Applications of artificial neural-networks for energy systems
AbstractArtificial 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 generalisations at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing, and social/psychological sciences. They are particularly useful in system modelling, 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 modelling 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 modelling 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 test room and for the prediction of the energy consumption of a passive solar building. In all such models, a multiple hidden-layer architecture has been used. Errors reported when using these models are well within acceptable limits, which clearly suggests that artificial neural-networks can be used for modelling in other fields of energy production and use. The work of other researchers in the field of energy is also reported. This includes the use of artificial neural-networks in heating, ventilating and air-conditioning systems, solar radiation, modelling and control of power-generation systems, load-forecasting and refrigeration.
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Bibliographic InfoArticle provided by Elsevier in its journal Applied Energy.
Volume (Year): 67 (2000)
Issue (Month): 1-2 (September)
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