Foresee the Future: Using Machine Learning, Climate, and Site Characteristics to Predict PV Solar Plant Generation

Technical Report · Sun Dec 01 00:00:00 EST 2019

DOI:https://doi.org/10.2172/1592882· OSTI ID:1592882

Gunda, Thushara ^[1]; Cai, May ^[2]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2. Trimark Associates, Inc., Folsom, CA (United States)

Accurately predicting power generation for PV sites is critical for prioritizing relevant operations & maintenance activities, thereby extending the lifetime of a system and increasing the amount of revenue generated. Machine learning techniques can help us in this regard by providing more accurate predictions of PV power production, such that the forecasts take into account not only a site's system design characteristics, but also important weather and climate information. This type of research is important because we can leverage the vast amounts of SCADA data we collect to build more effective, accurate models that can help improve our performance management.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• DOE Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1592882
• Report Number(s): SAND-2019-15241R; 682084
• Country of Publication: United States
• Language: English

Related Subjects

14 SOLAR ENERGY
97 MATHEMATICS AND COMPUTING
42 ENGINEERING