Abstract
Control may be carried out using compact mathematical models which describe the thermal behaviour of building systems. In this paper, the performance of advanced stochastic model-based control algorithms for use in building energy management systems (BEMS) is compared with that of the conventional control techniques currently employed in such systems. Test cell experiments show that model-based predictive on/off control is an improvement over standard on/off control, giving an energy saving of 11%. Based on a simulation approach, further comparisons show that the standard proportional-integral-derivative (PID) and minimum variance predictive control methodologies give comparable performances but that both are superior to the on/off cases. It is pointed out, however, that the minimum variance method can be improved so as to out-perform the PID technique. The modelling technique is then applied to a full-scale occupied zone, the model derived giving excellent agreement with measured data. The work demonstrates the potential of advanced techniques for building environmental control.
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