Journal articles: 'HVAC control systems'

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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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Javed, Umar, Neelam Mughees, Muhammad Jawad, Omar Azeem, Ghulam Abbas, Nasim Ullah, Md Shahariar Chowdhury, Kuaanan Techato, Khurram Shabih Zaidi, and Umair Tahir. "A Systematic Review of Key Challenges in Hybrid HVAC–HVDC Grids." Energies 14, no. 17 (September 1, 2021): 5451. http://dx.doi.org/10.3390/en14175451.

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The concept of hybrid high-voltage alternating current (HVAC) and high-voltage direct current (HVDC) grid systems brings a massive advantage to reduce AC line loading, increased utilization of network infrastructure, and lower operational costs. However, it comes with issues, such as integration challenges, control strategies, optimization control, and security. The combined objectives in hybrid HVAC–HVDC grids are to achieve the fast regulation of DC voltage and frequency, optimal power flow, and stable operation during normal and abnormal conditions. The rise in hybrid HVAC–HVDC grids and associated issues are reviewed in this study along with state-of-the-art literature and developments that focus on modeling robust droop control, load frequency control, and DC voltage regulation techniques. The definitions, characteristics, and classifications of key issues are introduced. The paper summaries the key insights of hybrid HVAC–HVDC grids, current developments, and future research directions and prospects, which have led to the evolution of this field. Therefore, the motivation, novelty, and the main contribution of the survey is to comprehensively analyze the integration challenges, implemented control algorithms, employed optimization algorithms, and major security challenges of hybrid HVAC–HVDC systems. Moreover, future research prospects are identified, such as security algorithms’ constraints, dynamic contingency modeling, and cost-effective and reliable operation.

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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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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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Georgiev, Zdravko. "BENCHMARKING OF HVAC CONTROL SYSTEMS." IFAC Proceedings Volumes 39, no. 19 (2006): 225–30. http://dx.doi.org/10.3182/20061002-4-bg-4905.00038.

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Kim, Sung-Kyung, Won-Hwa Hong, Jung-Ha Hwang, Myung-Sup Jung, and Yong-Seo Park. "Optimal Control Method for HVAC Systems in Offices with a Control Algorithm Based on Thermal Environment." Buildings 10, no. 5 (May 21, 2020): 95. http://dx.doi.org/10.3390/buildings10050095.

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This study examined a method to reduce energy consumption in office buildings. Correspondingly, an optimal control method was proposed for heating, ventilation, and air conditioning (HVAC) systems via two control algorithms that considered the indoor thermal environment. The control algorithms were developed by considering temperature and humidity as the factors of the indoor thermal environment that influence the control of HVAC systems and the predicted mean vote comfort ranges. Furthermore, an experiment was performed using office equipment that incorporated the two control algorithms for HVAC systems, and the correlation between changes in the thermal environment within the office and the occupant’s comfort levels was estimated via an actual survey. The results demonstrated that the proposed control method for HVAC systems, which considered the comfort ranges of temperature and humidity and the thermal adaptation capability, can efficiently maintain the occupant’s comfort with lower energy usage compared with conventional HVAC systems. Thus, the use of the control method contributes to the reduction of total energy consumption in buildings with HVAC systems.

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Øgård, O., H. Brustad, and V. Novakovič. "Simulation and Control of HVAC Systems." IFAC Proceedings Volumes 20, no. 12 (September 1987): 269–74. http://dx.doi.org/10.1016/s1474-6670(17)55642-6.

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Tiğrek, Tûba, Soura Dasgupta, and Theodore F. Smith. "NONLINEAR OPTIMAL CONTROL OF HVAC SYSTEMS." IFAC Proceedings Volumes 35, no. 1 (2002): 149–54. http://dx.doi.org/10.3182/20020721-6-es-1901.01578.

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Anderson, M., M. Buehner, P. Young, D. Hittle, C. Anderson, Jilin Tu, and D. Hodgson. "MIMO Robust Control for HVAC Systems." IEEE Transactions on Control Systems Technology 16, no. 3 (May 2008): 475–83. http://dx.doi.org/10.1109/tcst.2007.903392.

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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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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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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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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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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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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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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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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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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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Basile, M. C., V. Bruni, F. Buccolini, D. De Canditiis, S. Tagliaferri, and D. Vitulano. "Automatic and Noninvasive Indoor Air Quality Control in HVAC Systems." Journal of Industrial Mathematics 2016 (June 30, 2016): 1–11. http://dx.doi.org/10.1155/2016/9674387.

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This paper presents a methodology for assessing and monitoring the cleaning state of a heating, ventilation, and air conditioning (HVAC) system of a building. It consists of a noninvasive method for measuring the amount of dust in the whole ventilation system, that is, the set of filters and air ducts. Specifically, it defines the minimum amount of measurements, their time table, locations, and acquisition conditions. The proposed method promotes early intervention on the system and it guarantees high indoor air quality and proper HVAC working conditions. The effectiveness of the method is proved by some experimental results on different study cases.

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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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Ruano, A., S. Pesteh, S. Silva, H. Duarte, G. Mestre, P. M. Ferreira, H. Khosravani, and R. Horta. "PVM-based intelligent predictive control of HVAC systems." IFAC-PapersOnLine 49, no. 5 (2016): 371–76. http://dx.doi.org/10.1016/j.ifacol.2016.07.141.

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Hadjiski, Mincho, Vassil Sgurev, and Venelina Boishina. "MULTI AGENT INTELLIGENT CONTROL OF CENTRALIZED HVAC SYSTEMS." IFAC Proceedings Volumes 39, no. 19 (2006): 195–200. http://dx.doi.org/10.3182/20061002-4-bg-4905.00033.

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Foulkes, Timothy J. "New developments in noise control for HVAC systems." Journal of the Acoustical Society of America 99, no. 4 (April 1996): 2554–74. http://dx.doi.org/10.1121/1.415187.

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Counsell, John, Obadah Zaher, Joseph Brindley, and Gavin Murphy. "Robust nonlinear HVAC systems control with evolutionary optimisation." Engineering Computations 30, no. 8 (November 11, 2013): 1147–69. http://dx.doi.org/10.1108/ec-04-2012-0079.

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Orosa, José A. "A new modelling methodology to control HVAC systems." Expert Systems with Applications 38, no. 4 (April 2011): 4505–13. http://dx.doi.org/10.1016/j.eswa.2010.09.124.

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Chen, Liangliang, and Ying Zhang. "Accelerated distributed model predictive control for HVAC systems." Control Engineering Practice 110 (May 2021): 104782. http://dx.doi.org/10.1016/j.conengprac.2021.104782.

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House, John M., Kwangduk Douglas Lee, and Leslie K. Norford. "Controls and Diagnostics for Air Distribution Systems." Journal of Solar Energy Engineering 125, no. 3 (August 1, 2003): 310–17. http://dx.doi.org/10.1115/1.1592185.

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Fault detection and diagnostics applied to heating, ventilating, and air-conditioning (HVAC) equipment has been an active area of research over the past decade, with much of the work focusing on air distribution systems. Concurrent efforts have sought to improve the control of these systems. This paper discusses the relationship between controls and diagnostics for air distribution systems, provides an overview of the diagnostic literature for these systems, presents new findings from a diagnostic method that enables operational characteristics of individual HVAC components to be extracted from high-frequency measurements of whole-building power, and outlines research challenges that remain to be addressed.

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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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Dounis. "Special Issue “Intelligent Control in Energy Systems”." Energies 12, no. 15 (August 5, 2019): 3017. http://dx.doi.org/10.3390/en12153017.

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The editor of this special issue on “Intelligent Control in Energy Systems” have made an attempt to publish a book containing original technical articles addressing various elements of intelligent control in energy systems. The response to our call had 60 submissions, of which 27 were published submissions and 33 were rejections. This book contains 27 technical articles and one editorial. All have been written by authors from 15 countries (China, Netherlands, Spain, Tunisia, United States of America, Korea, Brazil, Egypt, Denmark, Indonesia, Oman, Canada, Algeria, Mexico, and Czech Republic), which elaborated several aspects of intelligent control in energy systems. It covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural network for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision tree for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust μ-synthesis for microgrid, and neuro-fuzzy systems in energy storage.

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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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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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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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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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Singh, G., M. Zaheer-uddin, and R. V. Patel. "Adaptive control of multivariable thermal processes in HVAC systems." Energy Conversion and Management 41, no. 15 (October 2000): 1671–85. http://dx.doi.org/10.1016/s0196-8904(99)00182-x.

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Al-Assadi, S. A. K., R. V. Patel, M. Zaheer-uddin, M. S. Verma, and J. Breitinger. "Robust decentralized control of HVAC systems using -performance measures." Journal of the Franklin Institute 341, no. 7 (November 2004): 543–67. http://dx.doi.org/10.1016/j.jfranklin.2004.06.001.

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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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Seo, Byeongmo, Yeo Beom Yoon, Jung Hyun Mun, and Soolyeon Cho. "Application of Artificial Neural Network for the Optimum Control of HVAC Systems in Double-Skinned Office Buildings." Energies 12, no. 24 (December 13, 2019): 4754. http://dx.doi.org/10.3390/en12244754.

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Double Skin Façade (DSF) systems have become an alternative to the environmental and energy savings issues. DSF offers thermal buffer areas that can provide benefits to the conditioned spaces in the form of improved comforts and energy savings. There are many studies conducted to resolve issues about the heat captured inside DSF. Various window control strategies and algorithms were introduced to minimize the heat gain of DSF in summer. However, the thermal condition of the DSF causes a time lag between the response time of the Heating, Ventilation, and Air-Conditioning (HVAC) system and cooling loads of zones. This results in more cooling energy supply or sometimes less than required, making the conditioned zones either too cold or warm. It is necessary to operate the HVAC system in consideration of all conditions, i.e., DSF internal conditions and indoor environment, as well as proper DSF window controls. This paper proposes an optimal air supply control for a DSF office building located in a hot and humid climate. An Artificial Neural Network (ANN)-based control was developed and tested for its effectiveness. Results show a 10.5% cooling energy reduction from the DSF building compared to the non-DSF building with the same HVAC control. Additionally, 4.5% more savings were observed when using the ANN-based control.

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Petri, Ioan, Omer Rana, Yacine Rezgui, and Fodil Fadli. "Edge HVAC Analytics." Energies 14, no. 17 (September 2, 2021): 5464. http://dx.doi.org/10.3390/en14175464.

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Integrating data analytics, optimisation and dynamic control to support energy services has seen significant interest in recent years. Larger appliances used in an industry context are now provided with Internet of Things (IoT)-based interfaces that can be remotely monitored, with some also provided with actuation interfaces. The combined use of IoT and edge computing enables connectivity between energy systems and infrastructure, providing the means to implement both energy efficiency/optimisation and cost reduction strategies. We investigate the economic implications of harnessing IoT and edge/cloud technologies to support energy management for HVAC (Heating, Ventilation and Air Conditioning) systems in buildings. In particular, we evaluate the cost savings for building operations through energy optimisation. We use a real use case for energy optimisation as identified in the EU “Sporte2” project (focusing on the energy optimisation of sports facilities) and explore several scenarios in relation to costs and energy optimisation.

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Rezanejadzanjani, Behdad, and Paul G. O’Brien. "EVALUATION OF SMART BOOSTER FANS AND DAMPERS FOR ADVANCED HVAC SYSTEMS." Journal of Green Building 16, no. 2 (March 1, 2021): 115–27. http://dx.doi.org/10.3992/jgb.16.2.115.

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ABSTRACT There is potential to significantly reduce CO2 emissions by increasing the efficiency and reducing the duty cycle of HVAC systems by using smart booster fans and dampers. Smart booster fans fit in the vents within a home, operating quietly on low power (2W) to augment HVAC systems and improve their performance. In this study, a prototype duct system is used to measure and evaluate the ability for smart booster fans and dampers to control airflow to different vents for the purpose of increasing the efficiency of HVAC systems. Four case studies were evaluated: an HVAC system (1) without any fans or dampers, (2) with a fan installed in one vent, but without any dampers, (3) with dampers installed at the vents, but without any fans, and (4) with both fan and dampers installed. The results from both the experimental and numerical evaluation show that the smart booster fan and dampers can significantly improve the airflow at a vent that is underperforming. For example, the airflow at the last vent in a ducting branch was increased from 17 to 37 CFM when a smart booster fan was installed at this vent. Results from the numerical analysis show that for the case of an underperforming vent during the winter season the HVAC running time may be reduced from 24 hr/day to 5.6 hr/day. Furthermore, results from the numerical analysis show the HVAC running time is further reduced to 4.5 hr/day for cases 3 and 4.

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Dong, Jin, Christopher Winstead, James Nutaro, and Teja Kuruganti. "Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings." Energies 11, no. 9 (September 13, 2018): 2427. http://dx.doi.org/10.3390/en11092427.

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This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure HVAC is not run needlessly when when a room/zone is unoccupied. In this paper, we propose simple yet effective algorithms to predict occupancy alongside an algorithm for automatically assigning temperature set-points. Utilizing past occupancy observations, we introduce three different techniques for occupancy prediction. Firstly, we propose an identification-based approach, which identifies the model via Expectation Maximization (EM) algorithm. Secondly, we study a novel finite state automata (FSA) which can be reconstructed by a general systems problem solver (GSPS). Thirdly, we introduce an alternative stochastic model based on uncertain basis functions. The results show that all the proposed occupancy prediction techniques could achieve around 70% accuracy. Then, we have proposed a scheme to adaptively adjust the temperature set-points according to a novel temperature set algorithm with customers’ different discomfort tolerance indexes. By cooperating with the temperature set algorithm, our occupancy-based HVAC control shows 20% energy saving while still maintaining building comfort requirements.

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Mirinejad, Hossein, Seyed Hossein Sadati, Maryam Ghasemian, and Hamid Torab. "Control Techniques in Heating, Ventilating and Air Conditioning (HVAC) Systems." Journal of Computer Science 4, no. 9 (September 1, 2008): 777–83. http://dx.doi.org/10.3844/jcssp.2008.777.783.

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Afroz, Zakia, GM Shafiullah, Tania Urmee, and Gary Higgins. "Modeling techniques used in building HVAC control systems: A review." Renewable and Sustainable Energy Reviews 83 (March 2018): 64–84. http://dx.doi.org/10.1016/j.rser.2017.10.044.

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Salsbury, T. I., and R. C. Diamond. "Fault detection in HVAC systems using model-based feedforward control." Energy and Buildings 33, no. 4 (April 2001): 403–15. http://dx.doi.org/10.1016/s0378-7788(00)00122-5.

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Blad, C., S. Koch, S. Ganeswarathas, C. S. Kallesøe, and S. Bøgh. "Control of HVAC-systems with Slow Thermodynamic Using Reinforcement Learning." Procedia Manufacturing 38 (2019): 1308–15. http://dx.doi.org/10.1016/j.promfg.2020.01.159.

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Ioan, URSU, NASTASE Ilinca, CALUIANU Sorin, IFTENE Andreea, and TOADER Adrian. "Intelligent control of HVAC systems. Part I: Modeling and synthesis." INCAS BULLETIN 5, no. 1 (March 11, 2013): 103–18. http://dx.doi.org/10.13111/2066-8201.2013.5.1.11.

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Andreea, IFTENE, URSU Ioan, NASTASE Ilinca, CALUIANU Sorin, TECUCEANU George, and TOADER Adrian. "Intelligent control of HVAC systems. Part II: perceptron performance analysis." INCAS BULLETIN 5, no. 3 (September 6, 2013): 127–35. http://dx.doi.org/10.13111/2066-8201.2013.5.3.13.

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Schwingshackl, Daniel, Jakob Rehrl, Martin Horn, Julian Belz, and Oliver Nelles. "Model extension for model based MIMO control in HVAC systems." Journal of Building Engineering 11 (May 2017): 224–29. http://dx.doi.org/10.1016/j.jobe.2017.04.015.

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Garnier, Antoine, Julien Eynard, Matthieu Caussanel, and Stéphane Grieu. "Predictive Control of Multizone HVAC Systems in Non-residential Buildings." IFAC Proceedings Volumes 47, no. 3 (2014): 12080–85. http://dx.doi.org/10.3182/20140824-6-za-1003.01826.

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Price, Christopher, and Bryan P. Rasmussen. "Optimal tuning of cascaded control architectures for nonlinear HVAC systems." Science and Technology for the Built Environment 23, no. 8 (March 7, 2017): 1190–202. http://dx.doi.org/10.1080/23744731.2016.1262663.

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Journal articles on the topic 'HVAC control systems'

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