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1
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.
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Feng, Yayuan, Youxian Huang, Haifeng Shang, Junwei Lou, Ala deen Knefaty, Jian Yao, and Rongyue Zheng. "Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression." Energies 15, no. 13 (June 24, 2022): 4626. http://dx.doi.org/10.3390/en15134626.
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Accurate prediction of air-conditioning energy consumption in buildings is of great help in reducing building energy consumption. Nowadays, most research efforts on predictive models are based on large samples, while short-term prediction with one-month or less-than-one-month training sets receives less attention due to data uncertainty and unavailability for application in practice. This paper takes a government office building in Ningbo as a case study. The hourly HVAC system energy consumption is obtained through the Ningbo Building Energy Consumption Monitoring Platform, and the meteorological data are obtained from the meteorological station of Ningbo city. This study utilizes a Gaussian process regression with the help of a 12 × 12 grid search and prediction processing to predict short-term hourly building HVAC system energy consumption by using meteorological variables and short-term building HVAC energy consumption data. The accuracy R2 of the optimal Gaussian process regression model obtained is 0.9917 and 0.9863, and the CV-RMSE is 0.1035 and 0.1278, respectively, for model testing and short-term HVAC system energy consumption prediction. For short-term HVAC system energy consumption, the NMBE is 0.0575, which is more accurate than the standard of ASHRAE, indicating that it can be applied in practical energy predictions.
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Lin, Yao Lin, Wei Yang, and Ming Sheng Liu. "Energy Efficiency Measures for a High-Tech Building with a Solar Roof." Advanced Materials Research 1073-1076 (December 2014): 1233–38. http://dx.doi.org/10.4028/www.scientific.net/amr.1073-1076.1233.
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This paper presents the implementation of energy efficiency measures in a building that consists of office, lab and clean room area. Total Performance Oriented Optimization and Retrofits (TPORs) were implemented. 594 kW solar panels were installed on the roof and connected to the electrical grid during the optimization process. Ten power meters were installed throughout the building to measure the total building electricity demand, solar generated electricity demand, HVAC and non-HVAC-equipment demand to quantify the energy savings from the implementation of the energy efficiency measures and savings from the solar panels. The electricity savings from optimization on the HVAC system is about 7,209,000kWh/year (194.4kWh/m2-year), which is about 30% of the total building electricity consumption with peak demand reduction of 935 kW. There savings come from the solar panel is 811,925 kWh/yr; however, it effectively reduced the peak electricity demand by 302.6 kW.
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Talib, Rand, and Nabil Nassif. "“Demand Control” an Innovative Way of Reducing the HVAC System’s Energy Consumption." Buildings 11, no. 10 (October 18, 2021): 488. http://dx.doi.org/10.3390/buildings11100488.
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According to EIA, the Heating Ventilation and Air Conditioning (HAVC) systems account for about 25% of the U.S.’s total commercial building’s energy use. Therefore, advanced modeling and optimization methods of the system components and operation offer great ways to reduce energy consumption in all types of buildings and mainly commercial buildings. This research introduced an innovative integrated two-level optimization technique for the HVAC system to reduce the total energy consumption while improving the indoor thermal comfort level. The process uses actual system performance data collected for the building automation systems (BAS) to create accurate component modeling and optimization process as the first level of optimization (MLO). Artificial neural networks were chosen to be the tool used to serve the process of modeling. The second optimization level utilizes the whole system-level optimization technique (SLO) using a genetic algorithm (G.A.). The proposed two-levels optimization technique will optimize the system setpoints, the supply air temperature, duct static pressure, minimum zone air flowrates, and minimum outdoor air ventilation rate. The proposed technique has contributed to the field of modeling and optimization of HVAC systems through several new contributions. (1) Implementing the demand control methodology with the optimization process to modify the electricity consumption power profile when the demand signal is received. (2) Implement the occupancy schedule inputs into the optimization process to adjust the ventilation airflow rates accordingly. (3) Implement the real-time zone occupancy sensor readings and adjust the zone’s ventilation flowrates and minimum flowrates. (4) Lastly, implementing the method of zone minimum air flowrates setpoint rests to reduce reheat requirements. The proposed optimization process was tested and validated, resulting in savings in the total energy consumed by the chilled water VAV system by 13.4%, 22.4 %, followed by 31% for July, February, and October, respectively.
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Xue, Guiyuan, Chen Wu, Wenjuan Niu, Xun Dou, Shizhen Wang, and Yadie Fu. "Flexible Control Strategy for Intelligent Building Air Conditioning System." E3S Web of Conferences 252 (2021): 01039. http://dx.doi.org/10.1051/e3sconf/202125201039.
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An improved optimization adjustment strategy for building heating ventilation and air conditioning (Heating Ventilation and Air Conditioning, HVAC) is proposed. The energy consumption model of building heating/refrigeration is established by using the instantaneous energy balance of heat, and then the optimal operation strategy of building HVAC energy based on weather forecast data is constructed in the range of user temperature comfort. Finally, the MATLAB and TRNSYS simulation techniques are used to verify the example. Simulation results show that the optimal operation strategy of building HVAC energy based on weather forecast data can not only significantly reduce the cost of energy use, but also effectively improve the absorption capacity of renewable energy on the building side.
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Mei, Guanghang, Bin Hu, Zhenjie Liu, Zhikun Zhu, and Wenjun Huang. "Energy-saving optimization of refrigeration system in a data centre based on Nelder-Mead method." ITM Web of Conferences 47 (2022): 03017. http://dx.doi.org/10.1051/itmconf/20224703017.
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In order to improve the cooling efficiency of the data centre HVAC (Heating Ventilation and Air Conditioning) system and save costs, this paper proposes a control strategy that combines traditional control method with Nelder and Mead method, in order to optimize equipment operating parameters. Firstly, this paper selected a data centre in Northeast China as the research object. Moreover, the central air-conditioning system, as the control object, was simulated on TRNSYS, whose result was compared with the actual operating parameters to verify the reliability of the simulation model. Finally, the optimal control strategy was applied to the simulation model to analyze the changes of energy consumption of the system before and after optimization. The results showed that in August, the optimal control strategy is able to meet the extremity of cooling load demand, and the total energy consumption of the system is 269,612kwh, which is 10% less than the energy consumption before optimization. The overall energy consumption of the system is significantly reduced, and the proportion of energy consumption of each part tends to be reasonable, which will effectively improve the cooling efficiency of HVAC.
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Lin, Xingbin, Deyu Yuan, and Xifei Li. "Reinforcement Learning with Dual Safety Policies for Energy Savings in Building Energy Systems." Buildings 13, no. 3 (February 21, 2023): 580. http://dx.doi.org/10.3390/buildings13030580.
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Reinforcement learning (RL) is being gradually applied in the control of heating, ventilation and air-conditioning (HVAC) systems to learn the optimal control sequences for energy savings. However, due to the “trial and error” issue, the output sequences of RL may cause potential operational safety issues when RL is applied in real systems. To solve those problems, an RL algorithm with dual safety policies for energy savings in HVAC systems is proposed. In the proposed dual safety policies, the implicit safety policy is a part of the RL model, which integrates safety into the optimization target of RL, by adding penalties in reward for actions that exceed the safety constraints. In explicit safety policy, an online safety classifier is built to filter the actions outputted by RL; thus, only those actions that are classified as safe and have the highest benefits will be finally selected. In this way, the safety of controlled HVAC systems running with proposed RL algorithms can be effectively satisfied while reducing the energy consumptions. To verify the proposed algorithm, we implemented the control algorithm in a real existing commercial building. After a certain period of self-studying, the energy consumption of HVAC had been reduced by more than 15.02% compared to the proportional–integral–derivative (PID) control. Meanwhile, compared to the independent application of the RL algorithm without safety policy, the proportion of indoor temperature not meeting the demand is reduced by 25.06%.
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Mckoy, DeQuante Rashon, Raymond Charles Tesiero, Yaa Takyiwaa Acquaah, and Balakrishna Gokaraju. "Review of HVAC Systems History and Future Applications." Energies 16, no. 17 (August 22, 2023): 6109. http://dx.doi.org/10.3390/en16176109.
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Today, HVAC (heating, ventilation, and air conditioning) systems have become an integral part of modern buildings and are designed to provide comfortable indoor environments while conserving energy and reducing carbon emissions. With advancement in technology, HVAC systems have a variety of sensors that are used to detect the occupants within a controlled environment. Advancements in computer control systems and the use of smart technology have made HVAC systems even more sophisticated, allowing for approximate temperature control and energy management. This paper will review the historical development of technology and the current state of HVAC systems. With the proper data, development of artificial intelligence models can, in theory, improve the overall optimization and reduce energy consumption This paper will provide a review of HVAC history and the key concepts around the usefulness of using AI from previous research conducted in this field of study.
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Aswad Mohammed, Subhi, Osama Ali Awad, and Abdulkareem Merhej Radhi. "Optimization of energy consumption and thermal comfort for intelligent building management system using genetic algorithm." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (December 1, 2020): 1613. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1613-1625.
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This paper presents a design, simulation and performance evaluation of an optimized model for the Heating, Ventilation and Air-Conditioning (HVAC) systems using intelligent control algorithm. Fanger’s comfort method and genetic algorithms were used to obtain the optimal and initial values. The heat transmission coefficient between internal and external environments were determined depending on several inputs and factors acquired via supervisory control and data acquisition (SCADA) system sensors. The main feature of the real-time model is the prediction of the internal buildings environment, in order to control HVAC system for indoor environment and to utilize the optimum power consumed depending on optimized air temperature value. The predicted air temperature value and Predictive Mean Vote (PMV) value was applied using intelligent algorithm to obtain an optimal comfort level of the air temperature. The optimized air temperature value can be used in HVAC system controller to ensure that the temperature of indoor can reach a specific value after a known period of time. The use of genetic algorithm (GE) ensures that the used power is well below its peak value and maintains the comfort of the user’s environment.
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Ignjatović, Marko G., Bratislav D. Blagojević, Mirko M. Stojiljković, Aleksandar S. Anđelković, Milena B. Blagojević, and Dejan M. Mitrović. "Energy performance of air conditioned buildings based on short-term weather forecast." E3S Web of Conferences 111 (2019): 04045. http://dx.doi.org/10.1051/e3sconf/201911104045.
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One of the possible ways to improve balance between building energy consumption and occupant thermal comfort in existing buildings is to use simulation-assisted operation of HVAC systems. Simulation-assisted operation can be formulated as a type of operation that implements knowledge of future disturbance acting on the building and that enables operating the systems in such a way to fulfill given goals, which in nature can often be contradictory. The most important future conditions on building energy consumption are weather parameters and occupant behavior and expectations of thermal environment. In order to achieve this type of operation, optimization methods must be applied. Methodology to create HVAC system operation strategies on a daily basis is presented. Methodology is based on using building energy performance simulation software EnergyPlus, available weather data, global sensitivity analysis, and custom developed software with particle swarm optimization method applied over the moving horizon. Global sensitivity analysis is used in order to reduce number of independent variables for the optimization process. The methodology is applied to office part of real combined-type building located in Niš, Serbia. Use of sensitivity analysis shows that the reduced number of independent variables for the optimization would lead to similar thermal comfort and energy consumption, with significant computer runtime reduction.
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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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Liang, Xiaodan, and Dan Wang. "Research on Key Technologies for Green Transformation of Existing Residential Buildings in Dense Urban Areas." E3S Web of Conferences 512 (2024): 01017. http://dx.doi.org/10.1051/e3sconf/202451201017.
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To achieve green and environmentally friendly urban construction, it is proposed to study a key technology for the green transformation of existing residential buildings in densely populated urban areas. Firstly, using a time series autoregressive model, achieve energy consumption prediction for HVAC heating. Secondly, based on multi-objective particle swarm optimization algorithm, energy-saving control of HVAC systems was achieved. Finally, the effectiveness of the proposed key technology was verified through experiments. The results of this article indicate that the green transformation of existing residential buildings in densely populated urban areas is necessary and feasible, and the application of key technologies can effectively reduce energy consumption and improve the quality of the living environment.
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Samadi, Nasim, and Mahdi Shahbakhti. "Energy Efficiency and Optimization Strategies in a Building to Minimize Airborne Infection Risks." Energies 16, no. 13 (June 26, 2023): 4960. http://dx.doi.org/10.3390/en16134960.
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Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in either increasing or decreasing the risk of airborne disease transmission. High ventilation, for instance, is a common method used to control and reduce the infection risk of airborne diseases such as COVID-19. On the other hand, high ventilation will increase energy consumption and cost. This paper proposes an optimal HVAC controller to assess the trade-off between energy consumption and indoor infection risk of COVID-19. To achieve this goal, a nonlinear model predictive controller (NMPC) is designed to control the HVAC systems of a university building to minimize the risk of COVID-19 transmission while reducing building energy consumption. The NMPC controller uses dynamic models to predict future outputs while meeting system constraints. To this end, a set of dynamic physics-based models are created to capture heat transfer and conservation of mass, which are used in the NMPC controller. Then, the developed models are experimentally validated by conducting experiments in the ETLC building at the University of Alberta, Canada. A classroom in the building is equipped with a number of sensors to measure indoor and outdoor environmental parameters such as temperature, relative humidity, and CO2 concentration. The validation results show that the model can predict room temperature and CO2 concentration by 0.8%, and 2.4% mean absolute average errors, respectively. Based on the validated models, the NMPC controller is designed to calculate the optimal airflow and supply air temperature for every 15 min. The results for real case studies show that the NMPC controller can reduce the infection risk of COVID-19 transmission below 1% while reducing energy consumption by 55% when compared to the existing building controller.
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Kim, Nam-Kyu, Myung-Hyun Shim, and Dongjun Won. "Building Energy Management Strategy Using an HVAC System and Energy Storage System." Energies 11, no. 10 (October 10, 2018): 2690. http://dx.doi.org/10.3390/en11102690.
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Recently, a worldwide movement to reduce greenhouse gas emissions has emerged, and includes efforts such as the Paris Agreement in 2015. To reduce greenhouse gas emissions, it is important to reduce unnecessary energy consumption or use environmentally-friendly energy sources and consumer products. Many studies have been performed on building energy management systems and energy storage systems (ESSs), which are aimed at efficient energy management. Herein, a heating, ventilation, and air-conditioning (HVAC) system peak load reduction algorithm and an ESS peak load reduction algorithm are proposed. First, an HVAC system accounts for the largest portion of building energy consumption. An HVAC system operates by considering the time-of-use price. However, because the indoor temperature is constantly changing with time, load shifting can be expected only immediately prior to use. Therefore, the primary objective is to reduce the operating time by changing the indoor temperature constraint at the forecasted peak time. Next, numerous research initiatives on ESSs are ongoing. In this study, we aim to systematically design the peak load reduction algorithm of ESS. The structure is designed such that the algorithm can be applied by distinguishing between the peak and non-peak days. Finally, the optimization scheduling simulation is performed. The result shows that the electricity price is minimized by peak load reduction and electricity usage reduction. The proposed algorithm is verified through MATLAB simulations.
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Kampelis, Nikolaos, Nikolaos Sifakis, Dionysia Kolokotsa, Konstantinos Gobakis, Konstantinos Kalaitzakis, Daniela Isidori, and Cristina Cristalli. "HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response." Energies 12, no. 11 (June 7, 2019): 2177. http://dx.doi.org/10.3390/en12112177.
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Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met.
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Metsä-Eerola, Iivo, Jukka Pulkkinen, Olli Niemitalo, and Olli Koskela. "On Hourly Forecasting Heating Energy Consumption of HVAC with Recurrent Neural Networks." Energies 15, no. 14 (July 12, 2022): 5084. http://dx.doi.org/10.3390/en15145084.
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Optimizing the heating, ventilation, and air conditioning (HVAC) system to minimize district heating usage in large groups of managed buildings is of the utmost important, and it requires a machine learning (ML) model to predict the energy consumption. An industrial use case to reach large building groups is restricted to using normal operational data in the modeling, and this is one reason for the low utilization of ML in HVAC optimization. We present a methodology to select the best-fitting ML model on the basis of both Bayesian optimization of black-box models for defining hyperparameters and a fivefold cross-validation for the assessment of each model’s predictive performance. The methodology was tested in one case study using normal operational data, and the model was applied to analyze the energy savings in two different practical scenarios. The software for the modeling is published on GitHub. The results were promising in terms of predicting the energy consumption, and one of the scenarios also showed energy saving potential. According to our research, the GitHub software for the modeling is a good candidate for predicting the energy consumption in large building groups, but further research is needed to explore its scalability for several buildings.
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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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Divyesh, VR, Chinmay Sahu, Velswamy Kirubakaran, TK Radhakrishnan, and Muralidharan Guruprasath. "Energy optimization using metaheuristic bat algorithm assisted controller tuning for industrial and residential applications." Transactions of the Institute of Measurement and Control 40, no. 7 (May 3, 2017): 2310–21. http://dx.doi.org/10.1177/0142331217701538.
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The advent of model based control provides optimization and constrained control capabilities that can be tailored to specific goals. Metaheuristic algorithms are being researched in various fields owing to their efficiency in providing global optimization. In this paper, both model regression and energy efficiency based controller tuning are attempted using the chaotic bat algorithm (CBA). Two sets of plant data, one from an industrial precalciner temperature loop (PTL) and another from a domestic heating ventilation and air conditioning system (HVAC) are considered. Explicit model predictive control is designed. Minimizing the energy consumption (HVAC) and coal feed rate (PTL) by tuning the controllers is attempted. Results indicate that CBA can be successfully deployed in both regression and achieving control objectives as observed from the case studies.
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Kelvin Wijaya, Timotius, Sholahudin, M. Idrus Alhamid, Kiyoshi Saito, and N. Nasruddin. "Dynamic optimization of chilled water pump operation to reduce HVAC energy consumption." Thermal Science and Engineering Progress 36 (December 2022): 101512. http://dx.doi.org/10.1016/j.tsep.2022.101512.
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Kurniawan, Andre, Nanang Qosim, Remon Lapisa, Zainal Abadi, and Jasman Jasman. "The Optimization of Building Energy Consumption in Universitas Negeri Padang Using Building Energy Simulation Program." Teknomekanik 4, no. 1 (May 27, 2021): 30–35. http://dx.doi.org/10.24036/teknomekanik.v4i1.9672.
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Energy consumption of a building is one of the biggest sources of energy use today. Green Building Comitte Indonesia (GBCI) has launched a concept of energy consumption saving in a nationally standard building. Audit Building energy audit is the way to know how actual building energy consumption is and find alternative solution to decrease energy consumption in order to fulfill the energy saving building criteria. Two types of HVAC systems will be run in the EnergyPlus simulation, split AC and central AC. The previous research proved that central AC is better than split AC system for energy saving in the building with 20 floors. The simulation results show that by using a certain energy system, a more efficient energy system will be achieved and can still maintain the comfort of the room at a temperature of 24 °C and relative humidity according to the Green Building Indonesia standard reference.
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Es-Sakali, Niima, Samir Idrissi Kaitouni, Imad Ait Laasri, Mohamed Oualid Mghazli, Moha Cherkaoui, and Jens Pfafferott. "Building energy efficiency improvement using multi-objective optimization for heating and cooling VRF thermostat setpoints." E3S Web of Conferences 396 (2023): 03032. http://dx.doi.org/10.1051/e3sconf/202339603032.
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The variable refrigerant flow system is one of the best heating, ventilation, and air conditioning systems (HVAC) thanks to its ability to provide thermal comfort inside buildings. But, at the same time, these systems are considered one of the most energy-consuming systems in the building sector. Thus, it is crucial to well size the system according to the building’s cooling and heating needs and the indoor temperature fluctuations. Although many researchers have studied the optimization of the building energy performance considering heating or cooling needs, using air handling units, radiant floor heating, and direct expansion valves, few studies have considered the use of multi-objective optimization using only the thermostat setpoints of VRF systems for both cooling and heating needs. Thus, the main aim of this study is to conduct a sensitivity analysis and a multi-objective optimization strategy for a residential building containing a variable refrigerant flow system, to evaluate the effect of the building performance on energy consumption and improve the building energy efficiency. The numerical model was based on the EnergyPlus, jEPlus, and jEPlus+EA simulation engines. The approach used in this paper has allowed us to reach significant quantitative energy saving by varying the cooling and heating setpoints and scheduling scenarios. It should be stressed that this approach could be applied to several HVAC systems to reduce energy-building consumption.
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Huong, Nguyen Lan, Nguyen Viet Anh, Dang Thi Thanh Huyen, Tran Hoai Son, and Dinh Viet Cuong. "Optimization to water supply system design and operation scheme in high rise buildings." Journal of Science and Technology in Civil Engineering (STCE) - NUCE 12, no. 3 (April 30, 2018): 123–31. http://dx.doi.org/10.31814/stce.nuce2018-12(3)-12.
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Greenhouse Gas emission from high-rise buildings has been increasing mainly due to excessive energy consumption of the HVAC system, structural system and electrical system. Electricity consumption for pump system accounts for 15% of total electricity usage in building. Therefore the reduction of electricity in operation is crucial to the overall reduction of GHGs in urban areas. In this study, a lab-scale experiment was conducted to test the electricity consumption in applying different design approaches; the energy efficiency of the system was calculated. Finally, this study proposes the advanced water supply design scheme to reduce electricity consumption of the pump system.
Article history: Received 12 March 2018, Revised 03 April 2018, Accepted 27 April 2018
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Kim, Dongsu, Jongman Lee, Sunglok Do, Pedro J. Mago, Kwang Ho Lee, and Heejin Cho. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends." Energies 15, no. 19 (October 1, 2022): 7231. http://dx.doi.org/10.3390/en15197231.
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Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems’ performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI’s and Elsevier’s databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields.
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Wandy, Yose, Marcus Vogt, Rushit Kansara, Clemens Felsmann, and Christoph Herrmann. "Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing." Energies 14, no. 21 (November 3, 2021): 7271. http://dx.doi.org/10.3390/en14217271.
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The alternative control concept using emission from the machine has the potential to reduce energy consumption in HVAC systems. This paper reports on a study of alternative inputs for a control system of HVAC using machine learning algorithms, based on data that are gathered in a welding area of an automotive factory. A data set of CO2, fine dust, temperatures and air velocity was logged using continuous and gravimetric measurements during two typical production weeks. The HVAC system was reduced gradually each day to trigger fluctuations of emission. The data were used to train and test various machine learning models using different statistical indices, consequently to choose a best fit model. Different models were tested and the Long Short-Term Memory model showed the best result, with 0.821 discrepancy on R2. The gravimetric samples proved that the reduction of air exchange rate does not correlate to escalation of fine dust linearly, which means one cannot rely on just gravimetric samples for HVAC system optimization. Furthermore, by using machine learning algorithms, this study shows that by using commonly available low cost sensors in a production hall, it is possible to correlate fine dust data cost effectively and reduce electricity consumption of the HVAC.
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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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König, Adrian, Sebastian Mayer, Lorenzo Nicoletti, Stephan Tumphart, and Markus Lienkamp. "The Impact of HVAC on the Development of Autonomous and Electric Vehicle Concepts." Energies 15, no. 2 (January 9, 2022): 441. http://dx.doi.org/10.3390/en15020441.
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Automation and electrification are changing vehicles and mobility. Whereas electrification is mainly changing the powertrain, automation enables the rethinking of the vehicle and its applications. The actual driving range is an important requirement for the design of automated and electric vehicles, especially if they are part of a fleet. To size the battery accordingly, not only the consumption of the powertrain has to be estimated, but also that of the auxiliary users. Heating Ventilation and Air Conditioning (HVAC) is one of the biggest auxiliary consumers. Thus, a variable HVAC model for vehicles with electric powertrain was developed to estimate the consumption depending on vehicle size and weather scenario. After integrating the model into a tool for autonomous and electric vehicle concept development, various vehicle concepts were simulated in different weather scenarios and driving cycles with the HVAC consumption considered for battery sizing. The results indicate that the battery must be resized significantly depending on the weather scenario to achieve the same driving ranges. Furthermore, the percentage of HVAC consumption is in some cases higher than that of the powertrain for urban driving cycles, due to lower average speeds. Thus, the HVAC and its energy demand should especially be considered in the development of autonomous and electric vehicles that are primarily used in cities.
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Short, Michael, Sergio Rodriguez, Richard Charlesworth, Tracey Crosbie, and Nashwan Dawood. "Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach." Energies 12, no. 22 (November 13, 2019): 4320. http://dx.doi.org/10.3390/en12224320.
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Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work.
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Yang, Xinyu, Ying Ji, Jiefan Gu, and Menghan Niu. "An Electricity Consumption Disaggregation Method for HVAC Terminal Units in Sub-Metered Buildings Based on CART Algorithm." Buildings 13, no. 4 (April 6, 2023): 967. http://dx.doi.org/10.3390/buildings13040967.
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Obtaining reliable and detailed energy consumption information about building service (BS) systems is an essential prerequisite for identifying energy-saving potential and improving energy efficiency of a building. Therefore, in recent years, energy sub-metering systems have been widely implemented in public buildings in China. A majority of electrical systems and equipment can be directly metered. However, in actual sub-metering systems, the terminal units of heating, ventilation and air conditioning (HVAC) systems, such as fan coils, air handling units and so on, are often mixed with the lighting-plug circuit. This mismatch between theoretical sub-metering systems and actual electricity supply circuits constitutes a lot of challenges in BS system management and control optimization. This study proposed an indirect method to disaggregate the energy consumption of HVAC terminal units from mixed sub-metering data based on the CART algorithm. This method was demonstrated in two buildings in Shanghai. The case study results show that the weighted mean absolute percentage errors (WMAPE) are within 5% and 15% during working hours in the cooling and heating seasons, respectively.
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Moayedi, Hossein, and Amir Mosavi. "Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings." Energies 14, no. 5 (March 1, 2021): 1331. http://dx.doi.org/10.3390/en14051331.
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A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).
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Lahlou, Anas, Florence Ossart, Emmanuel Boudard, Francis Roy, and Mohamed Bakhouya. "Optimal Management of Thermal Comfort and Driving Range in Electric Vehicles." Energies 13, no. 17 (August 31, 2020): 4471. http://dx.doi.org/10.3390/en13174471.
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The HVAC system represents the main auxiliary load in battery-powered electric vehicles (BEVs) and requires efficient control approaches that balance energy saving and thermal comfort. On the one hand, passengers always demand more comfort, but on the other hand the HVAC system consumption strongly impacts the vehicle’s driving range, which constitutes a major concern in BEVs. In this paper, a thermal comfort management approach that optimizes the thermal comfort while preserving the driving range during a trip is proposed. The electric vehicle is first modeled together with the HVAC and the passengers’ thermo-physiological behavior. Then, the thermal comfort management issue is formulated as an optimization problem solved by dynamic programing. Two representative test-cases of hot climates and traffic situations are simulated. In the first one, the energetic cost and ratio of improved comfort is quantified for different meteorological and traffic conditions. The second one highlights the traffic situation in which a trade-off between the driving speed and thermal comfort is important. A large number of weather and traffic situations are simulated and results show the efficiency of the proposed approach in minimizing energy consumption while maintaining a good comfort.
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Talei, Hanaa, Driss Benhaddou, Carlos Gamarra, Houda Benbrahim, and Mohamed Essaaidi. "Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning." Energies 14, no. 19 (September 23, 2021): 6042. http://dx.doi.org/10.3390/en14196042.
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The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building’s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings.
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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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Jalal, Bareq Musaab, and Rashid Hamid Al-Rubayi. "Energy management strategy with smart building control system to reduction electrical load using ANN." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3188–200. http://dx.doi.org/10.11591/eei.v11i6.4087.
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Buildings have long been large energy consumers, and inadequate control of heating, ventilation, and air conditioning kinds of variable refrigeration flow (HVAC-VRF) and lighting systems. To reduce energy consumption by using a smart building control system (SBCS) in a building was created using occupant control, daylight sensors, weather condition variations, load consumed, and changes in solar power. The model was tested using MATLAB/Simulink, and it was then utilized to investigate the impact of an integrated system on energy usage based on two scenarios. The first scenario was tested in a simulation of building occupant behavior, meteorological variables, daylight sensors, temperature, and load control. This resulted in energy savings for the HVAC system (23% on summer days and 16% on winter days), and lighting system energy savings (22% on summer days and 15% on winter days). In the second scenario, the building was tested to integrate PV system power with load consumption by using the artificial neural network (ANN) algorithm to manage building load consumption by PV, grid, and diesel generator. As a result, the energy savings were 56% on a summer day and 65% on a winter day of the combined energy utilized by the HVAC and lights.
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Andrés, Eva, Manuel Pegalajar Cuéllar, and Gabriel Navarro. "On the Use of Quantum Reinforcement Learning in Energy-Efficiency Scenarios." Energies 15, no. 16 (August 19, 2022): 6034. http://dx.doi.org/10.3390/en15166034.
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In the last few years, deep reinforcement learning has been proposed as a method to perform online learning in energy-efficiency scenarios such as HVAC control, electric car energy management, or building energy management, just to mention a few. On the other hand, quantum machine learning was born during the last decade to extend classic machine learning to a quantum level. In this work, we propose to study the benefits and limitations of quantum reinforcement learning to solve energy-efficiency scenarios. As a testbed, we use existing energy-efficiency-based reinforcement learning simulators and compare classic algorithms with the quantum proposal. Results in HVAC control, electric vehicle fuel consumption, and profit optimization of electrical charging stations applications suggest that quantum neural networks are able to solve problems in reinforcement learning scenarios with better accuracy than their classical counterpart, obtaining a better cumulative reward with fewer parameters to be learned.
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Qin, Haosen, Zhen Yu, Tailu Li, Xueliang Liu, and Li Li. "Heating Control Strategy Based on Dynamic Programming for Building Energy Saving and Emission Reduction." International Journal of Environmental Research and Public Health 19, no. 21 (October 29, 2022): 14137. http://dx.doi.org/10.3390/ijerph192114137.
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Finding the optimal balance between end-user’s comfort, lifestyle preferences and the cost of the heating, ventilation and air conditioning (HVAC) system, which requires intelligent decision making and control. This paper proposes a heating control method for HVAC based on dynamic programming. The method first selects the most suitable modeling approach for the controlled building among three machine learning modeling techniques by means of statistical performance metrics, after which the control of the HVAC system is described as a constrained optimization problem, and the action of the controller is given by solving the optimization problem through dynamic programming. In this paper, the variable ‘ thermal energy storage in building ‘ is introduced to solve the problem that dynamic programming is difficult to obtain the historical state of the building due to the requirement of no aftereffect, while the room temperature and the remaining start hours of the Primary Air Unit are selected to describe the system state through theoretical analysis and trial and error. The results of the TRNSYS/Python co-simulation show that the proposed method can maintain better indoor thermal environment with less energy consumption compared to carefully reviewed expert rules. Compared with expert rule set ‘baseline-20 °C’, which keeps the room temperature at the minimum comfort level, the proposed control algorithm can save energy and reduce emissions by 35.1% with acceptable comfort violation.
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Si, Qiang, Yougang Peng, Qiuli Jin, Yuan Li, and Hao Cai. "Multi-Objective Optimization Research on the Integration of Renewable Energy HVAC Systems Based on TRNSYS." Buildings 13, no. 12 (December 8, 2023): 3057. http://dx.doi.org/10.3390/buildings13123057.
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Well-designed passive buildings can drastically reduce building energy consumption, and optimal design of air conditioning systems is the key to achieving low operating energy consumption in near-zero energy buildings. TRNSYS was used to build the simulation model for a near-zero-energy building and its air conditioning system in Beijing. The Taguchi method was used to sort the design parameters that affect system performance according to the degree of influence and find the best combination of design parameters to optimize the system, which increased the solar fraction of the system by 4.6% and reduced the annual operating energy consumption by 7.32%. For the optimized system, a multi-objective optimization function of the life cycle costs and carbon emissions was established. By comparing the energy consumption, life cycle costs, and carbon emissions of the air conditioning system under different system configurations, optimal configuration solutions under different design target weights were obtained. It was found that using a ground source heat pump system + solar collector system had better energy-savings benefits, but the operating costs were slightly higher. The application of absorption refrigeration can reduce the system operating costs but will increase the initial investment. The best economic benefits were achieved using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system for cooling in summer, and the best environmental benefits were obtaining using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system + solar absorption refrigeration system in summer, which provides a reference for the optimization design and research of air conditioning systems in near-zero energy buildings.
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Wrana, Jan, Wojciech Struzik, Katarzyna Jaromin-Gleń, and Piotr Gleń. "FCH HVAC Honeycomb Ring Network—Transition from Traditional Power Supply Systems in Existing and Revitalized Areas." Energies 16, no. 24 (December 8, 2023): 7965. http://dx.doi.org/10.3390/en16247965.
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This paper discusses the application of a new honeycomb FCH HVAC (Free Cooling and Heating System, Heating, Ventilation, and Air Conditioning) ring network technology that reduces the primary energy consumption in existing infrastructure. The aim of the research is to evaluate the cost-environmental viability of upgrading the technical infrastructure and moving from traditional to newly designed green systems built on renewable energy sources. The results show that the energy capacity stored in groundwater is equivalent to 65% of building demand, resulting in a 60% reduction in CO2 emissions compared to a traditional HVAC system. The solution reduces the consumption of natural resources by using renewable energy sources with horizontal heat exchangers arranged in independent ring configurations.
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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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Segala, Giacomo, Roberto Doriguzzi-Corin, Claudio Peroni, Matteo Gerola, and Domenico Siracusa. "EECO: An AI-Based Algorithm for Energy-Efficient Comfort Optimisation." Energies 16, no. 21 (October 29, 2023): 7334. http://dx.doi.org/10.3390/en16217334.
Full textAbstract:
Environmental comfort takes a central role in the well-being and health of people. In modern industrial, commercial, and residential buildings, passive energy sources (such as solar irradiance and heat exchangers) and heating, ventilation, and air conditioning (HVAC) systems are usually employed to achieve the required comfort. While passive strategies can effectively enhance the livability of indoor spaces with limited or no energy cost, active strategies based on HVAC machines are often preferred to have direct control over the environment. Commonly, the working parameters of such machines are manually tuned to a fixed set point during working hours or throughout the whole day, leading to inefficiencies in terms of comfort and energy consumption. Albeit effective, previous works that tackle the comfort–energy tradeoff are tailored to the specific environment under study (in terms of geometry, characteristics of the building, etc.) and thus cannot be applied on a large industrial scale. We address the problem from a different angle and propose an adaptive and practical solution for comfort optimisation. It does not require the intervention of expert personnel or any customisations around the environment while it implicitly analyses the influence of different agents (e.g., passive phenomena) on the monitored parameters. A convolutional neural network (CNN) predicts the long-term impact on thermal comfort and energy consumption of a range of possible actuation strategies for the HVAC system. The decision on the best HVAC settings is taken by choosing the combination of ON/OFF and set point (SP), which optimises thermal comfort and, at the same time, minimises energy consumption. We validate our solution in a real-world scenario and through software simulations, providing a performance comparison against the fixed set point strategy and a greedy approach. The evaluation results show that our solution achieves the desired thermal comfort while reducing the energy footprint by up to approximately 16% in a real environment.
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Sierla, Seppo, Heikki Ihasalo, and Valeriy Vyatkin. "A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems." Energies 15, no. 10 (May 11, 2022): 3526. http://dx.doi.org/10.3390/en15103526.
Full textAbstract:
Reinforcement learning has emerged as a potentially disruptive technology for control and optimization of HVAC systems. A reinforcement learning agent takes actions, which can be direct HVAC actuator commands or setpoints for control loops in building automation systems. The actions are taken to optimize one or more targets, such as indoor air quality, energy consumption and energy cost. The agent receives feedback from the HVAC systems to quantify how well these targets have been achieved. The feedback is captured by a reward function designed by the developer of the reinforcement learning agent. A few reviews have focused on the reward aspect of reinforcement learning applications for HVAC. However, there is a lack of reviews that assess how the actions of the reinforcement learning agent have been formulated, and how this impacts the possibilities to achieve various optimization targets in single zone or multi-zone buildings. The aim of this review is to identify the action formulations in the literature and to assess how the choice of formulation impacts the level of abstraction at which the HVAC systems are considered. Our methodology involves a search string in the Web of Science database and a list of selection criteria applied to each article in the search results. For each selected article, a three-tier categorization of the selected articles has been performed. Firstly, the applicability of the approach to buildings with one or more zones is considered. Secondly, the articles are categorized by the type of action taken by the agent, such as a binary, discrete or continuous action. Thirdly, the articles are categorized by the aspects of the indoor environment being controlled, namely temperature, humidity or air quality. The main result of the review is this three-tier categorization that reveals the community’s emphasis on specific HVAC applications, as well as the readiness to interface the reinforcement learning solutions to HVAC systems. The article concludes with a discussion of trends in the field as well as challenges that require further research.
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Borodinecs, Anatolijs, Jurgis Zemitis, and Arturs Palcikovskis. "HVAC System Control Solutions Based on Modern IT Technologies: A Review Article." Energies 15, no. 18 (September 14, 2022): 6726. http://dx.doi.org/10.3390/en15186726.
Full textAbstract:
As energy consumption for building engineering systems is a major part of the total energy spent, it is necessary to reduce it. This leads to the need for the development of new solutions for the control of heating, ventilation, and conditioning (HVAC) systems that are responsive to humans and their demands. In this review article, the existing research and technology advancements of the modern technologies of computer vision and neural networks for application in HVAC control systems are studied. Objectives such as human detection and location, human activity monitoring, skin temperature detection, and clothing level detection systems are important for the operation of precise, high-tech HVAC systems. This article tries to compile the latest achievements and principal solutions on how this information is acquired. Moreover, it how parameters such as indoor air quality (IAQ), variable air volume ventilation, computer vision, metabolic rate, and human clothing isolation can affect final energy consumption is studied. The research studies discussed in this review article have been tested in real application scenarios and prove the benefits of using a particular technology in ventilation systems. As a result, the modernized control systems have shown advantages over the currently applied typical non-automated systems by providing higher IAQ and reducing unnecessary energy consumption.