          About Cookies On This Site

          We use cookies to ensure that we give you the best experience on our website.

          Learn more

          Got it!