The use of solar heating systems is a way of exploiting the clean and free energy from the sun. To optimize the energy gain from such a system, where the main input, the solar insolation, is an uncontrollable variable, good models of the system dynamics are required. Identification methods are often either highly specialized for the application or require an extensive amount of data, especially when the dynamics studied are nonlinear. This paper shows that by application of a new recursive system identification technique, a small scale solar heating system can be modeled with very little data, without having to tailor the model structure to the application.
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