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

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  • 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
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    References listed on IDEAS

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    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.
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    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.
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    3. 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).
    4. 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.
    5. 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.
    6. Kapsalis, Vasileios & Maduta, Carmen & Skandalos, Nikolaos & Wang, Meng & Bhuvad, Sushant Suresh & D'Agostino, Delia & Ma, Tao & Raj, Uday & Parker, Danny & Peng, Jinqing & Karamanis, Dimitris, 2024. "Critical assessment of large-scale rooftop photovoltaics deployment in the global urban environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
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    8. 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).

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