Academic literature on the topic 'Optimization of HVAC energy consumption'
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Journal articles on the topic "Optimization of HVAC energy consumption"
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Jung, Dae Kyo, Dong Hwan Lee, Joo Ho Shin, Byung Hun Song, and Seung Hee Park. "Optimization of Energy Consumption Using BIM-Based Building Energy Performance Analysis." Applied Mechanics and Materials 281 (January 2013): 649–52. http://dx.doi.org/10.4028/www.scientific.net/amm.281.649.
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Recently, the interest in increasing energy efficiency of building energy management system (BEMS) has become a high-priority and thus the related studies also increased. In particular, since the energy consumption in terms of heating and cooling system takes a large portion of the energy consumed in buildings, it is strongly required to enhance the energy efficiency through intelligent operation and/or management of HVAC (Heating, Ventilation and Air Conditioning) system. To tackle this issue, this study deals with the BIM (Building Information Modeling)-based energy performance analysis implemented in Energyplus. The BIM model constructed at Revit is updated at Design Builder, adding HVAC models and converted compatibly with the Energyplus environment. And then, the HVAC models are modified throughout the comparison between the energy consumption patterns and the real-time monitoring in-field data. In order to maximize the building energy performance, a genetic algorithm (GA)-based optimization technique is applied to the modified HVAC models. Throughout the proposed building energy simulation, finally, the best optimized HVAC control schedule for the target building can be obtained in the form of “supply air temperature schedule”.
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Kusiak, Andrew, Mingyang Li, and Fan Tang. "Modeling and optimization of HVAC energy consumption." Applied Energy 87, no. 10 (October 2010): 3092–102. http://dx.doi.org/10.1016/j.apenergy.2010.04.008.
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Bhatt, Dhowmya, Danalakshmi D, A. Hariharasudan, Marcin Lis, and Marlena Grabowska. "Forecasting of Energy Demands for Smart Home Applications." Energies 14, no. 4 (February 17, 2021): 1045. http://dx.doi.org/10.3390/en14041045.
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The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss, price management and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and in high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed system is considered more effective than other methods. The results of the Li-Fi communication protocols show improved results compared to the other communication protocols.
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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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Liu, Zhonghui, and Gongyi Jiang. "Optimization of intelligent heating ventilation air conditioning system in urban building based on BIM and artificial intelligence technology." Computer Science and Information Systems, no. 00 (2021): 27. http://dx.doi.org/10.2298/csis200901027l.
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The study aims to effectively reduce building energy consumption, improve the utilization efficiency of building resources, reduce the emission of pollutants and greenhouse gases, and protect the ecological environment. A prediction model of heating ventilation air conditioning (HVAC) energy consumption is established by using back propagation neural network (BPNN) and adapted boosting (Adaboost) algorithm. Then, the HVAC system is optimized by building information modeling (BIM). Finally, the effectiveness of the urban intelligent HVAC optimization prediction model based on BIM and artificial intelligence (AI) is further verified by simulation experiments. The research shows that the error of the prediction model is reduced, the accuracy is higher after the Adaboost algorithm is added to BPNN, and the average prediction accuracy is 86%. When the BIM is combined with the prediction model, the HVAC programme of hybrid cooling beam + variable air volume reheating is taken as the optimal programme of HVAC system. The power consumption and gas consumption of the programme are the least, and the CO2 emission is also the lowest. Programme 1 is compared with programme 3, and the cost is saved by 37% and 15%, respectively. Through the combination of BIM technology and AI technology, the energy consumption of HVAC is effectively reduced, and the resource utilization rate is significantly improved, which can provide theoretical basis for the research of energy-saving equipment.
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Rassadin, Yury, and Nikita Shushko. "Data Driven PMV-Comfort and Energy Consumption Control in Common Buildings." Journal of Physics: Conference Series 2701, no. 1 (February 1, 2024): 012148. http://dx.doi.org/10.1088/1742-6596/2701/1/012148.
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Abstract HVAC systems are essential in the energy management of commercial buildings. The main goal for HVAC system is to improve productivity of the inhabitants by providing comfortable indoor environment. The most common tool for evaluating comfort is PMV index, non-linear combination of indoor environment state variables. The Paper considers optimization of energy consumption of the building by combining state space expansion and microgrid approach.
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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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Corten, Kai, Eric Willems, Shalika Walker, and Wim Zeiler. "Energy performance optimization of buildings using data mining techniques." E3S Web of Conferences 111 (2019): 05016. http://dx.doi.org/10.1051/e3sconf/201911105016.
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The operational energy consumption of buildings often does not match with the predicted results from the design. One of the most dominant causes for these so-called energy performance gaps is the poor operational practice of the heating, ventilation and air conditioning (HVAC) systems. To improve underperforming HVAC systems, analysis of operational data collected by the building management system (BMS) can provide valuable information. In order to completely use and interpret operational data, the building sector is urging for methods and tools. Data mining (DM) is identified as an emerging powerful technique with great potential for discovering hidden knowledge in large data sets. In this study, the performance of HVAC systems was analysed using regression analysis as DM technique. This leads to valuable insights to control and improve the building energy performance. The results show that a reduction of 7-13% on the heating demand and 41-70% on the cooling demand can be obtained.
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Bazenkov, N., and I. Petrov. "Detailed Analysis of Energy Consumption for an Office Building." Journal of Physics: Conference Series 2701, no. 1 (February 1, 2024): 012145. http://dx.doi.org/10.1088/1742-6596/2701/1/012145.
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Abstract Modern buildings consume a significant share of global power production. The primary sources of the power consumption are heating, ventilation and air conditioning (HVAC), light, cooking equipment, technical equipment like elevators, office facilities. Optimization of the buildings power consumption requires real-life data both for strategic planing and operational control. Here we analyze detailed power consumption of a research institute campus which is a medium-sized office buildings. Our data contain detailed electric measurements of 100 three-phase power lines which power all facilities inside the building. Each line is associated with several power consumers like lights, HVAC and office equipment. We analyze weekly and monthly trends, typical patterns in power consumption, and provide rough analysis of what classes of power consumers are most active in specific periods of time.
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Wisdom Ebirim, Kehinde Andrew Olu-lawal, Nwakamma Ninduwezuor-Ehiobu, Danny Jose Portillo Montero, Favour Oluwadamilare Usman, and Emmanuel Chigozie Ani. "LEVERAGING PROJECT MANAGEMENT TOOLS FOR ENERGY EFFICIENCY IN HVAC OPERATIONS: A PATH TO CLIMATE RESILIENCE." Engineering Science & Technology Journal 5, no. 3 (March 10, 2024): 653–61. http://dx.doi.org/10.51594/estj.v5i3.863.
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Efficient management of heating, ventilation, and air conditioning (HVAC) systems is paramount for mitigating energy consumption and enhancing climate resilience in modern infrastructure. This review explores the significance of leveraging project management tools to optimize energy efficiency within HVAC operations, thus fostering a pathway towards climate resilience. The integration of project management methodologies offers a systematic approach to address challenges associated with energy consumption in HVAC systems, thereby facilitating informed decision-making processes. Firstly, the review delves into the pressing need for energy conservation in HVAC operations amidst escalating concerns over climate change and its adverse impacts. The imperative to minimize energy consumption in buildings, particularly through HVAC systems, is highlighted as a pivotal step towards achieving climate resilience and sustainability goals. Subsequently, the review outlines the role of project management tools in orchestrating effective strategies to enhance energy efficiency within HVAC operations. By employing techniques such as project scheduling, risk management, and resource allocation, project managers can streamline the implementation of energy-efficient measures, thereby optimizing HVAC system performance and reducing carbon emissions. Moreover, the review underscores the importance of data analytics and technological advancements in augmenting the efficacy of project management tools for energy efficiency in HVAC operations. Leveraging real-time data monitoring, predictive analytics, and IoT-enabled devices enables proactive maintenance and continuous optimization of HVAC systems, thereby maximizing energy savings and bolstering climate resilience. Furthermore, the review elucidates the potential benefits and challenges associated with the adoption of project management tools for energy efficiency in HVAC operations. While improved cost-effectiveness, environmental sustainability, and operational performance emerge as primary benefits, challenges such as initial investment costs, technological complexities, and organizational inertia necessitate careful consideration. This review advocates for the integration of project management tools as a viable approach to foster energy efficiency in HVAC operations, thereby paving the way towards enhanced climate resilience and sustainability in built environments. By embracing innovative methodologies and technological solutions, stakeholders can mitigate energy consumption, reduce greenhouse gas emissions, and fortify infrastructure against the impacts of climate change.
Keywords: HVAC, Climate Resilience, Project Management, Energy, Review.
Dissertations / Theses on the topic "Optimization of HVAC energy consumption"
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Abedi, Milad. "Directional Airflow for HVAC Systems." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/88524.
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Directional airflow has been utilized to enable targeted air conditioning in cars and airplanes for many years, where the occupants could adjust the direction of flow. In the building sector however, HVAC systems are usually equipped with stationary diffusors that can only supply the air either in the form of diffusion or with fixed direction to the room in which they have been installed. In the present thesis, the possibility of adopting directional airflow in lieu of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.M.S.
The notion of adjustable direction of airflow has been used in the car industry and airplanes for decades, enabling the users to manually adjust the direction of airflow to their satisfaction. However, in the building the introduction of the incoming airflow to the environment of the room is achieved either by non-adjustable uniform diffusors, aiming to condition the air in the environment in a homogeneous manner. In the present thesis, the possibility of adopting directional airflow in place of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle on average. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.
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Taghi, Nazari Alireza. "Interaction between thermal comfort and HVAC energy consumption in commercial buildings." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/597.
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The primary purpose of the current research was to implement a numerical model to investigate the interactions between the energy consumption in Heating, Ventilating, and Air Conditioning (HVAC) systems and occupants’ thermal comfort in commercial buildings. A numerical model was developed to perform a thermal analysis of a single zone and simultaneously investigate its occupants’ thermal sensations as a non-linear function of the thermal environmental (i.e. temperature, thermal radiation, humidity, and air speed) and personal factors (i.e. activity and clothing). The zone thermal analyses and thermal comfort calculations were carried out by applying the heat balance method and current thermal comfort standard (ASHRAE STANDARD 55-2004) respectively.
The model was then validated and applied on a single generic zone, representing the perimeter office spaces of the Centre for Interactive Research on Sustainability (CIRS), to investigate the impacts of variation in occupants’ behaviors, building’s envelope, HVAC system, and climate on both energy consumption and thermal comfort. Regarding the large number of parameters involved, the initial summer and winter screening analyses were carried out to determine the measures that their impacts on the energy and/or thermal comfort were most significant. These analyses showed that, without any incremental cost, the energy consumption in both new and existing buildings may significantly be reduced with a broader range of setpoints, adaptive clothing for the occupants, and higher air exchange rate over the cooling season.
The effects of these measures as well as their combination on the zone thermal performance were then studied in more detail with the whole year analyses. These analyses suggest that with the modest increase in the averaged occupants’ thermal dissatisfaction, the combination scenario can notably reduce the total annual energy consumption of the baseline zone.
Considering the global warming and the life of a building, the impacts of climate change on the whole year modeling results were also investigated for the year 2050. According to these analyses, global warming reduced the energy consumption for both the baseline and combination scenario, thanks to the moderate and cold climate of Vancouver.
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Xie, Wang. "Energy Consumption Modeling in Wireless Sensor Networked Smart Homes." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32071.
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Smart home automation is the dwelling bridge of smart grid technology, as it
integrates the modern home appliances power consumption information over
communication networks in the smart grid system. Among all the appliances,
Heating, Ventilation and Cooling (HVAC) systems is one of the most primary
concerns. Since a great amount of power consumption is contributed by these HVAC
systems. Traditionally, HVAC systems run at a fixed schedule without automatic
monitoring and control systems, which causes load variation, fluctuations in the
electricity demand and inefficient utility operation. In this thesis, we propose a Finite
State Machine (FSM) system to model the air condition working status to acquire the
relationship between temperature changing and cooling/heating duration. Finally, we
introduce the Zigbee communciation protocol into the model, the performance
analysis of the impact of end-to-end delay over HVAC systems is presented.
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Sun, Zhifeng. "Energy Consumption Optimization of Electric Vehicles." Thesis, KTH, Fordonsdynamik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302774.
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This master thesis report has studied several methods to improve the energy consumption of an electric vehicle equipped with two permanent magnet synchronous motors. Two driving torque distribution maps are developed based on efficiency map and load transfer, respectively. The drive torque distribution map based on the efficiency map shows up to 8.94% energy saving. Two regenerative braking strategies are designed and compared. Both strategies have pure regenerative brake at low decelerations and it is controlled by a modified acceleration pedal map. Strategy 1 does not add more regenerative braking when the brake pedal is pressed thus it is simpler while strategy 2 can blend in more motor torque. Rear axle steering is also studied in terms of contribution to energy consumption and an LQR controller is developed to control the vehicle with rear axle steering.Denna rapport avhandlar ett examensarbete där flera metoder har studerats för att förbättra energikonsumptionen för ett elektriskt fordon med två permanentmagnetsynkrona motorer. Två fördelningskartor för drivande moment är framtagna baserat på effektivitetskartor och lastöverföring. Fördelningskartorna för drivande moment som är baserat på effektivitet visar upp till 8,94% energiminskning. Två olika regenerativa bromsstrategier är framtagna och jämförda. Båda strategierna har ren regeneration vid låga decelerationer och är reglerat genom modifierat gaspedalsmappning. Strategi 1 ger inte mer regeneration när bromspedalen trycks ned och är då enklare medans strategi 2 kan blanda in mer vridmoment från elmotorn. Bakaxelstyrning är också studerat i termer av dess bidrag till energikonsumption samt en LQR regulator är utvecklad för reglering av fordonets bakaxelstyrning.
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Sui, Di. "Characterization of HVAC operation uncertainty in EnergyPlus AHU modules." Thesis, Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51911.
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This study addresses 5 uncertainties that exist in the operation of HVAC systems, which will presumably affect the actual energy consumption of the HVAC system in comparison to the consumption under idealized bahavior. We consequently add these parameters and their uncertainty range into the source code, eventually resulting in an EnergyPlus program in which the HVAC operation uncertainty is embedded as so-called model form uncertainty. The upgraded EnergyPlus is tested for each parameter uncertainty separately, and to show the impact of each uncertainty albeit for hypothetical uncertainty ranges of the parameters.
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Tang, Fan. "HVAC system modeling and optimization: a data-mining approach." Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/895.
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Heating, ventilating and air-conditioning (HVAC) system is complex non-linear system with multi-variables simultaneously contributing to the system process. It poses challenges for both system modeling and performance optimization. Traditional modeling methods based on statistical or mathematical functions limit the characteristics of system operation and management.
Data-driven models have shown powerful strength in non-linear system modeling and complex pattern recognition. Sufficient successful applications of data mining have proved its capability in extracting models accurately describing the relation of inner system. The heuristic techniques such as neural networks, support vector machine, and boosting tree have largely expanded to the modeling process of HVAC system.
Evolutionary computation has rapidly merged to the center stage of solving the multi-objective optimization problem. Inspired from the biology behavior, it has shown the tremendous power in finding the optimal solution of complex problem. Different applications of evolutionary computation can be found in business, marketing, medical and manufacturing domains. The focus of this thesis is to apply the evolutionary computation approach in optimizing the performance of HVAC system. The energy saving can be achieved by implementing the optimal control setpoints with IAQ maintained at an acceptable level. A trade-off between energy saving and indoor air quality maintenance is also investigated by assigning different weights to the corresponding objective function. The major contribution of this research is to provide the optimal settings for the existing system to improve its efficiency and different preference-based operation methods to optimally utilize the resources.
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Li, Mingyang. "Application of computational intelligence in modeling and optimization of HVAC systems." Thesis, University of Iowa, 2009. https://ir.uiowa.edu/etd/397.
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HVAC (Heating Ventilating and Air-Conditioning) system is multivariate, nonlinear, and shares time-varying characteristics. It poses challenges for both system modeling and performance optimization. Traditional modeling approaches based on mathematical equations limit the nature of the optimization models and solution approaches.
Computational intelligence is an emerging area of study which provides powerful tools for modeling and analyzing complex systems. Computational intelligence is concerned with discovery of structures in data and recognition of patterns. It encompasses techniques such as neural networks, fuzzy logic, and so on. These techniques derive rules, patterns, and develop complex mappings from the data. The recent advances in information technology have enabled collection of large volumes of data. Computational intelligence embraces biology-inspired paradigms like evolutionary computation and particle swarm intelligence in solving complex optimization problems.
Successful applications of computational intelligence have been found in business, marketing, medical and manufacturing domains. The focus of this thesis is to apply computational intelligence approach in modeling and optimization of HVAC systems. In this research, four HVAC sub-systems are investigated: the AHU (Air Handling Unit), VAV (Variable Air Volume), ventilation system, and thermal zone. Various computational intelligence approaches are used to identify parameters or problem solving. Energy savings are accomplished by minimizing the cooling output, reheating output or fan running time as well as on-line monitoring. One contribution of the research reported in the thesis is the use of computational intelligence algorithms to establish nonlinear mappings among different parameters. Another major contribution is in using heuristics algorithms to solve multi-objective optimization problems.
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Pietruschka, Dirk. "Model based control optimisation of renewable energy based HVAC Systems." Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/4022.
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During the last 10 years solar cooling systems attracted more and more interest not only in the research area but also on a private and commercial level. Several demonstration plants have been installed in different European countries and first companies started to commercialise also small scale absorption cooling machines. However, not all of the installed systems operate efficiently and some are, from the primary energy point of view, even worse than conventional systems with a compression chiller. The main reason for this is a poor system design combined with suboptimal control. Often several non optimised components, each separately controlled, are put together to form a ‘cooling system’. To overcome these drawbacks several attempts are made within IEA task 38 (International Energy Agency Solar Heating and Cooling Programme) to improve the system design through optimised design guidelines which are supported by simulation based design tools. Furthermore, guidelines for an optimised control of different systems are developed. In parallel several companies like the SolarNext AG in Rimsting, Germany started the development of solar cooling kits with optimised components and optimised system controllers. To support this process the following contributions are made within the present work: - For the design and dimensioning of solar driven absorption cooling systems a detailed and structured simulation based analysis highlights the main influencing factors on the required solar system size to reach a defined solar fraction on the overall heating energy demand of the chiller. These results offer useful guidelines for an energy and cost efficient system design. - Detailed system simulations of an installed solar cooling system focus on the influence of the system configuration, control strategy and system component control on the overall primary energy efficiency. From the results found a detailed set of clear recommendations for highly energy efficient system configurations and control of solar driven absorption cooling systems is provided. - For optimised control of open desiccant evaporative cooling systems (DEC) an innovative model based system controller is developed and presented. This controller consists of an electricity optimised sequence controller which is assisted by a primary energy optimisation tool. The optimisation tool is based on simplified simulation models and is intended to be operated as an online tool which evaluates continuously the optimum operation mode of the DEC system to ensure high primary energy efficiency of the system. Tests of the controller in the simulation environment showed that compared to a system with energy optimised standard control the innovative model based system controller can further improve the primary energy efficiency by 19 %.
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Xue, Li. "Process Optimization of Dryers/Tenters in the Textile Industry." Thesis, Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5066.
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Textile dyeing and finishing industry uses dryers/tenters for drying and heat-setting fabrics. A very large fraction of the heating value of the fuel consumed in the burner ends up as waste in the dryer exhaust. An initial calculation showed that up to 90% of the energy consumed in the tenter is wasted. Therefore, quantifying the energy waste and determining drying characteristics are vitally important to optimizing the tenter and dryer operations. This research developed a portable off-line gas chromatography-based characterization system to assess the excess energy consumption. For low-demanding heat-setting situations, energy savings can be realized quickly.
On the other hand, there are demanding situations where fabric drying represents the production bottleneck. The drying rate may be governed either by the rate of heat transport or by the rate of moisture transport. A mathematical model is being developed that incorporates both these processes. The model parameters are being obtained from bench-scale dryer studies in the laboratories. The model will be validated using production scale data. This will enable one to predict optimization dryer operation strategies.
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Gupta, Deepak Prakash. "Energy sensitive machining parameter optimization model." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4406.
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Thesis (M.S.)--West Virginia University, 2005.Title from document title page. Document formatted into pages; contains ix, 71 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 67-71).
Books on the topic "Optimization of HVAC energy consumption"
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Westphalen, Detlef. Energy consumption characteristics of commercial building HVAC systems: Energy savings potential. Cambridge, MA: TIAX LLC., 2002.
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Cooper, Ken. Residential HVAC controller measurement input analysis: Final report. Ottawa, Ont: CANMET Energy Technology Centre, 1999.
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Geller, Howard S. Consensus national efficiency standards for lamps, motors, showerheads, and commercial hvac equipment. Washington, DC: American Council for an Energy-Efficient Economy, 1992.
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Westphalen, Detlef. Energy consumption characteristics of commercial building HVAC systems: Thermal distribution, auxiliary equipment, and ventilation. Cambridge, MA: Arthur D. Little, Inc., 1999.
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Westphalen, Detlef. Energy consumption characteristics of commercial building HVAC systems: Chillers, refrigerant compressors, and heating systems. Cambridge, MA: Arthur D. Little, Inc., 2001.
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Rob, Queen, California Energy Commission. Public Interest Energy Research., California Energy Commission. Energy Innovations Small Grant Program., and San Diego State University. Foundation., eds. New powerline control technology for lighting and HVAC: Independent assessment report. [Sacramento, Calif.]: California Energy Commission, 2007.
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Zhaobo, Sun, ed. Qi hou bian hua dui wo guo qu nuan he jiang wen hao neng de ying xiang ji you hua yan jiu: Impact of climate change on energy consumption and optimization in China. Beijing: Qi xiang chu ban she, 2008.
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National Renewable Energy Laboratory (U.S.), United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information, eds. JouleLabs cooperative research and development agreement. Golden, Colo.]: National Renewable Energy Laboratory, 2010.
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Frontini, Francesco. Daylight and solar control in buildings: General evaluation and optimization of a new angle selective glazing façade. Stuttgart: Fraunhofer-Verlag, 2011.
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Xiao, Yu, Sasu Tarkoma, Matti Siekkinen, and Eemil Lagerspetz. Smartphone Energy Consumption: Modeling and Optimization. Cambridge University Press, 2014.
Find full textBook chapters on the topic "Optimization of HVAC energy consumption"
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Offtermatt, David, Daniel Lust, and Tobias Erhart. "Box-Type Windows as Means for Better Air Quality and Acoustic Comfort in Urban Areas." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 315–34. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_21.
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AbstractControlled natural ventilation in office buildings can ensure the indoor thermal comfort while reducing the life cycle energy consumption for ventilation, compared to mechanical ventilation systems (e.g. HVAC). Natural ventilation is mostly used in moderate climate zones where air conditioning is not a standard. During intermediate seasons, buildings with HVAC systems can additionally use natural ventilation to reduce energy consumption. However, in dense urban areas, natural ventilation can be problematic in terms of acoustic comfort. Here, a box-type window can serve as a compromise between thermal and acoustic comfort. Due to the more complex handling of the box-type window, an automated (electric driven) novel box-type window approach was developed within the imaF project, a part of the iCity initiative. The following article describes the basics of automated natural ventilation, acoustic characterization as well as architectural integration of this window type and the optimization of the airflow through box-type windows. The results show that the proposed geometry can provide sound insulation while providing an appropriate air exchange rate.
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Papadopoulos, Sokratis, and Elie Azar. "Multi-objective Genetic Algorithm Optimization of HVAC Operation: Integrating Energy Consumption, Thermal Comfort, and Productivity." In Energy Systems Evaluation (Volume 2), 261–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67376-5_11.
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Hilgers, Michael, and Wilfried Achenbach. "Vehicle and Energy Loss." In Fuel Consumption and Consumption Optimization, 5–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-60841-8_2.
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Hilgers, Michael. "Vehicle and Energy Loss." In Fuel Consumption and Consumption Optimization, 7–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66449-0_2.
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Belleudy, Cécile. "Optimization of Energy Consumption." In Real-Time Systems Scheduling 1, 231–67. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118984413.ch6.
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Al-Salim, Kasim, Ivan Andonovic, and Craig Michie. "Cyclic Blackout Mitigation Through HVAC Shifted Queue Optimization." In Energy Efficient Data Centers, 34–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15786-3_3.
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Hilgers, Michael. "Concluding Remarks on the Topic of Energy Consumption." In Fuel Consumption and Consumption Optimization, 59–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66449-0_7.
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Hilgers, Michael. "The Influence of the Driver on Energy Consumption." In Fuel Consumption and Consumption Optimization, 53–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66449-0_5.
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Hariharan, K., Mathiarasan Vivek Ramanan, Naresh Kumar, D. Kesava Krishna, Arockia Dhanraj Joshuva, and S. K. Indumathi. "Design and Development of Energy Meter for Energy Consumption." In Modeling, Simulation and Optimization, 563–69. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6866-4_42.
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Wojnicki, Igor, and Leszek Kotulski. "Street Lighting Control, Energy Consumption Optimization." In Artificial Intelligence and Soft Computing, 357–64. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59060-8_32.
Full textConference papers on the topic "Optimization of HVAC energy consumption"
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Wemhoff, Aaron P. "Optimization of Equipment Control Parameters to Minimize HVAC Energy Consumption." In ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54063.
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Heating, Ventilating, and Air Conditioning Systems (HVAC) consume nearly one-third of household energy in the United States. The optimization of the control parameters in HVAC equipment allows for a reduction in energy consumption. In this study, a supervisory control method is applied to a lumped parameter model of an HVAC system of interest containing a chiller and three dampers. The method determines the choice of control parameters that minimize the energy consumption for 1000 sampled steady-state loads. An energy savings of 39% was achieved using the method in this study compared to the base case.
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Wemhoff, Aaron P. "HVAC System Energy Minimization via Optimization of Lumped System Models." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-37163.
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Heating, ventilating, and air conditioning (HVAC) systems comprise nearly one third of annual household energy consumption in the United States. Energy savings can be gained by optimizing HVAC performance using a system of controls. This study applies a novel control method towards a system with an arbitrary steady-state load distribution. The new method applies multi-dimensional interpolation of optimized control configurations for various steady-state load distributions. These optimal configurations are derived using a lumped parameter simulation of the HVAC system. The new method is applied to a three-room HVAC system to demonstrate a power savings of 40% compared to an uncontrolled system. These savings compare favorably to predictions using the Variable-Air-Volume (VAV) method (37% power savings) and VAV with chiller control (33% power savings).
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Tesiero, Raymond C., Nabil Nassif, Balakrishna Gokaraju, and Daniel Adrian Doss. "Intelligent Approaches for Modeling and Optimizing HVAC Systems’ Energy Use." In ASME 2017 11th International Conference on Energy Sustainability collocated with the ASME 2017 Power Conference Joint With ICOPE-17, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/es2017-3105.
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Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This paper evaluates model-based optimization processes (OP) for HVAC systems utilizing any computer algebra system (CAS), genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) is tested using data collected from an actual HVAC system. Using this optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013. This research focuses primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort.
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Jovanović, Vladan, and Marko Ignjatović. "Simulation of the energy performance of potential HVAC systems and implementation of renewable energy sources to achieve nZEB on the example of an office building in Nis." In 54th International HVAC&R Congress and Exhibition. SMEITS, 2024. http://dx.doi.org/10.24094/kghk.023.061.
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One of the key approaches to achieving an optimal balance between energy consumption and overall comfort in existing buildings lies in the use of sophisticated simulations for efficient management of HVAC systems. Modeling and simulating the energy performance of buildings represent a highly advanced technique that enables the predic-tion of complex system behaviors based on physical laws and principles. These simulations allow for precise solving of thermal equilibrium equations, taking into account all essential physical characteristics of the building, the com-plexity of the mechanical systems serving it, as well as a wide range of dynamic input variables throughout the entire calendar cycle. Critical factors significantly influencing energy consumption in buildings are region-specific climatic condi-tions, as well as individual user expectations regarding indoor temperature, humidity, and air quality. The methodology of this advanced research is based on the use of the state-of-the-art simulation tool Ener-gyPlus, which enables a detailed analysis of the building's energy performance. This holistic approach enables the enhancement of energy efficiency in existing buildings and optimization of HVAC system operation, resulting in significant energy savings and improved overall user comfort. Furthermore, this study aims to demonstrate the improvement of building systems themselves to achieve nZEB buildings and the utiliza-tion of renewable energy sources. The work is expected to use simulations of the model, along with additional systems, to minimize the net site en-ergy through PV panel-integrated systems compared to the model without such systems. The results obtained from the baseline model already demonstrate low energy requirements, while the use of PV panels is expected to result in even lower consumption. The total energy required to meet the building's energy needs is 41,109.67 kWh, which translates to 36.41 kWh/m² of the total building area. The paper will also demonstrate a reduction in CO2 emissions compared to the model without PV panel-integrated systems.
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Kameel, Ramiz, and Essam E. Khalil. "Energy Efficiency, Air Quality, and Comfort in Air-Conditioned Spaces." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/cie-48255.
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To design an optimum HVAC airside system that provides comfort and air quality in the air-conditioned spaces with efficient energy consumption is a great challenge. This paper evaluates recent progresses of HVAC airside design for the airconditioned spaces. The present evaluation study defines the current status, future requirements, and expectations. It has been found that, the experimental investigations should be considered in the new trend of studies, not to validate the numerical tools only, but also to provide a complete database of the airflow characteristics in the air-conditioned spaces. Based on this analysis and the vast progress of computers and associated software, the artificial intelligent technique will be a competitor candidate to the experimental and numerical techniques. Finally, the researches that relate between the different designs of the HVAC systems and energy consumption should concern with the optimization of airside design as the expected target to enhance the indoor environment.
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Fiducioso, Marcello, Sebastian Curi, Benedikt Schumacher, Markus Gwerder, and Andreas Krause. "Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/811.
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We tune one of the most common heating, ventilation, and air conditioning (HVAC) control loops, namely the temperature control of a room. For economical and environmental reasons, it is of prime importance to optimize the performance of this system. Buildings account from 20 to 40 % of a country energy consumption, and almost 50 % of it comes from HVAC systems. Scenario projections predict a 30 % decrease in heating consumption by 2050 due to efficiency increase. Advanced control techniques can improve performance; however, the proportional-integral-derivative (PID) control is typically used due to its simplicity and overall performance. We use Safe Contextual Bayesian Optimization to optimize the PID parameters without human intervention. We reduce costs by 32 % compared to the current PID controller setting while assuring safety and comfort to people in the room. The results of this work have an immediate impact on the room control loop performances and its related commissioning costs. Furthermore, this successful attempt paves the way for further use at different levels of HVAC systems, with promising energy, operational, and commissioning costs savings, and it is a practical demonstration of the positive effects that Artificial Intelligence can have on environmental sustainability.
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Khalil, Essam E. "Innovative Approach to Energy Efficient Buildings From Construction to Services: A Review." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-85087.
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Attempts to adequately design an optimum HVAC airside system that furnishes comfort and air quality in the air-conditioned spaces with efficient energy consumption represent a great challenge. This paper presents a recent status quo of HVAC airside design for the air-conditioned spaces under holistic approach. The present review summarizes the current status, future requirements, and expectations. It has been found that, the experimental investigations should be considered in the new trend of energy investigations, not to merely to validate the numerical tools, but also to provide a complete database of the airflow characteristics in the air-conditioned spaces. Based on this analysis and the vast progress of computers and associated software, the artificial intelligent technique is sought as a prominent competitor candidate to the experimental and numerical techniques. Finally, the researches that relate between the different designs of the HVAC systems and energy consumption should concern with the optimization of airside design as the expected target to enhance the indoor environment. The present paper reviews the status quo and critically analyses the appropriate approaches to sustainability.
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Díaz Jácome, Alfredo, Marco E. Sanjuán, Victor Fontalvo Morales, and Cinthia Audivet Durán. "Model Predictive Control of an HVAC System Based on Dynamic Tracking and Optimization of Energy Use." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50434.
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U.S. Department of Energy affirms that HVAC systems consume approximately 40% of the total energy used in commercial-building sector. These types of systems are complex because they are composed of a large number of interconnected subsystems. The analysis shown in this paper is established on a building geographically located at the Caribbean coast region of Colombia in a region with tropical savanna climate and it is exposed to constant thermal load changes associated to high wall temperatures and direct sunlight incidence. Under this perspective, an energetic analysis is performed for the HVAC in order to implement a Model Predictive Control (MPC) strategy to enhance the system efficiency under the previously mentioned external conditions. The model predictive strategy is implemented as a system supervisor in order to minimize a cost function that measures the ratio of water consumption to air temperature change in the cooling coil. The strategy manipulates the required temperature of supply water to cooling coil from the chiller, perceiving as input perturbation the outdoor temperature, the desired temperatures for the classrooms and the desired temperature of the air supply to the different zones. The comparison and selection of thermodynamical states for analysis are conducted according to the dynamic characteristics of the entire system and individual components, and the energy assessment is performed including the system transient response. The accomplishment of the supervisory control strategy has demonstrated that dynamic energetic analysis and assessment is an auxiliary tool for HVAC performance management. The analysis performed shows that the supervisory strategy can reduce properly the energy performance index as a consequence, the energy consumption of the fan has a reduction of a 0.65%, while the water required shows a reduction of 66.93%.
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Yunus, Muhammad, Nandy Putra, Imansyah Ibnu Hakim, Fayza Yulia, and Nasruddin Nasruddin. "Multi-Objective Optimization of Techno-Economic Feasibility of Heat Pipe Heat Exchanger (HPHE) for Air Conditioning Systems." In The 11th Asia Conference on Mechanical and Materials Engineering. Switzerland: Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-k1gc4g.
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In the HVAC system, the required energy consumption is very large so that energy saving processes are needed. One effective method in this energy saving process is the heat recovery process using a Heat Pipe Heat Exchanger. Various studies have been carried out regarding the application of HPHE in HVAC systems to reduce temperature and maintain air humidity. In its application, there are many factors that affect the effectiveness of using this HPHE. Therefore, in this research, an optimization process will be carried out by considering the number of HPHE modules, inlet air temperature, and inlet air velocity. The modelling is performed to predict energy recovery and payback period by using Response Surface Methodology (RSM). Optimization study was conducted to investigate the optimum energy recovery and payback period by varying HPHE parameters experiment.
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Chan, Korey, and Saeid Bashash. "Modeling and Energy Cost Optimization of Air Conditioning Loads in Smart Grid Environments." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5284.
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Electricity for heating, ventilation, and air condition (HVAC) machines takes up a large percentage of energy consumption in the buildings and thus in turn, a large portion of the energy monetary cost. Optimization of air conditioners use throughout the day will reduce energy consumption and expenditure. This study introduces a second-order differential equation model to capture the indoor temperature dynamics of a building. An experimental test bed is developed to collect a set of indoor/outdoor temperature and sunlight data. Using a least-squares-based system identification process, the model parameters are identified and checked through simulation. Optimization of the room temperature is then determined by solving a mixed-integer quadratic programming problem in relation to the hourly-updated energy prices. Mixed-integer quadratic programming solution is compared to a two-point thermostatic control system. A hybrid solution compromising the quadratic programming algorithm and the conventional thermostatic control scheme is proposed as a tractable approach for the near-optimal energy management of the system.
Reports on the topic "Optimization of HVAC energy consumption"
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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0739.
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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.
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Witzig, Andreas, Camilo Tello, Franziska Schranz, Johannes Bruderer, and Matthias Haase. Quantifying energy-saving measures in office buildings by simulation in 2D cross sections. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541623658.
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A methodology is presented to analyse the thermal behaviour of buildings with the goal to quantify energy saving measures. The solid structure of the building is modelled with finite elements to fully account for its ability to store energy and to accurately predict heat loss through thermal bridges. Air flow in the rooms is approximated by a lumped element model with three dynamical nodes per room. The dynamic model also contains the control algorithm for the HVAC system and predicts the net primary energy consumption for heating and cooling of the building for any time period. The new simulation scheme has the advantage to avoid U-values and thermal bridge coefficients and instead use well-known physical material parameters. It has the potential to use 2D and 3D geometries with appropriate automatic processing from BIM models. Simulations are validated by comparison to IDA ICE and temperature measurement. This work aims to discuss novel approaches to disseminating building simulation more widely.
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Baker, Justin S., George Van Houtven, Yongxia Cai, Fekadu Moreda, Chris Wade, Candise Henry, Jennifer Hoponick Redmon, and A. J. Kondash. A Hydro-Economic Methodology for the Food-Energy-Water Nexus: Valuation and Optimization of Water Resources. RTI Press, May 2021. http://dx.doi.org/10.3768/rtipress.2021.mr.0044.2105.
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Growing global water stress caused by the combined effects of growing populations, increasing economic development, and climate change elevates the importance of managing and allocating water resources in ways that are economically efficient and that account for interdependencies between food production, energy generation, and water networks—often referred to as the “food-energy-water (FEW) nexus.” To support these objectives, this report outlines a replicable hydro-economic methodology for assessing the value of water resources in alternative uses across the FEW nexus–including for agriculture, energy production, and human consumption—and maximizing the benefits of these resources through optimization analysis. The report’s goal is to define the core elements of an integrated systems-based modeling approach that is generalizable, flexible, and geographically portable for a range of FEW nexus applications. The report includes a detailed conceptual framework for assessing the economic value of water across the FEW nexus and a modeling framework that explicitly represents the connections and feedbacks between hydrologic systems (e.g., river and stream networks) and economic systems (e.g., food and energy production). The modeling components are described with examples from existing studies and applications. The report concludes with a discussion of current limitations and potential extensions of the hydro-economic methodology.
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Levy, Alberto, Adriana M. Valencia J., and Ariel Yépez-García. The Energy Sector: Opportunities and Challenges. Inter-American Development Bank, August 2016. http://dx.doi.org/10.18235/0010658.
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The analysis is informed by the particular circumstances and needs of each country, as well as by the goals of key regional energy integration initiatives. The energy sector is referred to here as all economic activities related to the use of renewable and nonrenewable resources for the production, delivery, and consumption of energy in its various forms, such as electricity, heat, or fuels for further processing, as well as the optimization of energy use through energy efficiency and conservation. To approach this complex set of relationships, this document presents the challenges of the sector in the region, recognizing that they are interrelated and have varying levels of importance depending on the country in question.
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Ratmanski, Kiril, and Sergey Vecherin. Resilience in distributed sensor networks. Engineer Research and Development Center (U.S.), October 2022. http://dx.doi.org/10.21079/11681/45680.
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With the advent of cheap and available sensors, there is a need for intelligent sensor selection and placement for various purposes. While previous research was focused on the most efficient sensor networks, we present a new mathematical framework for efficient and resilient sensor network installation. Specifically, in this work we formulate and solve a sensor selection and placement problem when network resilience is also a factor in the optimization problem. Our approach is based on the binary linear programming problem. The generic formulation is probabilistic and applicable to any sensor types, line-of-site and non-line-of-site, and any sensor modality. It also incorporates several realistic constraints including finite sensor supply, cost, energy consumption, as well as specified redundancy in coverage areas that require resilience. While the exact solution is computationally prohibitive, we present a fast algorithm that produces a near-optimal solution that can be used in practice. We show how such formulation works on 2D examples, applied to infrared (IR) sensor networks designed to detect and track human presence and movements in a specified coverage area. Analysis of coverage and comparison of sensor placement with and without resilience considerations is also performed.
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Dumas, Nathalie, Flourentzou Flourentzos, Julien BOUTILLIER, Bernard Paule, and Tristan de KERCHOVE d’EXAERDE. Integration of smart building technologies costs and CO2 emissions within the framework of the new EPIQR-web application. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541616188.
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The EPIQR method was developed between 1996 and 1998 within the framework of the European research programme JOULE II and with the support of the Swiss Federal Office for Education and Science. In its first versions, the EPIQR software and EPIQR+ that succeeded it, were desktop tools, allowing a precise diagnosis of the state of deterioration of an existing building and the elaboration of renovation scenarios including the different costs of the necessary works. However, deep refurbishment rate is still low. Climatic emergency state declared by most of the Swiss Cantons makes it necessary to search also for other strategies for urgent reduction of CO2 emissions. As part of the PRELUDE project, a web version of this tool has been developed to integrate both smart technologies and energy optimization actions. Some of them can be considered as soft actions, making it possible to develop a soft renovation roadmap for buildings that are not scheduled for renovation in the short term. As examples, the costs of optimization contracts, intelligent heating control, demand-controlled ventilation, abandonment of heat production from fossil fuels, integration of renewable energies into the building, and communities’ creation for self-consumption of photovoltaic production have now been modelled. Το help the residential building stock fit with the CO2 reduction of 60% by 2030 compliance and the “2000 W society” energy sobriety target by 2050, the EPIQR-WEB database includes the CO2 indirect emissions of each refurbishment action. Hence, this updated version enables the building diagnosis expert to evaluate and optimise deep refurbishment scenarios, from both financial and environmental point of view. Parallel calculation of CO2 indirect emissions with the calculation of refurbishment cost is done without extra time cost for the user. The paper will show the software new functions, the EPIQR-WEB database expansion and how its overall results can be used to meet the European Union Climate Target through a realistic and comprehensive investment plan.
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Einarsson, Rasmus. Nitrogen in the food system. TABLE, February 2024. http://dx.doi.org/10.56661/2fa45626.
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Nitrogen (N) plays a dual role in the agri-food system: it is an essential nutrient for all life forms, yet also an environmental pollutant causing a range of environmental and human health impacts. As the plant nutrient needed in greatest quantities, and as a building block of proteins and other biomolecules, N is a necessary part of all life. In the last century, an enormous increase of N turnover in the agri-food system has enabled increasing per-capita food supply for a growing world population, but as an unintended side effect, N pollution has increased to levels widely agreed in science and policy to be far beyond sustainable limits. There is no such thing as perfectly circular N supply. Losses of N to the environment inevitably arise as N is transformed and used in the food system, for example in soil processes, in manure storage, and in fertilizer application. This lost N must be replaced by ‘new’ N, which is N converted to bioavailable forms from the vast atmospheric pool of unreactive dinitrogen (N2). New N comes mainly as synthetic N fertilizer and through a process known as biological N fixation (BNF). In addition, there is a large internal flow of recycled N in the food system, mainly in the form of livestock excreta. This recirculated N, however, is internal to the food system and cannot make up for the inevitable losses of N. The introduction of synthetic N fertilizer during the 20th century revolutionized the entire food system. The industrial production of synthetic N fertilizer was a revolution for agricultural systems because it removed the natural constraint of N scarcity. Given sufficient energy, synthetic N fertilizer can be produced in limitless quantities from atmospheric dinitrogen (N2). This has far-reaching consequences for the whole agri-food system. The annual input of synthetic N fertilizer today is more than twice the annual input of new N in pre-industrial agriculture. Since 1961, increased N input has enabled global output of both crop and livestock products to roughly triple. During the same time period, total food-system N emissions to the environment have also more than tripled. Livestock production is responsible for a large majority of agricultural N emissions. Livestock consume about three-quarters of global cropland N output and are thereby responsible for a similar share of cropland N emissions to air and water. In addition, N emissions from livestock housing and manure management systems contribute a substantial share of global N emissions to air. There is broad political agreement that global N emissions from agriculture should be reduced by about 50%. High-level policy targets of the EU and of the UN Convention on Biological Diversity are for a 50% reduction in N emissions. These targets are in line with a large body of research assessing what would be needed to stay within acceptable limits as regards ecosystem change and human health impacts. In the absence of dietary change towards less N-intensive diets, N emissions from food systems could be reduced by about 30%, compared to business-as-usual scenarios. This could be achieved by implementing a combination of technical measures, improved management practices, improved recycling of wasted N (including N from human excreta), and spatial optimization of agriculture. Human dietary change, especially in the most affluent countries, offers a huge potential for reducing N emissions from food systems. While many of the world’s poor would benefit nutritionally from increasing their consumption of nutrient-rich animal-source foods, many other people consume far more nutrients than is necessary and could reduce consumption of animal-source food by half without any nutritional issues. Research shows that global adoption of healthy but less N-polluting diets might plausibly cut future food-system N losses by 10–40% compared to business-as-usual scenarios. There is no single solution for solving the N challenge. Research shows that efficiency improvements and food waste reductions will almost certainly be insufficient to reach agreed environmental targets. To reach agreed targets, it seems necessary to also shift global average food consumption onto a trajectory with less animal-source food.