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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References
Wills, L., et al.: An Open Platform for Reconfigurable Control. IEEE Control Systems 21(3), 49–64 (2001)
Feldmann, K., Stockel, T., Gaberstumpf, B.: Conception and Implementation of an Object Request Broker for the Integration of the Process Level in Manufacturing Systems. J. Systems Ingetration 10(2), 169–180 (2001)
Raptis, D., Spinellis, D., Katsikas, S.: Multi-Technology Distributed Objects and Their Integration. Computer Standards and Interfaces 23(3), 157–168 (2001)
Schellenberg, F.M., Toublan, O., Capodieci, L., Socha, B.: Adoption of OPC and the Impact on Design and Layout. In: DAC 2001, Las Vegas, Nevada, USA, June 18-22 (2001)
Liu, J., Lim, K.W., Ho, W.K., Tan, K.C., Tay, A., Srinivasan, R.: IEEE Software 22(6), 54–59 (2005)
Schwarz, M.H., Boercsoek, J.: International Journal of Computers 1(4), 245 (2007)
Schwarz, M.H., Boercsoek, J.: WSEAS Transactions on Systems and Control 3(3), 195 (2008)
Horstmann, M., Kirtland, M.: DCOM Architecture, http://msdn.microsoft.com/library/
The World Wide Web Consortium (W3C) XML Specifications: http://www.w3.org/XML/
Tan, V.V., Yoo, D.S., Yi, M.J., Modern, M.J.: Proceedings 14th European Photovoltaic Solar Energy Conference, vol. 115 (2007)
Anderson, J.A.: An Introduction to Neural Networks. The MIT Press, Cambridge (1995)
Hassoun, M.H.: Fundamentals of Artificial Neural Networks. The MIT Press, Cambridge (1995)
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to The Theory of Neural Computing. Addison-Wesley Publishing Company, Reading (1991)
Nissen, S.: Implementation of a Fast Artificial Neural Network Library (fann), Graduate project, Department of Computer Science. University of Copenhagen, DIKU (October 2003)
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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
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