Applications of artificial neural-networks for energy system

Artificial neural networks offer an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform predictions and generalizations at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences. They are particularly useful in system modeling, such as in implementing complex mapping and system identification. This paper presents various applications of neural networks in energy problems in a thematic rather than a chronological or any other way. Artificial neural networks have been used by the author in the field of solar energy; for modeling and design of a solar steam generating plant, for the estimation of a parabolic-trough collector's intercept factor and local concentration ratio and for the modeling and performance prediction of solar water-heating systems. They have also been used for the estimation of heating-loads of buildings, for the prediction of air flows in a naturally ventilated tes

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