Abstract
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 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 ¯ows 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 arti®cial neural-networks
can be used for modelling in other fields of energy production and
use. The work of other researchers in the ®eld 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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