IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v82y2018ip3p2791-2805.html
   My bibliography  Save this article

Measurement uncertainty in energy monitoring: Present state of the art

Author

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

Abstract

Measurement uncertainty is a key component in the overall uncertainty calculation for Measurement and Verification (M&V) projects. However, in some cases, it is reduced to outlier detection or basic uncertainty propagation calculations. In other cases, funds are spent on determining uncertainties that have little effect on project decisions. Therefore a need exists for a fuller treatment of the subject in the light of literature from M&V and other fields. This paper surveys general M&V literature, as well as relevant research from metrology, electrical engineering, economics, decision analysis, and statistics. Electrical metering and sub-metering uncertainty is investigated, as well as often-overlooked considerations such as power quality and the cost of calibration. The effect of mismeasurement on energy models and practical techniques for mitigating such effects are assessed. Last, research on building simulation and project decisions in the light of measurement error is surveyed. Bayesian methods are found to be a recurring theme in much of the research being conducted on all of these aspects. Power quality and mismeasurement effects have also been found to make a material difference in project evaluation. The survey is concluded with recommendations for further research in the light of current trends in data analysis and energy evaluation.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:rensus:v:82:y:2018:i:p3:p:2791-2805
    DOI: 10.1016/j.rser.2017.10.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032117313825
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2017.10.006?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. Mills, Evan & Kromer, Steve & Weiss, Gary & Mathew, Paul A., 2006. "From volatility to value: analysing and managing financial and performance risk in energy savings projects," Energy Policy, Elsevier, vol. 34(2), pages 188-199, January.
    4. Vine, Edward & Kats, Gregory & Sathaye, Jayant & Joshi, Hemant, 2003. "International greenhouse gas trading programs: a discussion of measurement and accounting issues," Energy Policy, Elsevier, vol. 31(3), pages 211-224, February.
    5. Nic Rivers & Mark Jaccard, 2011. "Electric Utility Demand Side Management in Canada," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 93-116.
    6. Sharma, Konark & Mohan Saini, Lalit, 2015. "Performance analysis of smart metering for smart grid: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 720-735.
    7. 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.
    8. 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.
    9. Jackson, Jerry, 2010. "Promoting energy efficiency investments with risk management decision tools," Energy Policy, Elsevier, vol. 38(8), pages 3865-3873, August.
    10. I. D. Hill & R. Hill & R. L. Holder, 1976. "Fitting Johnson Curves by Moments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 25(2), pages 180-189, June.
    11. 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.
    12. 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.
    13. Lee, P. & Lam, P.T.I. & Lee, W.L. & Chan, E.H.W., 2016. "Analysis of an air-cooled chiller replacement project using a probabilistic approach for energy performance contracts," Applied Energy, Elsevier, vol. 171(C), pages 415-428.
    14. Kamilaris, Andreas & Kalluri, Balaji & Kondepudi, Sekhar & Kwok Wai, Tham, 2014. "A literature survey on measuring energy usage for miscellaneous electric loads in offices and commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 536-550.
    15. Friege, Jonas & Chappin, Emile, 2014. "Modelling decisions on energy-efficient renovations: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 196-208.
    16. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
    17. 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.
    18. Soroudi, Alireza & Amraee, Turaj, 2013. "Decision making under uncertainty in energy systems: State of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 376-384.
    19. Deng, Qianli & Jiang, Xianglin & Cui, Qingbin & Zhang, Limao, 2015. "Strategic design of cost savings guarantee in energy performance contracting under uncertainty," Applied Energy, Elsevier, vol. 139(C), pages 68-80.
    20. Deng, Qianli & Jiang, Xianglin & Zhang, Limao & Cui, Qingbin, 2015. "Making optimal investment decisions for energy service companies under uncertainty: A case study," Energy, Elsevier, vol. 88(C), pages 234-243.
    21. 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. Massimiliano Manfren & Maurizio Sibilla & Lamberto Tronchin, 2021. "Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector," Energies, MDPI, vol. 14(3), pages 1-29, January.
    2. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
    3. 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.
    4. Helmo K. Morales Paredes & Matheus Branco Arcadepani & Alexandre Candido Moreira & Flávio A. Serrão Gonçalves & Fernando Pinhabel Marafão, 2023. "Enlightening Load Modeling by Means of Power Factor Decompositions," Energies, MDPI, vol. 16(10), pages 1-22, May.
    5. Jose Ulises Castellanos Contreras & Leonardo Rodríguez Urrego, 2023. "Technological Developments in Control Models Using Petri Nets for Smart Grids: A Review," Energies, MDPI, vol. 16(8), pages 1-21, April.
    6. Manfren, Massimiliano & Nastasi, Benedetto & Tronchin, Lamberto & Groppi, Daniele & Garcia, Davide Astiaso, 2021. "Techno-economic analysis and energy modelling as a key enablers for smart energy services and technologies in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    7. Lucia Cattani & Anna Magrini & Valentina Leoni, 2022. "Energy Performance of Water Generators from Gaseous Mixtures by Condensation: Climatic Datasets Choice," Energies, MDPI, vol. 15(20), pages 1-24, October.
    8. Li, Lei & Huang, Haihong & Zou, Xiang & Zhao, Fu & Li, Guishan & Liu, Zhifeng, 2021. "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, Elsevier, vol. 286(C).
    9. David Macii & Daniele Fontanelli & Grazia Barchi, 2020. "A Distribution System State Estimator Based on an Extended Kalman Filter Enhanced with a Prior Evaluation of Power Injections at Unmonitored Buses," Energies, MDPI, vol. 13(22), pages 1-25, November.

    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, 2017. "Low-cost energy meter calibration method for measurement and verification," Applied Energy, Elsevier, vol. 188(C), pages 563-575.
    2. Töppel, Jannick & Tränkler, Timm, 2019. "Modeling energy efficiency insurances and energy performance contracts for a quantitative comparison of risk mitigation potential," Energy Economics, Elsevier, vol. 80(C), pages 842-859.
    3. 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.
    4. Lee, P. & Lam, P.T.I. & Lee, W.L. & Chan, E.H.W., 2016. "Analysis of an air-cooled chiller replacement project using a probabilistic approach for energy performance contracts," Applied Energy, Elsevier, vol. 171(C), pages 415-428.
    5. 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.
    6. Ackermann, Simon & Szabo, Andrei & Bamberger, Joachim & Steinke, Florian, 2022. "Design and optimization of performance guarantees for hybrid power plants," Energy, Elsevier, vol. 239(PA).
    7. Deng, Qianli & Jiang, Xianglin & Zhang, Limao & Cui, Qingbin, 2015. "Making optimal investment decisions for energy service companies under uncertainty: A case study," Energy, Elsevier, vol. 88(C), pages 234-243.
    8. Wenjie Zhang & Hongping Yuan, 2019. "A Bibliometric Analysis of Energy Performance Contracting Research from 2008 to 2018," Sustainability, MDPI, vol. 11(13), pages 1-23, June.
    9. 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.
    10. Scarpa, Federico & Tagliafico, Luca A. & Bianco, Vincenzo, 2021. "Financial and energy performance analysis of efficiency measures in residential buildings. A probabilistic approach," Energy, Elsevier, vol. 236(C).
    11. Baltuttis, Dennik & Töppel, Jannick & Tränkler, Timm & Wiethe, Christian, 2020. "Managing the risks of energy efficiency insurances in a portfolio context: An actuarial diversification approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    12. Wenjie Zhang & Hongping Yuan, 2019. "Promoting Energy Performance Contracting for Achieving Urban Sustainability: What is the Research Trend?," Energies, MDPI, vol. 12(8), pages 1-18, April.
    13. Rockstuhl, Sebastian & Wenninger, Simon & Wiethe, Christian & Ahlrichs, Jakob, 2022. "The influence of risk perception on energy efficiency investments: Evidence from a German survey," Energy Policy, Elsevier, vol. 167(C).
    14. Heutel, Garth, 2019. "Prospect theory and energy efficiency," Journal of Environmental Economics and Management, Elsevier, vol. 96(C), pages 236-254.
    15. Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.
    16. Lu, Zhijian & Shao, Shuai, 2016. "Impacts of government subsidies on pricing and performance level choice in Energy Performance Contracting: A two-step optimal decision model," Applied Energy, Elsevier, vol. 184(C), pages 1176-1183.
    17. 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.
    18. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.
    19. Ouyang, Jianjun & Fu, Jie, 2023. "Energy-saving and subsidy policy decisions for double competition manufacturers," Energy Economics, Elsevier, vol. 117(C).
    20. Herman Carstens & Xiaohua Xia & Sarma Yadavalli, 2018. "Bayesian Energy Measurement and Verification Analysis," Energies, MDPI, vol. 11(2), pages 1-20, February.

    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:rensus:v:82:y:2018:i:p3:p:2791-2805. 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/600126/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.