adaptation to user preferences, and an automatic control with adaptation to user
preferences, has shown that the energy saving due to the use of a smart control algorithm
when compared to a manual control, is preserved by the user-adaptive control algorithm
but with much less rejection rate and with an improved indoor comfort. (See more details
in the section below.)
2. Algorithm description - Rule base and models
The control algorithms already elaborated by our laboratory through several other
projects are using artificial neural networks and fuzzy logic, which are convenient for
describing the control rules and the various models (building, building services) to be
considered. The first part of the project was devoted to the elaboration of adaptive rules,
which would be adapted to the individual user preferences by the way of Genetic
Algorithms.
2.1 Basic Principles
Integrating all the different controllers in one unique system would have been very
difficult and inefficient if there were no underlying principles. This section describes the
basic principles used for the whole control system. It also explains how some additional
physical data are prepared.
2.1.1 Integration Aspects
Three different device categories are considered for the control: the heating/cooling
system, the blinds (shading devices) and the electric lighting. Ventilation was not taken
into account since the LESO building (in which the experiments have been undertaken)
has no mechanical ventilation system installed. Nevertheless, the chosen controller
architecture allows implementing easily additional control devices4. The integrated
system is built on the principle of three nested control loop levels (see Figure 2.1).
Figure 2.1: Principle block diagram of the three nested control loop levels
1
Level 1 performs the translation from physical values (heating power, blind
position, etc.) into electrical signals for field actuators (to modify the heating
system valve position, to raise or lower the blind, etc.). In our case, we use an
Summary :
adaptation to user preferences, and an automatic control with adaptation to user preferences, has shown that the energy saving due to the use of a smart control algorithm when compared to a manual control, is preserved by the user-adaptive control algorithm but with much less rejection rate and with an improved indoor comfort.
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