Search

Electrochromic glazing can involve substantial energy consumption in the building sector with their optical properties driven either by various occupant needs or environmental conditions. This paper presents comparison of ten different control strategies developed for such a glazing system in terms of specific energy consumption of a PASSYS test cell where the glazing was installed. The control strategies include scheduled ON–OFF controllers, a PID and advanced fuzzy controllers which were developed using experimental data. We used energy simulation to analyse the annual heating, cooling and lighting demands investigating the influence of each control strategy to the thermal behaviour of the building.

The potential of controlling techniques for an electrochromic device is investigated in a systematic way, using both experimental and theoretical tools. Concerning the theoretical part a model was developed in the TRNSYS environment and validated against experimental data. These data were collected from experiments, which were carried out in a PASSYS test cell with a movable wall. Having established a good model performance several cases of window types (such as a 4 mm clear window and a low-e double glazing) and controlling strategies for the electrochromic device are simulated. Obtained results from the series of simulations were compared in terms of the heating and cooling loads of the test cell for each case. It was found that the development of a scheduled control strategy for the electrochromic glazing is better in terms of cooling loads. Finally, a more sophisticated control strategy was examined based on the adaptive fuzzy approach.

This paper presents the development and testing of a multi objective fuzzy controller. It is dedicated to the control of thermal comfort and indoor air quality while minimising energy consumption and reducing instabilities are other constraints for this controller. After a brief introduction on the difficult task of indoor climate control, the test cell and its corresponding model is described in the second section. The model has been developed within TRNSYS. Section 3 describes the architecture of the fuzzy controller applied to these control objectives in the test cell. A hierarchical architecture has finally been selected since it is a very flexible architecture and it results in reducing the total number of fuzzy rules. The rule optimisation technique, based on genetic algorithms is also presented in the third section. Simulation results are finally presented. They prove that, after optimisation, the fuzzy controller greatly outperforms a typical ON-OFF controller on all control criteria. Energy consu...