Modeling Solar Radiation

In our tutorial at the 2014 IEEE Innovative Smart Grid Technologies - Asia conference, Tamer Khatib and I covered the application of machine learning techniques for energy applications, in particular for modeling solar radiation. In the first part we explored meta-heuristic search algorithms and envisioned their application for designing distributed, self-organizing control systems using evolutionary algorithms. We provide an open-source software tool, FREVO, to conveniently apply this approach of finding the proper configuration of a local agent.

In the second part, we targeted the problem of solar radiation modeling. After stepping through different classical modeling approaches, we presented the possibility of using artificial neural networks to learn the correlation of input parameters such as latitude, longitude, temperature, humidity, month, day, hour to predict global and diffuse solar radiation. Experiments show that this method can achieve a high accuracy compared to existing models.

Links:

• Download FREVO (open source, platform-independent)
• Video: 6 minute introduction to FREVO
• A. Sobe, I. Fehérvári, and W. Elmenreich. FREVO: A tool for evolving and evaluating self-organizing systems. In Proceedings of the 1st International Workshop on Evaluation for Self-Adaptive and Self-Organizing Systems, Lyon, France, September 2012.
• I. Fehervari and W. Elmenreich. Evolution as a tool to design self-organizing systems. In Self-Organizing Systems, volume LNCS 8221, pages 139–144. Springer Verlag, 2014.
• T. Khatib, A Mohamed, K Sopian. A review of solar energy modeling techniques. J. of Renewable & Sustainable Energy Reviews. 2012.16(5): 2864-2869.
• T. Khatib, A. Mohamed, K. Sopian, M. Mahmoud. Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction. J. of Photoenergy. 2012. 2012(ID 946890):1-7.
• IEEE Innovative Smart Grid Technologies - Asia conference site