Heating, Ventilation, And Air Conditioning Control Research Articles

Grey prediction on indoor comfort temperature for HVAC systems

This paper describes determination of indoor comfort temperature for efficiently Heating, Ventilating, and Air-Conditioning (HVAC) system under dynamical environment. Making occupants’ satisfaction on thermal comfort is still challenging by how the temperature setpoint of the fresh made-up air in conventional HVAC systems can be adapted properly (normally fixed) when surroundings changes in time. Essentially, being unknown ahead of time, the outdoor temperature is systematically predicted by grey prediction model in this work. The Adaptive Comfort Theory (ACT) model captures relation of the indoor comfort temperature to the outdoor temperature based on the survey data on thermal comfort in real occupants’ living environment. With the grey prediction model and the ACT model, the predicted indoor comfort temperature can be implemented as the comfort temperature reference for HVAC control systems. The experiment results show the viability of the proposed methodology for efficient HVAC control system.

The effects of service delays on a BACnet-based HVAC control system

Building automation and control network (BACnet) is a standard data communication protocol for building automation and control systems. This study investigated the relationship between network-induced delay and control performance of a BACnet-based heating, ventilating, and air-conditioning (HVAC) control system using a simulation. The model consisted of the HVAC control system model and a BACnet communication protocol model. The BACnet communication model delivered the HVAC control data using the communication services provided by BACnet. The simulation results were analysed to develop recommendations for designing and operating BACnet-based HVAC control systems.

Evaluating performance indices of a shopping centre and implementing HVAC control principles to minimize energy usage

Heating, ventilating and air-conditioning (HVAC) systems in buildings must be integrated with an efficient control scheme to maintain comfort under any load conditions. Efficient HVAC control is often the most cost-effective option to improve the energy efficiency of a building. However, HVAC processes are non-linear, and characteristics change on a seasonal basis so the effect of changing the control strategy is usually difficult to predict. The present study aims to reduce energy consumption by defining new HVAC control strategies and tuning control loops in a shopping centre. First, an energy audit was performed to investigate the potential for energy savings and to redefine the control scenarios, while a methodology for the shopping centre was developed. Performance indices were then calculated and compared with the yardsticks. Next, normalised performance indices were computed to reach out a better understanding of the building’s efficiency. Finally, new strategies were implemented with the help of the existing building management system (BMS) and about 22% of energy saving was achieved.

HVAC control strategies to enhance comfort and minimise energy usage

Good heating, ventilating and air conditioning (HVAC) control ensures comfort. It is usually also the most cost-effective way to improve energy efficiency of air-conditioned buildings. In this article, the comfort enhancement and energy saving potential with new control strategies are determined for the Human Science Building (HSB) at the University of Pretoria. A new software tool, QUICKcontrol, was used to perform the complex and fully integrated building, HVAC and control simulations. Various control strategies were investigated. These included air-bypass, reset control, setback control, improved start–stop times, economiser control and CO 2 control. The simulation models were firstly verified against measurements to ensure accurate and realistic retrofit simulations. It was then possible to ensure comfort and to predict savings of 60% in HVAC power consumption. This resulted in a simple payback period of 9 months. Preparing input data took about 2 days, while setting up the simulation model took another day. The typical run time for the fully integrated building, HVAC system and control simulation took approximately 90 s per day on an Intel™ Pentium 133 MHz personal computer.

Indoor cooling/heating load analysis based on coupled simulation of convection, radiation and HVAC control

A computational fluid dynamics (CFD) simulation for analyzing indoor cooling/heating load is presented in this study. It is coupled with a radiative heat transfer simulation and heating, ventilating, and air-conditioning (HVAC) controlling system in a room. This new method feeds back the outputs of the HVAC system control to the input boundary conditions of the CFD, and this method includes a human model to evaluate the thermal environment. It would be used to analyze the heating/cooling loads of different HVAC systems under the condition of the same human thermal sensation (e.g. PMV, operative temperature, etc.) even though the temperature and air-velocity distribution in the room are different from each other. To examine the performance of the new method, a cooling load and a thermal environment within a semi-enclosed space, which opens into an atrium space, is analyzed under the steady-state conditions during the summer season. This method is able to analyze the indoor cooling load with changes of target thermal environments of a room and/or changing clothing conditions of occupants considering the temperature and air-velocity distribution in the room. In this paper, two types of HVAC system are compared; i.e. radiation-panel system and all-air cooling system. The radiation-panel cooling system is found to be more energy efficient for cooling the semi-enclosed space. Changes of the level of thermal environment reduce cooling load effectively in case of the all-air cooling system while the radiation-panel system does not reduce cooling load even though the targeted thermal condition is relaxed. Energy saving effect is expected by easing the clothing conditions of occupants. In this study, the reducing effect of cooling load is quantitatively evaluated with clothing conditions also.

Coupled simulation of convicton, radiation, and HVAC control for attaining a given PMV value

A new computational fluid dynamics (CFD) simulation for designing indoor climates is presented in this study. It is coupled with a radiative heat transfer simulation and heating, ventilating, and air-conditioning (HVAC) control system in a room. This new method can feed back the outputs of the CFD to the input conditions for controlling the HVAC system, and includes a human model to evaluate the thermal environment. It can be used to analyze the conditions of the HVAC system (e.g. temperature of supply air, surface temperature of radiation panel, etc.) and the heating/cooling loads of different HVAC systems under the condition of the same human thermal sensation (e.g. PMV, operative temperature, etc.) To examine the performance of the new method, a thermal environment within a semi-enclosed space which opens into an atrium space is analyzed under steady-state conditions in the summer season. Using this method, the most energy efficient HVAC system can be chosen under the same PMV value. In this paper, two types of HVAC system are compared: one is a radiation-panel system and the other is an all-air cooling system. The radiation-panel cooling is found to be more energy efficient for cooling the semi-enclosed space in this study.

A practical algorithm for diagnosing control loop problems

This paper describes an algorithm for detecting and diagnosing poor performance in feedback control loops. The performance of the control loop is assessed based on criteria derived from parameters that can be estimated from superficial knowledge of the controlled process. The algorithm can distinguish between three types of control problems: oscillatory response, sluggish response, and plant failure. The method is particularly applicable to heating, ventilating, and air-conditioning (HVAC) systems where linear (PI) control law is normally used despite the existence of often quite non-linear operating characteristics. In these systems, the control performance varies as the plant is exercised across its operating range. In practice, periodical re-tuning of HVAC control loops during the yearly load cycle may be necessary in order to obtain consistent control performance. The algorithm presented in this paper can act as the basis for determining when re-tuning is necessary in addition to detecting plant failure. Potential advantages of detecting control problems in HVAC systems are energy savings, reduced maintenance costs, and improved thermal comfort conditions. The paper describes the techniques comprising the algorithm and presents results from tests carried out on a simulated air-handling unit (AHU).

Thermal and behavioral modeling of occupant-controlled heating, ventilating and air conditioning systems

Occupant-controlled heating, ventilating and air conditioning (HVAC) systems allow inhabitants of open-plan spaces some degree of control over their immediate microclimate. Typically, cooled air is supplied at floor or desktop levels. The amount and direction of air flow is under occupant control. Productivity increases have been attributed to this form of control. This paper proposes a simplified model of the thermal environment created by an occupant-controlled HVAC system and the behavior of the occupants within it. The thermal environment is characterized by individual nodes representing sub-areas of the conditioned space and a single well-mixed ceiling space above the occupied zone. Random processes are used to simulate the comings and goings of individual occupants and their HVAC control behavior. The model is used to identify the parameters which have the largest influence on the energy efficiency. Energy use of task HVAC with occupant sensors is found to be less than that of a conventional HVAC system by 13%. Individual HVAC control requires about 10% more energy than uniform temperature conditions.

Artificial Intelligence Approach to Energy Management and Control in the HVAC Process: An Evaluation, Development and Discussion

AbstractComputer control technology in heating, ventilating and air conditioning (HVAC) systems has evolved in two technical phases: central control and direct digital control (DDC) with central monitoring function. Many control strategies have been developed for energy conservation in HVAC energy management and control systems (EMCS) and these are reported in the literature. An artificial intelligence approach to the EMCS in HVAC process is proposed in this paper. Two real time expert systems for energy‐saving operation and indoor setting are developed and integrated with an adaptive control strategy. Further, a configuration of an integrated intelligent system for intelligent building management is proposed, in which building management functions are coordinated by a meta‐system. The applications of artificial intelligence (AI) techniques in a HVAC process give a new avenue for the development of EMCS in the HVAC industry.