Journal articles: 'HVAC control system'

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Hussain, Abadal Salam T., F. Malek, S. Faiz Ahmed, Taha A. Taha, Shouket A. Ahmed, Mardianaliza Othman, Muhammad Irwanto Misrun, Gomesh Nair Shasidharan, and Mohd Irwan Yusoff. "Operational Optimization of High Voltage Power Station Based Fuzzy Logic Intelligent Controller." Applied Mechanics and Materials 793 (September 2015): 100–104. http://dx.doi.org/10.4028/www.scientific.net/amm.793.100.

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This paper discusses the use of the intelligent microcontroller and also discusses the results from the simulation application of fuzzy logic theory to the control of the high voltage direct and alternation current (HVDC)& (HVAC) power station systems. The application considered their implementation in both low and high level control systems in HVDC& HVAC power station systems. The results for the fuzzy logic based controller shows many improvements compared to the conventional HVDC& HVAC control system. The fuzzy logic based controller concept was further successfully extended to high level control of optimization problems such as the power swings. Based on simulation results, HVDC and HVAC breaker design are online protection against unwanted incidents happening to the system.

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Narayan, R. S., S. Mohan, and K. Sunitha. "Simulative Study into the Development of a Hybrid HVDC System Through a Comparative Research with HVAC: a Futuristic Approach." Engineering, Technology & Applied Science Research 7, no. 3 (June 12, 2017): 1600–1604. http://dx.doi.org/10.48084/etasr.1192.

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High Voltage Direct Current Transmission (HVDC) is considered a better solution for bulk long distance transmissions. The increased use of HVDC is a result of its advantages over the HVAC systems and especially of its fault stability nature. A better solution is proposed by using a Voltage Source Controlled–HVDC as one of the infeed for the Multi-Infeed HVDC (MIDC or MI-HVDC) systems. The main advantage with the VSC converter is its flexible power control which enhances the stability of the MIDC systems. In this paper, the behavior of an HVDC system is compared with that of an HVAC during faults. A Hybrid HVDC system that includes a LCC as a rectifier unit and a VSC converter as the inverter is being proposed. It is considered suitable for MIDC systems and particularly for supplying a weak AC system. The performance of the system during steady state and transient conditions for all the proposed topologies including HVDC, HVAC and Hybrid HVDC are studied in MATLAB/SIMULINK. All of the proposed control strategies are evaluated via a series of simulation case studies.

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Lin, Chang-Ming, Hsin-Yu Liu, Ko-Ying Tseng, and Sheng-Fuu Lin. "Heating, Ventilation, and Air Conditioning System Optimization Control Strategy Involving Fan Coil Unit Temperature Control." Applied Sciences 9, no. 11 (June 11, 2019): 2391. http://dx.doi.org/10.3390/app9112391.

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The objective of this study was to develop a heating, ventilation, and air conditioning (HVAC) system optimization control strategy involving fan coil unit (FCU) temperature control for energy conservation in chilled water systems to enhance the operating efficiency of HVAC systems. The proposed control strategy involves three techniques, which are described as follows. The first technique is an algorithm for dynamic FCU temperature setting, which enables the FCU temperature to be set in accordance with changes in the outdoor temperature to satisfy the indoor thermal comfort for occupants. The second technique is an approach for determining the indoor cold air demand, which collects the set FCU temperature and converts it to the refrigeration ton required for the chilled water system; this serves as the control target for ensuring optimal HVAC operation. The third technique is a genetic algorithm for calculating the minimum energy consumption for an HVAC system. The genetic algorithm determines the pump operating frequency associated with minimum energy consumption per refrigeration ton to control energy conservation. To demonstrate the effectiveness of the proposed HVAC system optimization control strategy combining FCU temperature control, this study conducted a field experiment. The results revealed that the proposed strategy enabled an HVAC system to achieve 39.71% energy conservation compared with an HVAC system operating at full load.

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Goel, Supriya, Michael Rosenberg, Juan Gonzalez, and Jérémy Lerond. "Total System Performance Ratio—A Systems Based Approach for Evaluating HVAC System Efficiency." Energies 14, no. 16 (August 19, 2021): 5108. http://dx.doi.org/10.3390/en14165108.

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The prescriptive path is the most widely used approach for commercial code compliance in the United States. Though easy to implement, prescriptive approaches do not typically discriminate between minimally compliant, high-performing and poorly performing HVAC system configurations. Hence, to meet aggressive energy and carbon reduction goals, it is clear that energy codes will need to transition from prescriptive to performance-based approaches, a transition that is riddled with several challenges. This paper discusses a new HVAC system-based performance approach (HVAC System Performance) which provides a simpler solution to HVAV system evaluation compared to whole building performance, while keeping tradeoffs limited to specific building systems. The Total System Performance Ratio (TSPR) is a metric for evaluation of overall system efficiency instead of individual component efficiency, a solution which could also eventually facilitate the transition to a 100% performance-based code structure. TSPR is a ratio that compares the annual heating and cooling load of a building to the annual energy consumed by the building’s HVAC system. A calculation software tool has been developed for determining a building’s TSPR. Already incorporated into the 2018 Washington State Energy Code, this approach is also being evaluated by ASHRAE Standard 90.l Project Committee and has the potential to provide a comprehensive performance-based approach for HVAC system evaluation and analysis.

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Bidadfar, Ali, Oscar Saborío-Romano, Jayachandra Naidu Sakamuri, Vladislav Akhmatov, Nicolaos Antonio Cutululis, and Poul Ejnar Sørensen. "Coordinated Control of HVDC and HVAC Power Transmission Systems Integrating a Large Offshore Wind Farm." Energies 12, no. 18 (September 6, 2019): 3435. http://dx.doi.org/10.3390/en12183435.

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The development of efficient and reliable offshore electrical transmission infrastructure is a key factor in the proliferation of offshore wind farms (OWFs). Traditionally, high-voltage AC (HVAC) transmission has been used for OWFs. Recently, voltage-source-converter-based (VSC-based) high-voltage DC (VSC-HVDC) transmission technologies have also been considered due to their grid-forming capabilities. Diode-rectifier-based (DR-based) HVDC (DR-HVDC) transmission is also getting attention due to its increased reliability and reduced offshore platform footprint. Parallel operation of transmission systems using such technologies can be expected in the near future as new OWFs are planned in the vicinity of existing ones, with connections to more than one onshore AC system. This work addresses the control and parallel operation of three transmission links: VSC-HVDC, DR-HVDC, and HVAC, connecting a large OWF (cluster) to three different onshore AC systems. The HVAC link forms the offshore AC grid, while the diode rectifier and the wind farm are synchronized to this grid voltage. The offshore HVDC converter can operate in grid-following or grid-forming mode, depending on the requirement. The contributions of this paper are threefold. (1) Novel DR- and VSC-HVDC control methods are proposed for the parallel operation of the three transmission systems. (2) An effective control method for the offshore converter of VSC-HVDC is proposed such that it can effectively operate as either a grid-following or a grid-forming converter. (3) A novel phase-locked loop (PLL) control for VSC-HVDC is proposed for the easy transition from the grid-following to the grid-forming converter in case the HVAC link trips. Dynamic simulations in PSCAD validate the ability of the proposed controllers to ride through faults and transition between grid-following and grid-forming operation.

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T.P.So, Albert, W. L. Chan, T. T. Chow, and W. L. Tse. "New HVAC control by system identification." Building and Environment 30, no. 3 (July 1995): 349–57. http://dx.doi.org/10.1016/0360-1323(94)00063-x.

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Swaminathan, Siva, Ximan Wang, Bingyu Zhou, and Simone Baldi. "A University Building Test Case for Occupancy-Based Building Automation." Energies 11, no. 11 (November 14, 2018): 3145. http://dx.doi.org/10.3390/en11113145.

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Heating, ventilation and air-conditioning (HVAC) units in buildings form a system-of-subsystems entity that must be accurately integrated and controlled by the building automation system to ensure the occupants’ comfort with reduced energy consumption. As control of HVACs involves a standardized hierarchy of high-level set-point control and low-level Proportional-Integral-Derivative (PID) controls, there is a need for overcoming current control fragmentation without disrupting the standard hierarchy. In this work, we propose a model-based approach to achieve these goals. In particular: the set-point control is based on a predictive HVAC thermal model, and aims at optimizing thermal comfort with reduced energy consumption; the standard low-level PID controllers are auto-tuned based on simulations of the HVAC thermal model, and aims at good tracking of the set points. One benefit of such control structure is that the PID dynamics are included in the predictive optimization: in this way, we are able to account for tracking transients, which are particularly useful if the HVAC is switched on and off depending on occupancy patterns. Experimental and simulation validation via a three-room test case at the Delft University of Technology shows the potential for a high degree of comfort while also reducing energy consumption.

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Conceição, Eusébio, António Sousa, João Gomes, and António Ruano. "HVAC Systems Applied in University Buildings with Control Based on PMV and aPMV Indexes." Inventions 4, no. 1 (January 15, 2019): 3. http://dx.doi.org/10.3390/inventions4010003.

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In this work, HVAC (Heating, Ventilation and Air Conditioning) systems applied in university buildings with control based on PMV (Predicted Mean Vote) and aPMV (adaptive Predicted Mean Vote) indexes are discussed. The building’s thermal behavior with complex topology, in transient thermal conditions, for summer and winter conditions is simulated by software. The university building is divided into 124 spaces, on two levels with an area of 5931 m2, and is composed of 201 transparent surfaces and 1740 opaque surfaces. There are 86 compartments equipped with HVAC systems. The simulation considers the actual occupation and ventilation cycles, the external environmental variables, the internal HVAC system and the occupants’ and building’s characteristics. In this work, a new HVAC control system, designed to simultaneously obtain better occupants’ thermal comfort levels according to category C of ISO 7730 with less energy consumption, is presented. This new HVAC system with aPMV index control is numerically implemented, and its performance is compared with the performance of the same HVAC system with the usual PMV index control. Both HVAC control systems turn on only when the PMV index or the aPMV index reaches values below −0.7, in winter conditions, and when the PMV index or the aPMV index reaches values above +0.7, in summer conditions. In accordance with the results obtained, the HVAC system guarantees negative PMV and aPMV indexes in winter conditions and positive PMV and aPMV indexes in summer conditions. The energy consumption level is higher in winter conditions than in summer conditions for compartments with shading, and it is lower in winter conditions than in summer conditions for compartments exposed to direct solar radiation. The consumption level is higher using the PMV control than with the aPMV control. Air temperature, in accordance with Portuguese standards, is higher than 20 °C in winter conditions and lower than 27 °C in summer conditions. In Mediterranean climates, the HVAC systems with aPMV control provide better occupants’ thermal comfort levels and less energy consumption than the HVAC system with PMV control.

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Toub, Mohamed, Chethan R. Reddy, Rush D. Robinett, and Mahdi Shahbakhti. "Integration and Optimal Control of MicroCSP with Building HVAC Systems: Review and Future Directions." Energies 14, no. 3 (January 30, 2021): 730. http://dx.doi.org/10.3390/en14030730.

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Heating, ventilation, and air-conditioning (HVAC) systems are omnipresent in modern buildings and are responsible for a considerable share of consumed energy and the electricity bill in buildings. On the other hand, solar energy is abundant and could be used to support the building HVAC system through cogeneration of electricity and heat. Micro-scale concentrated solar power (MicroCSP) is a propitious solution for such applications that can be integrated into the building HVAC system to optimally provide both electricity and heat, on-demand via application of optimal control techniques. The use of thermal energy storage (TES) in MicroCSP adds dispatching capabilities to the MicroCSP energy production that will assist in optimal energy management in buildings. This work presents a review of the existing contributions on the combination of MicroCSP and HVAC systems in buildings and how it compares to other thermal-assisted HVAC applications. Different topologies and architectures for the integration of MicroCSP and building HVAC systems are proposed, and the components of standard MicroCSP systems with their control-oriented models are explained. Furthermore, this paper details the different control strategies to optimally manage the energy flow, both electrical and thermal, from the solar field to the building HVAC system to minimize energy consumption and/or operational cost.

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Xue, Guiyuan, Chen Wu, Wenjuan Niu, Xun Dou, Shizhen Wang, and Yadie Fu. "Flexible Control Strategy for Intelligent Building Air Conditioning System." E3S Web of Conferences 252 (2021): 01039. http://dx.doi.org/10.1051/e3sconf/202125201039.

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An improved optimization adjustment strategy for building heating ventilation and air conditioning (Heating Ventilation and Air Conditioning, HVAC) is proposed. The energy consumption model of building heating/refrigeration is established by using the instantaneous energy balance of heat, and then the optimal operation strategy of building HVAC energy based on weather forecast data is constructed in the range of user temperature comfort. Finally, the MATLAB and TRNSYS simulation techniques are used to verify the example. Simulation results show that the optimal operation strategy of building HVAC energy based on weather forecast data can not only significantly reduce the cost of energy use, but also effectively improve the absorption capacity of renewable energy on the building side.

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Zhao, Jing, and Yu Shan. "A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System." Energies 13, no. 3 (January 21, 2020): 530. http://dx.doi.org/10.3390/en13030530.

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The energy consumption of air-conditioning systems is a major part of energy consumption in buildings. Optimal control strategies have been increasingly developed in building heating, ventilation, and air-conditioning (HVAC) systems. In this paper, a load forecast fuzzy (LFF) control strategy was proposed. The predictive load based on the SVM method was used as the input parameter of the fuzzy controller to perform feedforward fuzzy control on the HVAC system. This control method was considered as an effective way to reduce energy consumption while ensuring indoor comfort, which can solve the problem of hysteresis and inaccuracy in building HVAC systems by controlling the HVAC system in advance. The case study was conducted on a ground source heat pump system in Tianjin University to validate the proposed control strategy. In addition, the advantages of the LFF control strategy were verified by comparing with two feedback control strategies, which are the supply water temperature (SWT) control strategy and the room temperature fuzzy (RTF) control strategy. Results show that the proposed LFF control strategy is capable not only to ensure the minimum indoor temperature fluctuations but also decrease the total energy consumption.

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Ettouil, Radhia, Karim Chabir, Dominique Sauter, and Mohamed Naceur Abdelkrim. "Synergetic Control for HVAC System Control and VAV Box Fault Compensation." International Journal of Applied Mathematics and Computer Science 29, no. 3 (September 1, 2019): 555–70. http://dx.doi.org/10.2478/amcs-2019-0041.

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Abstract Synergetic control is proposed for heating, ventilating and air-conditioning (HVAC) system control. The synergetic controller is developed using the nonlinear model of the HVAC system. Occupancy information in each zone is required in the design of the controller which offers inherent comfort according to the occupancy in the zone. The stability of the building system using the proposed control is verified through the Lyapunov approach. It is also proved that the synergetic controller is robust to external disturbances. Then, synergetic theories are used to design a reconfigurable control for damper stuck failures in variable air volume (VAV) to recover the nominal performance. Simulations are provided to validate the effectiveness of the proposed controller for a three-zone building.

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Li, Jiaming, Geoff Poulton, Glenn Platt, Josh Wall, and Geoff James. "Dynamic zone modelling for HVAC system control." International Journal of Modelling, Identification and Control 9, no. 1/2 (2010): 5. http://dx.doi.org/10.1504/ijmic.2010.032354.

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Razmara, M., M. Maasoumy, M. Shahbakhti, and R. D. Robinett. "Optimal exergy control of building HVAC system." Applied Energy 156 (October 2015): 555–65. http://dx.doi.org/10.1016/j.apenergy.2015.07.051.

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Sun, Jian Min, and Chun Dong Zhang. "Development and Analysis on Energy Conservation Equipment and Control Technology of HVAC." Advanced Materials Research 424-425 (January 2012): 852–56. http://dx.doi.org/10.4028/www.scientific.net/amr.424-425.852.

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In building, the energy consumption of heating, ventilation and air conditioning (HVAC) is the largest, which accounts for forty to sixty percent of the total building consumption. So it is a key research to reduce the energy consumption of the HVAC system for saving building energy. This article describes a variety of energy conservation equipment of HVAC, and describes in detail the principles of each type of equipment. This article also analyzes the growing advanced control technologies for the HVAC system. In conclusion, HVAC equipment is developing in the direction of clean energy and energy efficient; intelligent control technology is more applicable to the varying parameters of complex system such as air conditioning, is more energy conservation and will become the leading direction of research and application

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Adegbenro, Akinkunmi, Michael Short, and Claudio Angione. "An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications." Energies 14, no. 8 (April 8, 2021): 2078. http://dx.doi.org/10.3390/en14082078.

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Heating, ventilating, and air-conditioning (HVAC) systems account for a large percentage of energy consumption in buildings. Implementation of efficient optimisation and control mechanisms has been identified as one crucial way to help reduce and shift HVAC systems’ energy consumption to both save economic costs and foster improved integration with renewables. This has led to the development of various control techniques, some of which have produced promising results. However, very few of these control mechanisms have fully considered important factors such as electricity time of use (TOU) price information, occupant thermal comfort, computational complexity, and nonlinear HVAC dynamics to design a demand response schema. In this paper, a novel two-stage integrated approach for such is proposed and evaluated. A model predictive control (MPC)-based optimiser for supervisory setpoint control is integrated with a digital parameter-adaptive controller for use in a demand response/demand management environment. The optimiser is designed to shift the heating load (and hence electrical load) to off-peak periods by minimising a trade-off between thermal comfort and electricity costs, generating a setpoint trajectory for the inner loop HVAC tracking controller. The tracking controller provides HVAC model information to the outer loop for calibration purposes. By way of calibrated simulations, it was found that significant energy saving and cost reduction could be achieved in comparison to a traditional on/off or variable HVAC control system with a fixed setpoint temperature.

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Namatēvs, Ivars. "Deep Reinforcement Learning on HVAC Control." Information Technology and Management Science 21 (December 14, 2018): 29–36. http://dx.doi.org/10.7250/itms-2018-0004.

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Due to increase of computing power and innovative approaches of an end-to-end reinforcement learning (RL) that feed data from high-dimensional sensory inputs, it is now plausible to combine RL and Deep learning to perform Smart Building Energy Control (SBEC) systems. Deep reinforcement learning (DRL) revolutionizes existing Q-learning algorithm to Deep Q-learning (DQL) profited by artificial neural networks. Deep Neural Network (DNN) is well trained to calculate the Q-function. To create comprehensive SBEC system it is crucial to choose appropriate mathematical background and benchmark the best framework of a model based predictive control to manage the building heating, ventilation, and air condition (HVAC) system. The main contribution of this paper is to explore a state-of-the-art DRL methodology to smart building control.

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Usoro, P. B., I. C. Schick, and S. Negahdaripour. "An Innovation-Based Methodology for HVAC System Fault Detection." Journal of Dynamic Systems, Measurement, and Control 107, no. 4 (December 1, 1985): 284–89. http://dx.doi.org/10.1115/1.3140737.

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Although Energy Management and Control Systems (EMCS) have since the early 1970’s contributed significantly to the reduction (20-40 percent) of energy use in buildings without sacrificing occupants’ comfort, their full capabilities have not been completely realized. This is in part due to their inability to quickly detect and compensate for failures in the Heating, Ventilation and Air Conditioning (HVAC) system. In fact, no matter how good the control scheme for the HVAC system might be, the presence of undetected faults can completely offset any expected savings. This paper presents a methodology for detecting faults in an HVAC system using a nonlinear mathematical model and an extended Kalman filter. The technique was implemented in a computer program and successfully used to detect “planted” faults in simulations of the air handler unit of an HVAC system. Test results are presented to demonstrate the effectiveness of the methodology.

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Cvok, Ivan, Igor Ratković, and Joško Deur. "Multi-Objective Optimisation-Based Design of an Electric Vehicle Cabin Heating Control System for Improved Thermal Comfort and Driving Range." Energies 14, no. 4 (February 23, 2021): 1203. http://dx.doi.org/10.3390/en14041203.

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Modern electric vehicle heating, ventilation, and air-conditioning (HVAC) systems operate in more efficient heat pump mode, thus, improving the driving range under cold ambient conditions. Coupling those HVAC systems with novel heating technologies such as infrared heating panels (IRP) results in a complex system with multiple actuators, which needs to be optimally coordinated to maximise the efficiency and comfort. The paper presents a multi-objective genetic algorithm-based control input allocation method, which relies on a multi-physical HVAC model and a CFD-evaluated cabin airflow distribution model implemented in Dymola. The considered control inputs include the cabin inlet air temperature, blower and radiator fan air mass flows, secondary coolant loop pump speeds, and IRP control settings. The optimisation objective is to minimise total electric power consumption and thermal comfort described by predictive mean vote (PMV) index. Optimisation results indicate that HVAC and IRP controls are effectively decoupled, and that a significant reduction of power consumption (typically from 20% to 30%) can be achieved using IRPs while maintaining the same level of thermal comfort. The previously proposed hierarchical HVAC control strategy is parameterised and extended with a PMV-based controller acting via IRP control inputs. The performance is verified through simulations in a heat-up scenario, and the power consumption reduction potential is analysed for different cabin air temperature setpoints.

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Wang, Shiqiang, Jianchun Xing, Ziyan Jiang, and Juelong Li. "Decentralized Optimization for a Novel Control Structure of HVAC System." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/9402538.

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A decentralized control structure is introduced into the heating, ventilation, and air conditioning (HVAC) system to solve the high maintenance and labor cost problem in actual engineering. Based on this new control system, a decentralized optimization method is presented for sensor fault repair and optimal group control of HVAC equipment. Convergence property of the novel method is theoretically analyzed considering both convex and nonconvex systems with constraints. In this decentralized control system, traditional device is fitted with a control chip such that it becomes a smart device. The smart device can communicate and operate collaboratively with the other devices to accomplish some designated tasks. The effectiveness of the presented method is verified by simulations and hardware tests.

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Son, Junseo, and Hyogon Kim. "Sensorless Air Flow Control in an HVAC System through Deep Learning." Applied Sciences 9, no. 16 (August 11, 2019): 3293. http://dx.doi.org/10.3390/app9163293.

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Sensor-based intelligence is essential in future smart buildings, but the benefits of increasing the number of sensors come at a cost. First, purchasing the sensors themselves can incur non-negligible costs. Second, since the sensors need to be physically connected and integrated into the heating, ventilation, and air conditioning (HVAC) system, the complexity and the operating cost of the system are increased. Third, sensors require maintenance at additional costs. Therefore, we need to pursue the appropriate technology (AT) in terms of the number of sensors used. In the ideal scenario, we can remove excessive sensors and yet achieve the intelligence that is required to operate the HVAC system. In this paper, we propose a method to replace the static pressure sensor that is essential for the operation of the HVAC system through the deep neural network (DNN).

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Nie, Zelin, Feng Gao, and Chao-Bo Yan. "A Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency." Energies 14, no. 2 (January 12, 2021): 400. http://dx.doi.org/10.3390/en14020400.

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Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

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Chung, Samuel W., and Jeong Je Jo. "Pressure Control of HVAC System for Corona Virus." European Journal of Engineering Research and Science 5, no. 4 (April 24, 2020): 462–68. http://dx.doi.org/10.24018/ejers.2020.5.4.1872.

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Corona Virus is deadly spread out to thousands of healthy people and so strong as to infect to the surrounding people after people. We must keep the deadly virus indoor as much as we can. How? We must not allow the indoor air already contaminated to leak out to the atmosphere, which will transmit to other people. The new virus is so strong and so fast to transmit, it will spread within a few seconds to thousands of people and became patients immediately. To solve the problem, we must build a pressure vessel to keep the virus inside the vessel and not to leak outside. It should be a negative pressure compare to the atmospheric pressure. Another words, is to keep the indoor pressure lower than atmospheric pressure, so the contaminated indoor pressure is lower than outdoor pressure. It is a part of a traditional HVAC operation. This article shows step by step procedures of HVAC system design procedures, with emphasis of the air duct system design. Care must be taken to keep close attention to the maintenance of the system.

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Cheng, Chin-Chi, and Dasheng Lee. "Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis." Sensors 19, no. 5 (March 6, 2019): 1131. http://dx.doi.org/10.3390/s19051131.

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In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research.

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Kardos, Tamás, and Dénes Nimród Kutasi. "Model-based Predictive Control of an HVAC System." Műszaki Tudományos Közlemények 11, no. 1 (October 1, 2019): 101–4. http://dx.doi.org/10.33894/mtk-2019.11.21.

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Abstract This paper presents the application of two model-based predictive control (MPC) algorithms on the cooling system of an office building. The two strategies discussed are a simple MPC, and an adaptive MPC algorithm connected to a model predictor. The cooling method used represents the air-conditioning unit of an HVAC system. The temperature of the building’s three rooms is controlled with fan coil units, based on the reference temperature and with different constraints applied. Furthermore, the building model is affected by dynamically changing interior and exterior heat sources, which we introduced into the controller as disturbances.

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Choudhary, Shivam, and Pranav Balachander. "Smart HVAC System Control using RF and Zigbees." International Journal of Computer Applications 68, no. 24 (April 18, 2013): 25–31. http://dx.doi.org/10.5120/11728-7378.

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Zhang Huaguang and Lilong Cai. "Decentralized nonlinear adaptive control of an HVAC system." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 32, no. 4 (November 2002): 493–98. http://dx.doi.org/10.1109/tsmcc.2002.807271.

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Liu, Zhe, Fangting Song, Ziyan Jiang, Xi Chen, and Xiaohong Guan. "Optimization based integrated control of building HVAC system." Building Simulation 7, no. 4 (January 8, 2014): 375–87. http://dx.doi.org/10.1007/s12273-014-0161-z.

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Ning, M., and M. Zaheeruddin. "Neural Network Model-Based Adaptive Control of a VAV-HVAC&R System." International Journal of Air-Conditioning and Refrigeration 27, no. 01 (March 2019): 1950006. http://dx.doi.org/10.1142/s2010132519500068.

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A dynamic system model of a two-zone variable air volume heating, ventilation and air conditioning and refrigeration (VAV-HVAC&R) system is considered. The system model consists of two environmental zones, an HVAC system and a water-cooled vapor compression chiller. Five adaptive controllers were designed to achieve good tracking control of set points of zone air temperatures, discharge air temperature, chilled water supply temperature and static pressure of the VAV-HVAC&R system. The PI controller gains were updated online using adaptive neural networks and an auto-tuning algorithm. Simulation results show that adaptive PI control gave faster response and less overshoot compared to conventional constant gain PI control. The control responses tracked set-points closely and remained stable over a typical day simulation of building operation under variable load conditions.

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Olama, Mohammed, Teja Kuruganti, James Nutaro, and Jin Dong. "Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid." Energies 11, no. 7 (July 16, 2018): 1852. http://dx.doi.org/10.3390/en11071852.

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Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a variable air volume HVAC system (continuously variable HVAC load), which is available in large commercial buildings. The second strategy is rule-based control acting on an aggregate of on/off HVAC systems, which are available in residential buildings in addition to many small to medium size commercial buildings. Hardware constraints that include limiting the switching between the different states for on/off HVAC units to maintain their lifetimes are considered. Simulations illustrate that the proposed control strategies provide frequency regulation to the grid, without affecting the indoor climate significantly.

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Ye, Zhijing, Fei Hu, Lin Zhang, Zhe Chu, and Zheng O'Neill. "A Low-Cost Experimental Testbed for Energy-Saving HVAC Control Based on Human Behavior Monitoring." International Journal of Cyber-Physical Systems 2, no. 1 (January 2020): 33–55. http://dx.doi.org/10.4018/ijcps.2020010103.

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Heating, ventilation, and cooling (HVAC) is the largest source of residential energy consumption. Occupancy sensors' data can be used for HVAC control since they indicate the number of people in the building. HVAC/sensor interactions show the essential features of a typical cyber-physical system (CPS). However, there are communication protocol incompatibility issues in the CPS interface between the sensors and the building HVAC server. Through either wired or wireless communication links, the server always needs to understand the communication schedule to receive occupant values from sensors. This paper proposes two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control. The interaction scheme between sensors and HVAC server will be discussed. The authors have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies. The first emulator demonstrates the residential building's energy control by using sensors and Raspberry pi boards to emulate the functions/responses of a static thermostat. In this case, room HVAC temperature settings could be changed in real-time with a high resolution based on the collected sensor data. The second emulator is built to show the energy control in commercial building by transmitting the sensor data and control signals via BACnet in HVAC system. Both emulators discussed above are portable (i.e., all hardware units can be easily taken to a new place) and have extremely low cost. This research tests the whole system with YABE (Yet Another BACnet Explorer) and WebCTRL.

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Han, Jinmog, Jongkyun Bae, Jihoon Jang, Jumi Baek, and Seung-Bok Leigh. "The Derivation of Cooling Set-Point Temperature in an HVAC System, Considering Mean Radiant Temperature." Sustainability 11, no. 19 (September 30, 2019): 5417. http://dx.doi.org/10.3390/su11195417.

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Heating, ventilation, and air-conditioning (HVAC) systems usually have a set-point temperature control feature that uses the indoor dry-bulb temperature to control the indoor environment. However, an incorrect set-point temperature can reduce thermal comfort and result in unnecessary energy consumption. This study focuses on a derivation method for the optimal cooling set-point temperature of an HVAC system used in office buildings, considering the thermal characteristics and daily changes in the weather conditions, to establish a comfortable indoor environment and minimize unnecessary energy consumption. The operative temperature is used in the HVAC system control, and the mean radiant temperature is predicted with 94% accuracy through a multiple regression analysis by applying the indoor thermal environment data and weather information. The regression equation was utilized to create an additional equation to calculate the optimal set-point temperature. The simulation results indicate that the HVAC system control with the new set-point temperatures calculated from the derived equation improves thermal comfort by 38.5% (26%p). This study confirmed that a cooling set-point temperature that considers both the thermal characteristics of a building and weather conditions is effective in enhancing the indoor thermal comfort during summer.

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Federspiel, Clifford C., and Haruhiko Asada. "User-Adaptable Comfort Control for HVAC Systems." Journal of Dynamic Systems, Measurement, and Control 116, no. 3 (September 1, 1994): 474–86. http://dx.doi.org/10.1115/1.2899242.

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This paper describes a new approach to the control of heating, ventilating, and air-conditioning (HVAC) systems. The fundamental concept of the new approach is that the controller learns to predict the actual thermal sensation of the specific occupant by tuning parameters of a model of the occupant’s thermal sensation. The parameters are adjusted with respect to thermal sensation ratings acquired from the specific occupant and measurements of physical variables that affect thermal sensation so that with time the model accurately reflects the thermal sensation of the specific occupant. From a lumped-parameter model of a singleroom enclosure, it is shown that the stability of the nominal system can be maintained by utilizing a priori information about the parameters of the thermal sensation model. The method is implemented on a ductless, split-system heat pump. Experiments using human subjects verify the feasibility of the method.

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Imal, Muharrem. "Design and Implementation of Energy Efficiency in HVAC Systems Based on Robust PID Control for Industrial Applications." Journal of Sensors 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/954159.

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Energy efficiency in heating, ventilating, and air-conditioning (HVAC) systems is a primary concern in process projects, since the energy consumption has the highest percentage in HVAC for all processes. Without sacrifice of thermal comfort, to reset the suitable operating parameters, such as the humidity and air temperature, would have energy saving with immediate effect. In this paper, the simulation-optimization approach described the effective energy efficiency for HVAC systems which are used in industrial process. Due to the complex relationship of the HVAC system parameters, it is necessary to suggest optimum settings for different operations in response to the dynamic cooling loads and changing weather conditions during a year. Proportional-integral-derivative (PID) programming was developed which can effectively handle the discrete, nonlinear and highly constrained optimization problems. Energy efficiency process has been made by controlling of alternative current (AC) drivers for ventilation and exhaust fans, according to supplied air flow capacity and differential air pressure between supplied and exhaust air. Supervisory controller software was developed by using programmable controllers and human machine interface (HMI) units. The new designed HVAC control system would have a saving potential of about 40% as compared to the existing operational settings, without any extra cost.

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Cvok, Ivan, Igor Ratković, and Joško Deur. "Optimisation of Control Input Allocation Maps for Electric Vehicle Heat Pump-based Cabin Heating Systems." Energies 13, no. 19 (October 2, 2020): 5131. http://dx.doi.org/10.3390/en13195131.

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The heating, ventilation and air conditioning (HVAC) system negatively affects the electric vehicle (EV) driving range, especially under cold ambient conditions. Modern HVAC systems based on the vapour-compression cycle can be rearranged to operate in the heat pump mode to improve the overall system efficiency compared to conventional electrical/resistive heaters. Since such an HVAC system is typically equipped with multiple actuators (compressor, pumps, fans, valves), with the majority of them being controlled in open loop, an optimisation-based control input allocation is necessary to achieve the highest efficiency. This paper presents a genetic algorithm optimisation-based HVAC control input allocation method, which utilises a multi-physical HVAC system model implemented in Dymola/Modelica. The considered control inputs include the cabin inlet air temperature reference, blower and radiator fan air mass flows and secondary coolant loop pumps’ speeds. The optimal allocation is subject to specified, target cabin air temperatures and heating power. Additional constraints include actuator hardware limits and safety functions, such as maintaining the superheat temperature at its reference level. The optimisation objective is to maximise the system efficiency defined by the coefficient of performance (COP). The optimised allocation maps are fitted by proper mathematical functions to facilitate the control strategy implementation and calibration. The overall control strategy consists of superimposed cabin air temperature controller that commands heating power, control input allocation functions, and low-level controllers that ensure cabin inlet air and superheat temperature regulation. The control system performance is verified through Dymola simulations for the heat pump mode in a heat-up scenario. Control input allocation map optimisation results are presented for air-conditioning (A/C) mode, as well.

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Carli, Raffaele, Graziana Cavone, Sarah Ben Othman, and Mariagrazia Dotoli. "IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings." Sensors 20, no. 3 (January 31, 2020): 781. http://dx.doi.org/10.3390/s20030781.

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The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach.

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Dehestani, Davood, Jafar Madadnia, Homa Koosha, and Fahimeh Eftekhari. "Comprehensive Analysis for Air Supply Fan Faults Based on HVAC Mathematical Model." Advanced Materials Research 452-453 (January 2012): 460–68. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.460.

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Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems

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Reed, Thomas, and William Brader. "Availability Analyses of HVAC Systems In Cleanrooms and Other Critical Applications." Journal of the IEST 32, no. 3 (May 1, 1989): 25–30. http://dx.doi.org/10.17764/jiet.1.32.3.1853ml1r9jp4447t.

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The operational and functional criteria of heating, ventilation, and air conditioning (HVAC) systems and associated control systems for cleanrooms and other similar critical applications impose very demanding requirements on both the installed systems and the system designers. Designers and facility operators often compromise original reliability criteria due to rule-of-thumb decision making processes. These processes do not usually include a truly determinant method of evaluating, in a quantitative manner, the advantages and disadvantages of alternative HVAC and control systems with respect to redundancy, reliability, and availability. A methodology as proposed here should help to enhance the decision making processes associated with defining availability criteria and predicting the effects of alternative HVAC and control systems on critical facility operations.

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Ivanova, Donka, Nikolay Valov, and Martin Deyanov. "Application of the genetic algorithm for cascade control of a HVAC system." MATEC Web of Conferences 292 (2019): 01064. http://dx.doi.org/10.1051/matecconf/201929201064.

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In this article the application of genetic algorithm for tuning of HVAC cascade system is proposed. The tuning procedure for a cascade system is very time-consuming and practice shows that additional controller tuning is needed when classical method is used. The main problem in classical method is the interconnection between the parameters of the two controllers. The proposed optimal tuning procedure overcomes the disadvantages. It is based on the following criteria: minimum integral square error, minimum settling time and minimum overshoot. The best process quality is achieved with PI controller in the inner loop and a PID controller in the outer loop of the cascade HVAC system. The proposed method for simultaneous tuning of controller parameters in a cascade control system can be applied in different control systems.

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Ding, Junwei, Chuck Wah Yu, and Shi-Jie Cao. "HVAC systems for environmental control to minimize the COVID-19 infection." Indoor and Built Environment 29, no. 9 (October 21, 2020): 1195–201. http://dx.doi.org/10.1177/1420326x20951968.

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The outbreak of pneumonia caused by 2019 Novel Coronavirus arises significant concern for virus transmission and control. The control of the indoor environment or public-enclosed environment is crucial to reduce the risk of infection. Heating, ventilation, air-conditioning (HVAC) systems are used to create a healthy, thermal-comfort indoor environments. Thus, the rational use of HVAC systems is of great importance for the environmental control to reduce infection risk and to improve human wellbeing in the pandemic. In order to satisfy the requirement of better healthy environment and more thermal comfort performance of indoor ventilation system, prevention of indoor pollution is essential, especially considering the purpose of disease transmission resistance. This paper investigated the collective contagion events in enclosed spaces as well as engineering control against virus spread with ventilation systems for health-care facilities and public vehicles. Future challenges of HVAC design and control were discussed.

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Chen, Yimin, Guanjing Lin, Eliot Crowe, and Jessica Granderson. "Development of a Unified Taxonomy for HVAC System Faults." Energies 14, no. 17 (September 6, 2021): 5581. http://dx.doi.org/10.3390/en14175581.

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Detecting and diagnosing HVAC faults is critical for maintaining building operation performance, reducing energy waste, and ensuring indoor comfort. An increasing deployment of commercial fault detection and diagnostics (FDD) software tools in commercial buildings in the past decade has significantly increased buildings’ operational reliability and reduced energy consumption. A massive amount of data has been generated by the FDD software tools. However, efficiently utilizing FDD data for ‘big data’ analytics, algorithm improvement, and other data-driven applications is challenging because the format and naming conventions of those data are very customized, unstructured, and hard to interpret. This paper presents the development of a unified taxonomy for HVAC faults. A taxonomy is an orderly classification of HVAC faults according to their characteristics and causal relations. The taxonomy includes fault categorization, physical hierarchy, fault library, relation model, and naming/tagging scheme. The taxonomy employs both a physical hierarchy of HVAC equipment and a cause-effect relationship model to reveal the root causes of faults in HVAC systems. A structured and standardized vocabulary library is developed to increase data representability and interpretability. The developed fault taxonomy can be used for HVAC system ‘big data’ analytics such as HVAC system fault prevalence analysis or the development of an HVAC FDD software standard. A common type of HVAC equipment-packaged rooftop unit (RTU) is used as an example to demonstrate the application of the developed fault taxonomy. Two RTU FDD software tools are used to show that after mapping FDD data according to the taxonomy, the meta-analysis of the multiple FDD reports is possible and efficient.

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Huang, Chun-E., Chunwang Li, and Xiaojun Ma. "Active-Disturbance-Rejection-Control for Temperature Control of the HVAC System." Intelligent Control and Automation 09, no. 01 (2018): 1–9. http://dx.doi.org/10.4236/ica.2018.91001.

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Hong, Goopyo, Chul Kim, and Jun Hong. "Energy Conservation Potential of Economizer Controls Using Optimal Outdoor Air Fraction Based on Field Study." Energies 13, no. 19 (September 24, 2020): 5038. http://dx.doi.org/10.3390/en13195038.

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In commercial buildings, HVAC systems are becoming a primary driver of energy consumption, which already account for 45% of the total building energy consumption. In the previous literature, researchers have studied several energy conservation measures to reduce HVAC system energy consumption. One of the effective ways is an economizer in air-handling units. Therefore, this study quantified the impact of the outdoor air fraction by economizer control type in cooling system loads based on actual air-handling unit operation data in a hospital. The optimal outdoor air fraction and energy performance for economizer control types were calculated and analyzed. The result showed that economizer controls using optimal outdoor air fraction were up to 45% more efficient in cooling loads than existing HVAC operations in the hospital. The energy savings potential was 6–14% of the differential dry-bulb temperature control, 17–27% of the differential enthalpy control, 8–17% of the differential dry-bulb temperature and high-limit differential enthalpy control, and 16–27% of the differential enthalpy and high-limit differential dry-bulb temperature control compared to the no economizer control. The result of this study will contribute to providing a better understanding of economizer controls in the hospital when the building operates in hot-humid climate regions.

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Fasolo, Paul S., and Dale E. Seborg. "Monitoring and Fault Detection for an HVAC Control System." IFAC Proceedings Volumes 27, no. 2 (May 1994): 535–40. http://dx.doi.org/10.1016/s1474-6670(17)48205-x.

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Kardos, Tamás, and Dénes Nimród Kutasi. "Modelling and Model-Based Control of an HVAC System." Műszaki Tudományos Közlemények 10, no. 1 (April 1, 2019): 25–30. http://dx.doi.org/10.33894/mtk-2019.10.03.

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Abstract An HVAC system contains heating, ventilation and air conditioning equipment used in office or industrial buildings. The goal of this research is to design a controller for the process of cooling an office building that is made up of three rooms. The desired room temperature can be achieved by controlling the fans making up the fan coil units and the cooling medium’s temperature. By these means the building connected to the electrical grid becomes a smart office. The used building model includes several dynamically changing interior and exterior heat sources affecting the inner climate, which introduces a level of uncertain prediction into the system. We have determined the controller’s performance by the rate of deviation from the expected temperature, the consumed electrical energy and the generated noise. The controller was created in Matlab Simulink with the possibility of migration to a Siemens PLC.

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Dhar, Narendra Kumar, Nishchal Kumar Verma, and Laxmidhar Behera. "Adaptive Critic-Based Event-Triggered Control for HVAC System." IEEE Transactions on Industrial Informatics 14, no. 1 (January 2018): 178–88. http://dx.doi.org/10.1109/tii.2017.2725899.

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Bengea, Sorin C., Pengfei Li, Soumik Sarkar, Sergey Vichik, Veronica Adetola, Keunmo Kang, Teems Lovett, Francesco Leonardi, and Anthony D. Kelman. "Fault-tolerant optimal control of a building HVAC system." Science and Technology for the Built Environment 21, no. 6 (July 6, 2015): 734–51. http://dx.doi.org/10.1080/23744731.2015.1057085.

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Aswani, Anil, Neal Master, Jay Taneja, Andrew Krioukov, David Culler, and Claire Tomlin. "Energy-Efficient Building HVAC Control Using Hybrid System LBMPC." IFAC Proceedings Volumes 45, no. 17 (2012): 496–501. http://dx.doi.org/10.3182/20120823-5-nl-3013.00069.

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Fasolo, Paul, and Dale Seborg. "Monitoring and Fault Detection for an HVAC Control System." HVAC&R Research 1, no. 3 (July 1, 1995): 177–93. http://dx.doi.org/10.1080/10789669.1995.10391318.

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Tashtoush, Bourhan, M. Molhim, and M. Al-Rousan. "Dynamic model of an HVAC system for control analysis." Energy 30, no. 10 (July 2005): 1729–45. http://dx.doi.org/10.1016/j.energy.2004.10.004.

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Journal articles on the topic 'HVAC control system'

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