Abstract
This paper describes a framework to develop software to monitor and evaluate solar installations using machine-learning models and OPC technology. The proposed framework solves both the problem of monitoring solar installations when there are devices from different manufacturers and the problem of evaluating solar installations whose operation changes throughout the plant operation period. Moreover, the evaluation programs can be integrated with the monitoring problems. The proposed solution is based on the use of machine-learning models to evaluate the plants and on the use of OPC technology to integrate the monitoring program with the evaluation program. This framework has been used for monitoring and evaluating several real photovoltaic solar plants.
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Martinez-Marchena, I., Mora-Lopez, L., Sanchez, P.J., Sidrach-de-Cardona, M. (2010). Binding Machine Learning Models and OPC Technology for Evaluating Solar Energy Systems. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13033-5_62
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DOI: https://doi.org/10.1007/978-3-642-13033-5_62
Publisher Name: Springer, Berlin, Heidelberg
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