IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v267y2020ics0306261920304074.html
   My bibliography  Save this article

A method to estimate residential PV generation from net-metered load data and system install date

Author

Listed:
  • Stainsby, Wendell
  • Zimmerle, Daniel
  • Duggan, Gerald P.

Abstract

In the USA, residential photovoltaic (PV) systems are often configured for net metering “behind-the-meter”, where PV energy generation and building energy demand are reported as a combined net load to advanced metering infrastructure (AMI) meters, impeding estimates of PV generation. This work presents a methodology for modeling individual array and system-wide PV generation using only weather data, premise AMI data, and the approximate date of PV installation – information available to most distribution utilities. The study uses 36 months of data spanning nearly 850 homes with installed PV systems in Fort Collins, Colorado, USA. The algorithm estimates building energy consumption by comparing time periods before PV installation with similar periods after PV installation that have common weather and activity characteristics. Estimated building energy consumption is then compared with AMI meter data to estimate otherwise unobservable solar generation. To assess accuracy, modeled outputs are compared with directly metered PV generation and white-box physical models of PV production. Considering aggregate, utility-wide, generation estimates for the three year study period, the proposed method estimates over 75% of all days to within ±20% of established physical models. The method estimates more effectively in summer months when PV generation peaks and is of most interest to utilities. The model often outperforms physical models for days with snow cover and for arrays with shading or complex multi-roof implementations. The model also supports day-ahead PV prediction using forecasted weather data.

Suggested Citation

  • Stainsby, Wendell & Zimmerle, Daniel & Duggan, Gerald P., 2020. "A method to estimate residential PV generation from net-metered load data and system install date," Applied Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920304074
    DOI: 10.1016/j.apenergy.2020.114895
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920304074
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.114895?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. Yuan, Shengxi & Stainsby, Wendell & Li, Mo & Xu, Kewei & Waite, Michael & Zimmerle, Dan & Feiock, Richard & Ramaswami, Anu & Modi, Vijay, 2019. "Future energy scenarios with distributed technology options for residential city blocks in three climate regions of the United States," Applied Energy, Elsevier, vol. 237(C), pages 60-69.
    2. Qiu, Yueming & Kahn, Matthew E. & Xing, Bo, 2019. "Quantifying the rebound effects of residential solar panel adoption," Journal of Environmental Economics and Management, Elsevier, vol. 96(C), pages 310-341.
    3. Tarroja, Brian & Mueller, Fabian & Eichman, Joshua D. & Samuelsen, Scott, 2012. "Metrics for evaluating the impacts of intermittent renewable generation on utility load-balancing," Energy, Elsevier, vol. 42(1), pages 546-562.
    4. DeBenedictis, A. & Hoff, T.E. & Price, S. & Woo, C.K., 2010. "Statistically adjusted engineering (SAE) modeling of metered roof-top photovoltaic (PV) output: California evidence," Energy, Elsevier, vol. 35(10), pages 4178-4183.
    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. Pereira, Diogo Santos & Marques, António Cardoso, 2020. "How should price-responsive electricity tariffs evolve? An analysis of the German net demand case," Utilities Policy, Elsevier, vol. 66(C).
    2. Pan, Keda & Chen, Zhaohua & Lai, Chun Sing & Xie, Changhong & Wang, Dongxiao & Li, Xuecong & Zhao, Zhuoli & Tong, Ning & Lai, Loi Lei, 2022. "An unsupervised data-driven approach for behind-the-meter photovoltaic power generation disaggregation," Applied Energy, Elsevier, vol. 309(C).
    3. Honglu Zhu & Tingting Jiang & Yahui Sun & Shuang Sun, 2022. "A New Regional Distributed Photovoltaic Power Calculation Method Based on FCM-mRMR and nELM Model," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    4. Yuan-Kang Wu & Yi-Hui Lai & Cheng-Liang Huang & Nguyen Thi Bich Phuong & Wen-Shan Tan, 2022. "Artificial Intelligence Applications in Estimating Invisible Solar Power Generation," Energies, MDPI, vol. 15(4), pages 1-18, February.
    5. Liu, Chao Charles & Chen, Hongkun & Shi, Jing & Chen, Lei, 2022. "Self-supervised learning method for consumer-level behind-the-meter PV estimation," Applied Energy, Elsevier, vol. 326(C).
    6. Shaojie Li & Tao Zhang & Xiaochen Liu & Xiaohua Liu, 2023. "A Battery Capacity Configuration Method of a Photovoltaic and Battery System Applied in a Building Complex for Increased Self-Sufficiency and Self-Consumption," Energies, MDPI, vol. 16(5), pages 1-18, February.
    7. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).

    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. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Fikru, Mahelet G. & Gautier, Luis, 2023. "Consumption and production of cleaner energy by prosumers," Energy Economics, Elsevier, vol. 124(C).
    3. Maliyamu Abudureheman & Qingzhe Jiang & Xiucheng Dong & Cong Dong, 2022. "CO 2 Emissions in China: Does the Energy Rebound Matter?," Energies, MDPI, vol. 15(12), pages 1-25, June.
    4. Robu, Valentin & Chalkiadakis, Georgios & Kota, Ramachandra & Rogers, Alex & Jennings, Nicholas R., 2016. "Rewarding cooperative virtual power plant formation using scoring rules," Energy, Elsevier, vol. 117(P1), pages 19-28.
    5. Kim, Jae D. & Trevena, William, 2021. "Measuring the rebound effect: A case study of residential photovoltaic systems in San Diego," Utilities Policy, Elsevier, vol. 69(C).
    6. Aydın, Erdal & Brounen, Dirk & Ergün, Ahmet, 2023. "The rebound effect of solar panel adoption: Evidence from Dutch households," Energy Economics, Elsevier, vol. 120(C).
    7. Atasoy, Ayse Tugba & Schmitz, Hendrik & Madlener, Reinhard, 2021. "Mechanisms for Rebound Effects of Solar Electricity Prosuming in Germany," FCN Working Papers 10/2021, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised 01 Jun 2023.
    8. Peter M. Schwarz, Nathan Duma, and Ercument Camadan, 2023. "Compensating Solar Prosumers Using Buy-All, Sell-All as an Alternative to Net Metering and Net Purchasing: Total Use, Rebound, and Cross Subsidization," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    9. Raúl Castaño-Rosa & Roberto Barrella & Carmen Sánchez-Guevara & Ricardo Barbosa & Ioanna Kyprianou & Eleftheria Paschalidou & Nikolaos S. Thomaidis & Dusana Dokupilova & João Pedro Gouveia & József Ká, 2021. "Cooling Degree Models and Future Energy Demand in the Residential Sector. A Seven-Country Case Study," Sustainability, MDPI, vol. 13(5), pages 1-25, March.
    10. Akpan, P.U. & Fuls, W.F., 2021. "Cycling of coal fired power plants: A generic CO2 emissions factor model for predicting CO2 emissions," Energy, Elsevier, vol. 214(C).
    11. Caballero, F. & Sauma, E. & Yanine, F., 2013. "Business optimal design of a grid-connected hybrid PV (photovoltaic)-wind energy system without energy storage for an Easter Island's block," Energy, Elsevier, vol. 61(C), pages 248-261.
    12. Liu, Liuchen & Zhu, Tong & Pan, Yu & Wang, Hai, 2017. "Multiple energy complementation based on distributed energy systems – Case study of Chongming county, China," Applied Energy, Elsevier, vol. 192(C), pages 329-336.
    13. Fridgen, Gilbert & Halbrügge, Stephanie & Olenberger, Christian & Weibelzahl, Martin, 2020. "The insurance effect of renewable distributed energy resources against uncertain electricity price developments," Energy Economics, Elsevier, vol. 91(C).
    14. Abajian, Alexander & Pretnar, Nick, 2023. "Subsidies for Close Substitutes: Evidence from Residential Solar Systems," MPRA Paper 118171, University Library of Munich, Germany.
    15. Frondel, Manuel & Kaestner, Kathrin & Sommer, Stephan & Vance, Colin, 2022. "Photovoltaics and the solar rebound: Evidence for Germany," Ruhr Economic Papers 954, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    16. Flora, Rui & Marques, António Cardoso & Fuinhas, José Alberto, 2014. "Wind power idle capacity in a panel of European countries," Energy, Elsevier, vol. 66(C), pages 823-830.
    17. Mads Raunbak & Timo Zeyer & Kun Zhu & Martin Greiner, 2017. "Principal Mismatch Patterns Across a Simplified Highly Renewable European Electricity Network," Energies, MDPI, vol. 10(12), pages 1-13, November.
    18. Boccard, Nicolas & Gautier, Axel, 2021. "Solar rebound: The unintended consequences of subsidies," Energy Economics, Elsevier, vol. 100(C).
    19. Liu, Diyi & Qi, Suntong & Xu, Tiantong, 2023. "In the post-subsidy era: How to encourage mere consumers to become prosumers when subsidy reduced?," Energy Policy, Elsevier, vol. 174(C).
    20. Belaïd, Fateh & Youssef, Adel Ben & Lazaric, Nathalie, 2020. "Scrutinizing the direct rebound effect for French households using quantile regression and data from an original survey," Ecological Economics, Elsevier, vol. 176(C).

    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:267:y:2020:i:c:s0306261920304074. 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.