Performance evaluation of artificial neural network-based variable control logic for double skin enveloped buildings during the heating season

Abstract

This study describes integrated logic for an artificial neural network (ANN) to control heating devices on a continuous basis. Two ANN-based control logic systems and two conventional rule-based logic systems were developed to control a heating device and the openings of a double skin enveloped building. The ANN-based logic controls heating devices on a continuous basis according to the indoor temperature. The rule-based logic controls heating systems and openings at envelopes in two-position on/off operation. Control performance for the developed logic was numerically conducted using computer simulations for a small office space with double skin envelopes during the heating season.

Analysis results indicate that the ANN-based temperature control logic resulted in a more stable temperature near the center of the comfortable range with a reduced opening period of the internal envelope. The reduced number of on/off moments of the heating device and the openings in the ANN-based logic were predicted to save energy and prevent system degradation. The use of ANN-based logic would be effective for maintaining a stable thermal environment and for system operation. Rule-based logic can be effectively used to improve building energy efficiency. In this study, two ANN-based logic types were developed for heating devices controlled on a continuous basis and their performance was compared with those of rule-based on/off logic. Thus, in order to cover the limitation of this study, further study is warranted for examining the clear difference achieved by ANN-based vs. rule-based control, when they are applied to control heating output on a continuous basis.