Building Space Thermal Control Model Responding to Sharp Changes in Outdoor Temperature

Abstract

As computing and data-driven technologies have improved, the precision of the building thermal control models has been gradually improved, but the use of energy resources to operate them has been also increased. It is imperative to investigate the optimized point of energy use and human comfort for their thermal control strategies. The aim of this research is to find an energy-efficient thermal control model to maintain the constancy of thermal comfort and suppress the increase of energy use in association with precise environmental controls. Based on a cooling and heating air supply model in a simplified building model, a comprehensive energy use pattern is confirmed by adding an adaptive control model that allows indoor thermal comfort to be maintained at a setting level. The adaptive control model utilizing the artificial neural network and the adjustment process of initial settings is proposed to examine its performance in controlling the amount of thermal supply air and its temperature. For the clear comparison between a baseline model and a proposed model, the statistical indices of each thermal dissatisfaction value and the weekly heating energy use are utilized. The results of this research show that the thermal dissatisfaction fluctuation is alleviated by about 22.0~41.0% and the energy efficiency is improved by about 5.1%, respectively. The results provide the effectiveness of the proposed model which can improve both the energy use and thermal comfort in a building space. This advantage can help old thermal systems to improve their usability without replacing any major components.

An Adaptive Control Model for Thermal Environmental Factors to Supplement the Sustainability of a Small-Sized Factory

“…For the internal structure, as a conventional rule, a large class of several structures consisted of one ANN system. Conditional selecting functions of non-linear mapping are required to define a specific network process [34][35][36]. The ANN algorithm function consisted of two layers for input values, a single layer for an output value, and, between the input and output layers, 10 hidden layers.…”