Predicting Solar Plant Generation with Machine Learning Techniques
- Trimark Associates Inc., Folsom, CA (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (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 improving profit margins. A number of factors influence power generation at PV sites, including local weather, shading and soiling losses, design of modules, DC mismatches, and degradation over time. Other external factors such as curtailment and grid outages can also have a notable impact on power generation. Machine learning techniques can be used to provide more accurate predictions of PV power production by accounting for important weather and climate information neglected by current industry methods. This article will cover the deficiencies of those methods and will show how machine learning can dramatically improve power generation predictions.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Trimark Associates Inc., Folsom, CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1598939
- Report Number(s):
- SAND--2020-1521R; 683577
- Country of Publication:
- United States
- Language:
- English
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