In the literature, black box is more common due in large part to issues related to thermodynamic modeling. The grey box approach depends on the existence of physical knowledge, while the black box method requires no previous knowledge. Modeling HVAC systems and components mathematically has been demonstrated in the literature to be a viable approach for designing controls and detecting faults.Įarlier research in the field reveals modeling strategies that fall into two distinct categories: grey box and black box. Due to the potential these systems have for future energy needs, this paper proposes identifying advanced novel HVAC system models that employ intelligent control algorithms to produce energy savings without sacrificing comfort levels. Intelligently controlled HVAC systems have been shown to reduce energy consumption by up to 30% or higher . Recent research indicates that intelligent control might be a viable method of achieving optimal comfort levels at high energy efficiency. However, there is a rising demand for costs to remain reasonable but efficiency to be high without sacrificing comfort levels. Typically, an HVAC system requires more energy per building than any other system, given optimal comfort in home and work environments. As the housing and business needs of the developed world generally include buildings that require HVAC systems, the percentage contribution of the total energy consumption of these buildings has increased from 20% to 40% in Western countries.
The HVAC approach to heating and cooling has become much more complicated, with the latest HVAC components using control algorithms, sensing technology, and artificial intelligence .Įnergy saving is a key feature of HVAC systems and is increasing in importance. Heating, ventilation, and air conditioning (HVAC) systems are installed in millions of commercial and noncommercial buildings as a means to provide the desired thermal comfort standards at an affordable cost and with minimal maintenance requirements. The novel system is a redesign of an FLC using MATLAB/Simulink, with the results showing an enhancement in thermal comfort levels. In this paper, modulating equal percentage globe valves, fans speed, and dampers position have been modeled according to exact flow rates of hot water and air into the building, and a new approach to adapting FLC through the modification of fuzzy rules surface is presented. These types of controllers work through the manipulation of dampers, fans, and valves to adjust flow rates of water and air. The latest generation of fuzzy logic controllers (FLC) is algorithm-based and is used to control indoor temperatures, CO 2 concentrations in air handling units (AHUs), and fan speeds. Most of these systems use nonlinear time variances to handle disturbances, along with controllers that try to balance rise times and stability. Proper functioning of heating, ventilation, and air conditioning (HVAC) systems is important for efficient thermal management, as well as operational costs.
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