IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v188y2017icp563-575.html
   My bibliography  Save this article

Low-cost energy meter calibration method for measurement and verification

Author

Listed:
  • Carstens, Herman
  • Xia, Xiaohua
  • Yadavalli, Sarma

Abstract

Energy meters need to be calibrated for use in Measurement and Verification (M&V) projects. However, calibration can be prohibitively expensive and affect project feasibility negatively. This study presents a novel low-cost in-situ meter data calibration technique using a relatively low accuracy commercial energy meter as a calibrator. Calibration is achieved by combining two machine learning tools: the SIMulation EXtrapolation (SIMEX) Measurement Error Model and Bayesian regression. The model is trained or calibrated on half-hourly building energy data for 24h. Measurements are then compared to the true values over the following months to verify the method. Results show that the hybrid method significantly improves parameter estimates and goodness of fit when compared to Ordinary Least Squares regression or standard SIMEX. This study also addresses the effect of mismeasurement in energy monitoring, and implements a powerful technique for mitigating the bias that arises because of it. Meters calibrated by the technique presented have adequate accuracy for most M&V applications, at a significantly lower cost.

Suggested Citation

  • Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2017. "Low-cost energy meter calibration method for measurement and verification," Applied Energy, Elsevier, vol. 188(C), pages 563-575.
    • Handle: RePEc:eee:appene:v:188:y:2017:i:c:p:563-575
    DOI: 10.1016/j.apenergy.2016.12.028
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916317913
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.12.028?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ye, Xianming & Xia, Xiaohua & Zhang, Jiangfeng, 2014. "Optimal sampling plan for clean development mechanism lighting projects with lamp population decay," Applied Energy, Elsevier, vol. 136(C), pages 1184-1192.
    2. Granderson, Jessica & Touzani, Samir & Custodio, Claudine & Sohn, Michael D. & Jump, David & Fernandes, Samuel, 2016. "Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings," Applied Energy, Elsevier, vol. 173(C), pages 296-308.
    3. Carstens, Herman & Xia, Xiaohua & Ye, Xianming, 2014. "Improvements to longitudinal Clean Development Mechanism sampling designs for lighting retrofit projects," Applied Energy, Elsevier, vol. 126(C), pages 256-265.
    4. Xia, Xiaohua & Zhang, Jiangfeng, 2013. "Mathematical description for the measurement and verification of energy efficiency improvement," Applied Energy, Elsevier, vol. 111(C), pages 247-256.
    5. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    6. Walter, Travis & Price, Phillip N. & Sohn, Michael D., 2014. "Uncertainty estimation improves energy measurement and verification procedures," Applied Energy, Elsevier, vol. 130(C), pages 230-236.
    7. Ye, Xianming & Xia, Xiaohua, 2016. "Optimal metering plan for measurement and verification on a lighting case study," Energy, Elsevier, vol. 95(C), pages 580-592.
    8. Wang, Qinpeng & Augenbroe, Godfried & Kim, Ji-Hyun & Gu, Li, 2016. "Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings," Applied Energy, Elsevier, vol. 174(C), pages 166-180.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2018. "Measurement uncertainty in energy monitoring: Present state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2791-2805.
    2. Calama-González, Carmen María & Symonds, Phil & Petrou, Giorgos & Suárez, Rafael & León-Rodríguez, Ángel Luis, 2021. "Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring," Applied Energy, Elsevier, vol. 282(PA).
    3. Muhammad Waseem Ahmad & Anthony Mouraud & Yacine Rezgui & Monjur Mourshed, 2018. "Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption," Energies, MDPI, vol. 11(12), pages 1-21, December.
    4. Lim, Hyunwoo & Zhai, Zhiqiang (John), 2018. "Influences of energy data on Bayesian calibration of building energy model," Applied Energy, Elsevier, vol. 231(C), pages 686-698.
    5. Herman Carstens & Xiaohua Xia & Sarma Yadavalli, 2018. "Bayesian Energy Measurement and Verification Analysis," Energies, MDPI, vol. 11(2), pages 1-20, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2018. "Measurement uncertainty in energy monitoring: Present state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2791-2805.
    2. Olinga, Zadok & Xia, Xiaohua & Ye, Xianming, 2017. "A cost-effective approach to handle measurement and verification uncertainties of energy savings," Energy, Elsevier, vol. 141(C), pages 1600-1609.
    3. Ye, Xianming & Xia, Xiaohua, 2016. "Optimal metering plan for measurement and verification on a lighting case study," Energy, Elsevier, vol. 95(C), pages 580-592.
    4. Ke, Ming-Tsun & Yeh, Chia-Hung & Su, Cheng-Jie, 2017. "Cloud computing platform for real-time measurement and verification of energy performance," Applied Energy, Elsevier, vol. 188(C), pages 497-507.
    5. Fan, Yuling & Xia, Xiaohua, 2018. "Building retrofit optimization models using notch test data considering energy performance certificate compliance," Applied Energy, Elsevier, vol. 228(C), pages 2140-2152.
    6. Angeliki Mavrigiannaki & Kostas Gobakis & Dionysia Kolokotsa & Kostas Kalaitzakis & Anna Laura Pisello & Cristina Piselli & Rajat Gupta & Matt Gregg & Marina Laskari & Maria Saliari & Margarita-Niki A, 2020. "Measurement and Verification of Zero Energy Settlements: Lessons Learned from Four Pilot Cases in Europe," Sustainability, MDPI, vol. 12(22), pages 1-16, November.
    7. Simon Rouchier, 2022. "Bayesian Workflow and Hidden Markov Energy-Signature Model for Measurement and Verification," Energies, MDPI, vol. 15(10), pages 1-19, May.
    8. Herman Carstens & Xiaohua Xia & Sarma Yadavalli, 2018. "Bayesian Energy Measurement and Verification Analysis," Energies, MDPI, vol. 11(2), pages 1-20, February.
    9. Liang, Xin & Hong, Tianzhen & Shen, Geoffrey Qiping, 2016. "Improving the accuracy of energy baseline models for commercial buildings with occupancy data," Applied Energy, Elsevier, vol. 179(C), pages 247-260.
    10. Ikuzwe, Alice & Ye, Xianming & Xia, Xiaohua, 2020. "Energy-maintenance optimization for retrofitted lighting system incorporating luminous flux degradation to enhance visual comfort," Applied Energy, Elsevier, vol. 261(C).
    11. Ye, Xianming & Xia, Xiaohua & Zhang, Jiangfeng, 2014. "Optimal sampling plan for clean development mechanism lighting projects with lamp population decay," Applied Energy, Elsevier, vol. 136(C), pages 1184-1192.
    12. Ikuzwe, Alice & Xia, Xiaohua & Ye, Xianming, 2020. "Maintenance optimization incorporating lumen degradation failure for energy-efficient lighting retrofit projects," Applied Energy, Elsevier, vol. 267(C).
    13. Fu, Hongxiang & Baltazar, Juan-Carlos & Claridge, David E., 2021. "Review of developments in whole-building statistical energy consumption models for commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    14. Vanslette, Kevin & Tohme, Tony & Youcef-Toumi, Kamal, 2020. "A general model validation and testing tool," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    15. Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
    16. Jakub Bijak & Jason D. Hilton & Eric Silverman & Viet Dung Cao, 2013. "Reforging the Wedding Ring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(27), pages 729-766.
    17. Hao Wu & Michael Browne, 2015. "Random Model Discrepancy: Interpretations and Technicalities (A Rejoinder)," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 619-624, September.
    18. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    19. Villez, Kris & Del Giudice, Dario & Neumann, Marc B. & Rieckermann, Jörg, 2020. "Accounting for erroneous model structures in biokinetic process models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    20. Xiaoyu Xiong & Benjamin D. Youngman & Theodoros Economou, 2021. "Data fusion with Gaussian processes for estimation of environmental hazard events," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:188:y:2017:i:c:p:563-575. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.