Thermal control processes by deterministic and networkbased models for energy use and control accuracy in a building space

Abstract

Various control approaches for building thermal controls have been studied to improve the energy use which determines a large part of the spatial thermal quality. This research compares the performance of deterministic models and a network-based model to examine the aspects of both energy consumption and thermal comfort. The single-switch deterministic model immediately responds to indoor thermal conditions, but the network-based model sends better-fit signals derived from learned data reflecting seven different climate conditions. As a result, the network-based model improves the thermal comfort level by about 6.1% to 9.4% and the energy efficiency by about 1.8% to 39.5% as compared to a thermostat and a fuzzy model. In the case of a specific weather condition, it can be confirmed that the process of finding efficient control values based on the network-based learning algorithm is more efficient than the conventional deterministic models.