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This talk covers the application of machine learning techniques for energy applications, in particular for modeling solar radiation. The first part explores metaheuristic search algorithms and envisioned their application for designing distributed, selforganizing control systems using evolutionary algorithms. The second part gives an introduction to solar radiation modeling and shows how neural networks can be used to 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.
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