Abstract
Advanced thermal control technologies have been continuously developed to complement conventional models and algorithms to improve their performance regarding control accuracy and energy efficiency. This study analyses the strengths and weaknesses of simultaneous controls for the amount of air and its temperature by use of on-demand and predictive control strategies responding to two different outdoor conditions. The framework performs the comparative analyses of an on-demand model, which reacts immediately to indoor conditions, and a predictive model, which provides reference signals derived from data learned. Two models are combined to make a comparison of how much more efficient the combined model operates than each model when abnormal situations occur. As a result, when the two models are combined, its efficiency improves from 20.0% to 33.6% for indoor thermal dissatisfaction and from 13.0% to 44.5% for energy use, respectively. This result implies that in addition to creating new algorithms to cope with any abnormal situation, combining existing models can also be a resource-saving approach.
Highlights
Advanced thermal control technologies have been continuously developed to complement conventional models and algorithms to improve their performance regarding control accuracy and energy efficiency
In order to improve the performance of building energy supply methods, the efficiency of the Heating, Ventilating, and Air-Conditioning (HVAC) systems commonly has been investigated by use of controlling fuel amount into boilers, fan motor speed, and distribution networks as control targets
It was confirmed that the combined model could lower about 13.0% of Energy Use Intensity (EUI)
Summary
Advanced thermal control technologies have been continuously developed to complement conventional models and algorithms to improve their performance regarding control accuracy and energy efficiency. For indoor thermal dissatisfaction and from 13.0% to 44.5% for energy use, respectively This result implies that in addition to creating new algorithms to cope with any abnormal situation, combining existing models can be a resource-saving approach. Complementing the control strategies and tuning rules have helped to upgrade the inner algorithms of Proportional–Integral–Derivative (PID) control systems such as optimization of fuel amount or speed of turbines. In such models, the focus on the analyses was placed on reducing absolute errors by analyzing the differences between control volume and actual demand.
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