Solar thermal collectors and applications

Artificial neural networks in solar energy systems modelling and prediction

Artificial neural networks mimic somewhat the learning process of a human brain They are widely accepted as a technology offering an alternative way to tackle complex and ill specified 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 prediction and generalisation 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 mappings and system identification. Artificial neural networks have been used by the author in the field of solar energy, for modelling the heat-up response of a solar steam generating plant, for the estimation of a PTC intercept factor, for the estimation of a PTC local concentration ratios and for the design of a solar steam generation system. A review of these models together with other applications in the field of renewable energy is given in Ref. [126]. In all those models a multiple hidden layer architecture has been used. Errors reported are well within acceptable limits, which clearly suggest that artificial neural networks can be used for modelling and prediction in other fields of solar energy production and use. What is required is to have a set of data (preferably experimental) representing the past history of a system so as a suitable neural network can be trained to learn the dependence of expected output on the input parameters.