Journal articles on the topic 'Commercial buildings Heating and ventilation Mathematical models'
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1
Szász, Cs. "Air-source heat pump LabView-based model development for NZEB applications." International Review of Applied Sciences and Engineering 5, no. 1 (June 1, 2014): 59–66. http://dx.doi.org/10.1556/irase.5.2014.1.8.
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Abstract A net zero-energy building (NZEB) is considered as a resident or commercial building where the energy needs are covered by using locally available renewable energy sources and technologies. Various types of heat pumps are widely used energy conversion systems for NZEB strategies implementation. This paper is focused on the development of a novel LabView-based model for an air-source heat pump system that absorbs heat from outside air and releases it inside the building as domestic hot water supply or room's space heating by using hot water-filled fan-coils. In the first research steps the mathematical background of the considered heat pump system has been developed. Then the LabView-based software implementation of the air-source heat pump and entire heating circuit model is unfolded and presented. The result is a versatile and powerful graphical software toolkit, suitable to simulate the complex heating, ventilation and air-conditioning processes in net-zero energy buildings and to perform energy balance performance evaluations. Beside the elaborated mathematical models, a concrete software implementation example and measurement data is provided in the paper. Last but not least, the proposed original model offers a feasible solution for future developments and research in NZEB applications modeling and simulation purposes.
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Faddel, Samy, Guanyu Tian, and Qun Zhou. "Decentralized Management of Commercial HVAC Systems." Energies 14, no. 11 (May 24, 2021): 3024. http://dx.doi.org/10.3390/en14113024.
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With the growth of commercial building sizes, it is more beneficial to make them “smart” by controlling the schedule of the heating, ventilation, and air conditioning (HVAC) system adaptively. Single-building-based scheduling methods are more focused on individual interests and usually result in overlapped schedules that can cause voltage deviations in their microgrid. This paper proposes a decentralized management framework that is able to minimize the total electricity costs of a commercial microgrid and limit the voltage deviations. The proposed scheme is a two-level optimization where the lower level ensures the thermal comfort inside the buildings while the upper level consider system-wise constraints and costs. The decentralization of the framework is able to maintain the privacy of individual buildings. Multiple data-driven building models are developed and compared. The effect of the building modeling on the overall operation of coordinated buildings is discussed. The proposed framework is validated on a modified IEEE 13-bus system with different connected types of commercial buildings. The results show that coordinated optimization outperforms the commonly used commercial controller and individual optimization of buildings. The results also show that the total costs are greatly affected by the building modeling.
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Junior, Moacir José Dalmina, Jair Antonio Cruz Siqueira, Carlos Eduardo Camargo Nogueira, Samuel Nelson Melegari de Souza, and Luciene Kazue Tokura. "Optimization of Energy Efficiency and Environmental Comfort in Broiler House." Journal of Agricultural Science 12, no. 10 (September 15, 2020): 162. http://dx.doi.org/10.5539/jas.v12n10p162.
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The objective of the study was to develop a design methodology involving a mixed system for better use of natural lighting and ventilation, together with electrical heating and ventilation systems that are currently used in commercial aviaries. Two building models were analyzed, one open conventional, and the other developed specifically for this study, with a cross ventilation brise-soleil system that provided greater energy efficiency in aviaries. Subsequently, the two models were compared using Autodesk’s Revit software through the Green Building Studio, to analyze the energy consumption of buildings during the year. The results showed that the model of poultry developed for the study proved to be more efficient in relation to the model of open poultry. The proposed broiler house was 21.07% more efficient than the conventional open aviary.
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Baniyounes, Ali M., Yazeed Yasin Ghadi, Eyad Radwan, and Khalid S. Al-Olimat. "Functions of fuzzy logic based controllers used in smart building." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (June 1, 2022): 3061. http://dx.doi.org/10.11591/ijece.v12i3.pp3061-3071.
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<span>The main aim of this study is to support design and development processes of advanced fuzzy-logic-based controller for smart buildings e.g., heating, ventilation and air conditioning, heating, ventilation and air conditioning (HVAC) and indoor lighting control systems. Moreover, the proposed methodology can be used to assess systems energy and environmental performances, also compare energy usages of fuzzy control systems with the performances of conventional on/off and proportional integral derivative controller (PID). The main objective and purpose of using fuzzy-logic-based model and control is to precisely control indoor thermal comfort e.g., temperature, humidity, air quality, air velocity, thermal comfort, and energy balance. Moreover, this article present and highlight mathematical models of indoor temperature and humidity transfer matrix, uncertainties of users’ comfort preference set-points and a fuzzy algorithm.</span>
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Liu, Shuailing, Guoyuan Ma, Xiaoya Jia, Shuxue Xu, and Guoqiang Wu. "Simulation research on heat recovery system of heat pump composite pump-driven loop heat pipe." Thermal Science, no. 00 (2022): 44. http://dx.doi.org/10.2298/tsci211119044l.
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To promote energy-saving potentials of the energy recovery unit under all-year conditions, a composite system combining pump-driven loop heat pipe with heat pump was firstly proposed, and the mathematical models were established. The operating characteristics of the composite system were studied in the whole year and compared with the traditional heat pump heat recovery system. The results show that the heating capacity of the composite system is in line with the heating load in winter. Compared with the traditional heat pump system, the composite system has higher energy efficiency ratio and lower deviation degree of temperature effectiveness in the whole year. The heat pump composite pump-driven loop heat pipe heat recovery system is generally superior to similar system reported in literatures, which indicates that it can replace heat pump system in buildings ventilation.
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Pandey, Kamal, Bhaskar Basu, and Sandipan Karmakar. "An Efficient Decision-Making Approach for Short Term Indoor Room Temperature Forecasting in Smart Environment: Evidence from India." International Journal of Information Technology & Decision Making 20, no. 02 (March 2021): 733–74. http://dx.doi.org/10.1142/s0219622021500164.
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“Smart cities” start with “Smart Buildings” that improve the quality of urban services while ensuring sustainability. The current scenario in India reveals that the corporate and residential building structures are incorporating various self-sustainable techniques. Out of the multiple factors governing the comfort of smart buildings, indoor room temperature is an important one, since it drives the need of cooling or heating through controlling systems. Around one-third of total energy consumption of commercial buildings in India is attributed to Heating, Ventilation and Air Conditioning (HVAC) systems. Accurate prediction of indoor room temperature helps in creating an efficient equilibrium between energy consumption and comfort level of the building, thus providing opportunities for efficient decision making for energy optimization. Considering Indian climatic and geographical conditions, this paper proposes an efficient decision making approach using Bayesian Dynamic Models (BDM) for short-term indoor room temperature forecasting of a corporate building structure. The results obtained from Bayesian Dynamic linear model, using Expectation Maximization (EM) algorithm, have been compared to standard Auto Regressive Integrated Moving Average (ARIMA) model, and have been found to be more accurate. Forecasting of indoor room temperature is a highly nonlinear phenomenon, so to further improve the accuracy of the linear models, a hybrid modeling approach has been proposed. The inclusion of state-of-the-art nonlinear models such as Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) improves the forecasting accuracy of the linear models significantly. Results show that the hybrid model obtained using BDM and ANN is the best fit model.
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Katipamula, S., T. A. Reddy, and D. E. Claridge. "Multivariate Regression Modeling." Journal of Solar Energy Engineering 120, no. 3 (August 1, 1998): 177–84. http://dx.doi.org/10.1115/1.2888067.
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An empirical or regression modeling approach is simple to develop and easy to use compared to detailed hourly simulations of energy use in commercial buildings. Therefore, regression models developed from measured energy data are becoming an increasingly popular method for determining retrofit savings or identifying operational and maintenance (O&M) problems. Because energy consumption in large commercial buildings is a complex function of climatic conditions, building characteristics, building usage, system characteristics and type of heating, ventilation, and air conditioning (HVAC) equipment used, a multiple linear regression (MLR) model provides better accuracy than a single-variable model for modeling energy consumption. Also, when hourly monitored data are available, an issue which arises is what time resolution to adopt for regression models to be most accurate. This paper addresses both these topics. This paper reviews the literature on MLR models of building energy use, describes the methodology to develop MLR models, and highlights the usefulness of MLR models as baseline models and in detecting deviations in energy consumption resulting from major operational changes. The paper first develops the functional basis of cooling energy use for two commonly used HVAC systems: dual-duct constant volume (DDCV) and dual-duct variable air volume (DDVAV). Using these functional forms, the cooling energy consumption in five large commercial buildings located in central Texas were modeled at monthly, daily, hourly, and hour-of-day (HOD) time scales. Compared to the single-variable model (two-parameter model with outdoor dry-bulb as the only variable), MLR models showed a decrease in coefficient of variation (CV) between 10 percent to 60 percent, with an average decrease of about 33 percent, thus clearly indicating the superiority of MLR models. Although the models at the monthly time scale had higher coefficient of determination (R2) and lower CV than daily, hourly, and HOD models, the daily and HOD models proved more accurate at predicting cooling energy use.
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Lazarevic, Sladjana, Velimir Congradac, Aleksandar Andjelkovic, Miroslav Kljajic, and Zeljko Kanovic. "District heating substation elements modeling for the development of the real-time model." Thermal Science 23, no. 3 Part B (2019): 2061–70. http://dx.doi.org/10.2298/tsci181226031l.
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In the heating system of residential and commercial buildings, heating substation has an important role because it is used as a separator between primary and secondary supply sides. In this paper, we focused on the development of the simulation model of all heating substation elements, which influence the change of relevant parameters: water temperature, flow, and pressure. The primary objective of the paper is to analyze and develop mathematical models of the heat exchanger, control valve, three-way valve and frequency-regulated centrifugal pump that are configurable and generic as much as possible, so they can be used for the development of the model that could operate in real time. A real-time model could be used as a suitable replacement for an actual physical system in the process of testing and improvement of the control system performance. Different elements setups of district heating substation are considered, and the modelbased simulation is presented for one of them.
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Merabet, Ghezlane Halhoul, Mohamed Essaaidi, and Driss Benhaddou. "A dynamic model for human thermal comfort for smart building applications." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 4 (July 28, 2019): 472–83. http://dx.doi.org/10.1177/0959651819865795.
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Thermal comfort is closely related to the evaluation of heating, ventilation, and air conditioning systems. It can be seen as the result of the perception of the occupants of a given environment, and it is the product of the interaction of a number of personal and environmental factors. Otherwise, comfort issues still do not play an important role in the daily operation of commercial buildings. However, in the workplace, local quality effects, in addition to the health, the productivity that has a significant impact on the performance of the activities. In this regard, researchers have conducted, for decades, investigations related to thermal comfort and indoor environments, which includes developing models and indices through experimentations to establish standards to evaluate comfort and factors and set-up parameters for heating, ventilation, and air conditioning systems. However, to our best knowledge, most of the research work reported in the literature deals only with parameters that are not dynamically tracked. This work aims to propose a prototype for comfort measuring through a wireless sensor network and then presenting a model for thermal comfort prediction. The developed model can be used to set up a heating, ventilation, and air conditioning system to meet the expected comfort level. In particular, the obtained results show that there is a strong correlation between users’ comfort and variables such as age, gender, and body mass index as a function of height and weight.
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Yayla, Alperen, Kübra Sultan Świerczewska, Mahmut Kaya, Bahadır Karaca, Yusuf Arayıcı, Yunus Emre Ayözen, and Onur Behzat Tokdemir. "Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings." Sustainability 14, no. 23 (December 2, 2022): 16107. http://dx.doi.org/10.3390/su142316107.
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Buildings are responsible for almost half of the world’s energy consumption, and approximately 40% of total building energy is consumed by the heating ventilation and air conditioning (HVAC) system. The inability of traditional HVAC controllers to respond to sudden changes in occupancy and environmental conditions makes them energy inefficient. Despite the oversimplified building thermal response models and inexact occupancy sensors of traditional building automation systems, investigations into a more efficient and effective sensor-free control mechanism have remained entirely inadequate. This study aims to develop an artificial intelligence (AI)-based occupant-centric HVAC control mechanism for cooling that continually improves its knowledge to increase energy efficiency in a multi-zone commercial building. The study is carried out using two-year occupancy and environmental conditions data of a shopping mall in Istanbul, Turkey. The research model consists of three steps: prediction of hourly occupancy, development of a new HVAC control mechanism, and comparison of the traditional and AI-based control systems via simulation. After determining the attributions for occupancy in the mall, hourly occupancy prediction is made using real data and an artificial neural network (ANN). A sensor-free HVAC control algorithm is developed with the help of occupancy data obtained from the previous stage, building characteristics, and real-time weather forecast information. Finally, a comparison of traditional and AI-based HVAC control mechanisms is performed using IDA Indoor Climate and Energy (ICE) simulation software. The results show that applying AI for HVAC operation achieves savings of a minimum of 10% energy consumption while providing a better thermal comfort level to occupants. The findings of this study demonstrate that the proposed approach can be a very advantageous tool for sustainable development and also used as a standalone control mechanism as it improves.
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Fromreide, M., and I. Henne. "Smart control of HVAC based on measurements of indoor radon concentration." TOS Forum 2022, no. 11 (May 27, 2022): 441. http://dx.doi.org/10.1255/tosf.172.
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Sufficient ventilation is important for creating a healthy indoor environment in both households and larger buildings (schools, offices, shops, warehouses). As the cost of heating and ventilation in large buildings is very large, modern ventilation systems apply smart control for optimizing the energy consumption. The control is typically based on temperature and/or CO2 levels, whereas other pollutants such as particulate microparticles and radon are not accounted for. Exposure to indoor radon is the second most important reason for lung cancer, with more than 200 000 casesestimated worldwide every year. The indoor radon concentration depends on many factors, from local geological conditions and weather, to building materials and natural and mechanical ventilation. Together with the Norwegian company OBEO AS, NORCE has completed a study of the indoor radon concentration at a Norwegian primary school prone to high radon levels. The study includes continuous measurements of radon, CO2 and temperature for multiple rooms for different use as well as data for the ventilation system. The aim for the project was to build knowledge for a larger research project, where the goal is to develop a control algorithm for HVAC systems which also takes radon levels into account. An important challenge is to make accurate sensors for measuring radon continuously at an affordable price for implementation in complex buildings. Generally, sensors that can measure at high frequency with good accuracy is very expensive. Thus, developing algorithms and mathematical models to treat the data from low-cost sensors can be an adequate tool for large systems/buildings. One must also account for the positioning of the sensor. For instance, it is known that the radon concentration is better distributed in a room with mechanical ventilation than in a room without, making the position of the sensor important for better interpretation of measurement data. In the pre-project we have studied how the radon levels show large variations that are related to the ventilation operation and daily/weekly variations. The large variations show the need for a high sampling rate (minutes to hours) to capture the peaks in the concentration. We have also studied the rate of change in radon concentrations over time and have amongst discovered a noticeable delay from the start of ventilation until radon is effectively removed from the air. The rate of change is an important parameter for planning ahead and securing fresh air during the full period of presence whilst optimizing the energy cost.
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Matetić, Iva, Ivan Štajduhar, Igor Wolf, and Sandi Ljubic. "A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems." Sensors 23, no. 1 (December 20, 2022): 1. http://dx.doi.org/10.3390/s23010001.
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Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings’ energy is mostly used for heating and/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review.
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Moreno Santamaria, Belen, Fernando del Ama Gonzalo, Danielle Pinette, Roberto-Alonso Gonzalez-Lezcano, Benito Lauret Aguirregabiria, and Juan A. Hernandez Ramos. "Application and Validation of a Dynamic Energy Simulation Tool: A Case Study with Water Flow Glazing Envelope." Energies 13, no. 12 (June 19, 2020): 3203. http://dx.doi.org/10.3390/en13123203.
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The transparent materials used in building envelopes significantly contribute to heating and cooling loads of a building. The use of transparent materials requires to solve issues regarding heat gain, heat loss, and daylight. Water flow glazing (WFG), a disruptive technology, includes glazing as part of the Heating, Ventilation and Air Conditioning (HVAC) system. Water is transparent to visible wavelengths, but it captures most of the infrared solar radiation. As an alternative to fossil fuel-based HVAC systems, the absorbed energy can be transferred to the ground through borehole heat exchangers and dissipated as a means of free-cooling. Researchers of the Polytechnic University of Madrid have developed a software tool to calculate the energy balance while incorporating the dynamic properties of WFG. This article has studied the mathematical model of that tool and validated its ability to predict energy savings in buildings, taking spectral and thermal parameters of glazing catalogs, commercial software, and inputs from the measurements of the prototypes. The results found in this article showed that it is possible to predict the thermal behavior of WFG and the energy savings by comparing the thermal parameters of two prototypes. The energy absorbed by the water depends on the mass flow rate and the inlet and outlet temperatures.
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Nikolsky, Valeriy, Ivan Kuzyayev, Roman Dychkovskyi, Oleksandr Alieksandrov, Vadim Yaris, Serhiy Ptitsyn, Ludmila Tikhaya, et al. "A Study of Heat Exchange Processes within the Channels of Disk Pulse Devices." Energies 13, no. 13 (July 6, 2020): 3492. http://dx.doi.org/10.3390/en13133492.
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The effect of basic parameters of the channels of disk pulse devices on the heat exchange efficiency was studied both analytically and experimentally, especially in terms of pulse acting on the heat carrier. A methodology to determine the main parameters, namely the pressure and the temperature of the heat carrier as well as the pulse effect on the fluid, was proposed. The mathematical models of the effect of the structural and technological parameters of the channels in the disk pulse device on the heat exchange efficiency were developed. The models’ adequacy was proved based on a series of experimental studies involving devices with one-stage and multistage systems of pulsed heat carrier processing. This enabled the development, testing, and implementation of practical construction designs of pulse disk heat generators for decentralized heating of commercial and domestic buildings with one-stage and multistage systems of pulsed heat carrier processing. Taking into account the results of the mathematical modeling, the developed method of multistage pulse action was proved experimentally and implemented in regard to the structural design of a working chamber of the disk pulse heat generator. An efficient geometry of the working chamber of the disk pulse heat generator was specified for its further integration into the system of decentralized heat supply. One of the developed heat generators with the multistage pulse action on the heat carrier was integrated into the heating system of a greenhouse complex with a 0.86–0.9 efficiency coefficient.
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Lyu, Yan, Yiqun Pan, Xiaolei Yuan, Mingya Zhu, Zhizhong Huang, and Risto Kosonen. "A Comprehensive Evaluation Method for Air-Conditioning System Plants Based on Building Performance Simulation and Experiment Information." Buildings 11, no. 11 (November 6, 2021): 522. http://dx.doi.org/10.3390/buildings11110522.
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During the design stage of an HVAC (heating, ventilation, and air conditioning) system in a construction project, designers must decide on the most workable design scheme for the plant room in the building based on the evaluation of multiple aspects related to system performance that need to be considered, such as energy efficiency, economic effectiveness, etc. To solve this problem, this paper proposes a comprehensive evaluation method for the plant rooms of centralized air-conditioning systems in commercial buildings. This new method consists of two analyses used in tandem: Building Performance Simulation (BPS) models and a collection of real HVAC design cases (the carried-out design solutions). The BPS models and a knowledge of the reduction approach based on Rough Set (RS) theory are used to generate data and weight factors for the indices of energy efficiency; and the real design cases are employed with a heuristic algorithm to extract the compiled empirical information for other evaluation items of the centralized HVAC system. In addition, this paper also demonstrates an application in an actual case of a building construction project. By comparing the expert decision-making process and the evaluation results, it is found that they are basically consistent, which verifies the reasonability of the comprehensive evaluation method.
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Simmons, Cody R., Joshua R. Arment, Kody M. Powell, and John D. Hedengren. "Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts." Processes 7, no. 12 (December 5, 2019): 929. http://dx.doi.org/10.3390/pr7120929.
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This work explores the development of a home energy management system (HEMS) that uses weather and market forecasts to optimize the usage of home appliances and to manage battery usage and solar power production. A Moving Horizon Estimation (MHE) application is used to find the unknown home model parameters. These parameters are then updated in a Model Predictive Controller (MPC) which optimizes and balances competing comfort and economic objectives. Combining MHE and MPC applications alleviates model complexity commonly seen in HEMS by using a lumped parameter model that is adapted to fit a high-fidelity model. Heating, ventilation, and air conditioning (HVAC) on/off behaviors are simulated by using Mathematical Program with Complementarity Constraints (MPCCs) and solved in near real time with a non-linear solver. Removing HVAC on/off as a discrete variable and replacing it with an MPCC reduces solve time. The results of this work indicate that energy management optimization significantly decreases energy costs and balances energy usage more effectively throughout the day. A case study for Phoenix, Arizona shows an energy reduction of 21% and a cost reduction of 40%. This simulated home contributes less to the grid peak load and therefore improves grid stability and reduces the amplitude of load-following cycles for utilities. The case study combines renewable energy, energy storage, forecasts, cooling system, variable rate electricity plan and a multi-objective function allowing for a complete home energy optimization assessment. There remain several challenges, including improved forecast models, improved computational performance to allow the algorithms to run in real time, and mixed empirical/physics-based machine-learning methods to guide the model structure.
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Nazemi, Seyyed Danial, Mohsen A. Jafari, and Esmat Zaidan. "An Incentive-Based Optimization Approach for Load Scheduling Problem in Smart Building Communities." Buildings 11, no. 6 (May 31, 2021): 237. http://dx.doi.org/10.3390/buildings11060237.
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The impact of load growth on electricity peak demand is becoming a vital concern for utilities. To prevent the need to build new power plants or upgrade transmission lines, power companies are trying to design new demand response programs. These programs can reduce the peak demand and be beneficial for both energy consumers and suppliers. One of the most popular demand response programs is the building load scheduling for energy-saving and peak-shaving. This paper presents an autonomous incentive-based multi-objective nonlinear optimization approach for load scheduling problems (LSP) in smart building communities. This model’s objectives are three-fold: minimizing total electricity costs, maximizing assigned incentives for each customer, and minimizing inconvenience level. In this model, two groups of assets are considered: time-shiftable assets, including electronic appliances and plug-in electric vehicle (PEV) charging facilities, and thermal assets such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters. For each group, specific energy consumption and inconvenience level models were developed. The designed model assigned the incentives to the participants based on their willingness to reschedule their assets. The LSP is a discrete–continuous problem and is formulated based on a mixed-integer nonlinear programming approach. Zoutendijk’s method is used to solve the nonlinear optimization model. This formulation helps capture the building collaboration to achieve the objectives. Illustrative case studies are demonstrated to assess the proposed model’s effect on building communities consisting of residential and commercial buildings. The results show the efficiency of the proposed model in reducing the total energy cost as well as increasing the participants’ satisfaction. The findings also reveal that we can shave the peak demand by 53% and have a smooth aggregate load profile in a large-scale building community containing 500 residential and commercial buildings.
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Zakharchenko, Anastasiya, and Oleksandr Stepanets. "MATHEMATICAL MODELING OF THE FAN COIL TEMPERATURE BEHAVIOR." Bulletin of the National Technical University «KhPI» Series: New solutions in modern technologies, no. 3(13) (October 26, 2022): 11–17. http://dx.doi.org/10.20998/2413-4295.2022.03.02.
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Fan coils are widely used in heating and air conditioning systems in both residential buildings and commercial areas. This article deals with the creation of a mathematical model of the fan coil for use in control systems, building digital twins, etc. The development of models of components of building engineering systems contributes to the introduction of more sophisticated control algorithms and analytics to coordinate the operation of equipment and as a result improve the energy efficiency of systems, the ability to investigate the dynamics of systems, etc. In this paper, a system of heat balance equations for the water, air and walls of the heat exchanger was used, which allows for simulating the operation of the system in transient modes. Considerable attention was paid to the calculation of the coolant and air parameters, including specific heat capacity, heat transfer coefficients, water and air thermal conductivity, kinematic viscosity coefficients, density, etc. The use of dynamic calculation of the coolant and air characteristics was proposed, an algorithm using the Python programming language and the CoolProp, SciPy, and NumPy libraries were implemented, and simulation results were presented. To assess the effectiveness of the proposed solutions, an analysis of the simulation results for the system with constant values of the coolant and air parameters, determined from the averaged initial values of the input and output parameters of the model, compared to the system with dynamic calculation, was performed. Finally, we investigated the dynamics of the external factor influence on the simulation results and presented an analysis of the influence of the model input variables on the output temperature values due to implicit relationships in the calculation of the parameters characterizing the heat transfer fluid and the air in the fan coil. According to the results of the comparison, the deviations in the simulation results of the models under study were estimated for the calculated value of heat output on the air side in absolute and relative units.
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Acquaah, Yaa Takyiwaa, Balakrishna Gokaraju, Raymond C. Tesiero, and Gregory H. Monty. "Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy." Remote Sensing 13, no. 19 (September 26, 2021): 3847. http://dx.doi.org/10.3390/rs13193847.
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The control of thermostats of a heating, ventilation, and air-conditioning (HVAC) system installed in commercial and residential buildings remains a pertinent problem in building energy efficiency and thermal comfort research. The ability to determine the number of people at a particular time in an area is imperative for energy efficiency in order to condition only occupied regions and thermally deficient regions. In this study of the best features comparison for detecting the number of people in an area, feature extraction techniques including wavelet scattering, wavelet decomposition, grey-level co-occurrence matrix (GLCM) and feature maps convolution neural network (CNN) layers were explored using thermal camera imagery. Specifically, the pretrained CNN networks explored are the deep residual (Resnet-50) and visual geometry group (VGG-16) networks. The discriminating potential of Haar, Daubechies and Symlets wavelet statistics on different distributions of data were investigated. The performance of VGG-16 and ResNet-50 in an end-to-end manner utilizing transfer learning approach was investigated. Experimental results showed the classification and regression trees (CART) model trained on only GLCM and Haar wavelet statistic features, individually achieved accuracies of approximately 80% and 84%, respectively, in the detection problem. Moreover, k-nearest neighbors (KNN) trained on the combined features of GLCM and Haar wavelet statistics achieved an accuracy of approximately 86%. In addition, the performance accuracy of the multi classification support vector machine (SVM) trained on deep features obtained from layers of pretrained ResNet-50 and VGG-16 was between 96% and 97%. Furthermore, ResNet-50 transfer learning outperformed the VGG-16 transfer learning model for occupancy detection using thermal imagery. Overall, the SVM model trained on features extracted from wavelet scattering emerged as the best performing classifier with an accuracy of 100%. A principal component analysis (PCA) on the wavelet scattering features proved that the first twenty (20) principal components achieved a similar accuracy level instead of training on the whole feature set to reduce the execution time. The occupancy detection models can be integrated into HVAC control systems for energy efficiency and security systems, and aid in the distribution of resources to people in an area.
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Emamjome Kashan, Mohammad, Alan S. Fung, and John Swift. "Integrating Novel Microchannel-Based Solar Collectors with a Water-to-Water Heat Pump for Cold-Climate Domestic Hot Water Supply, Including Related Solar Systems Comparisons." Energies 14, no. 13 (July 5, 2021): 4057. http://dx.doi.org/10.3390/en14134057.
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In Canada, more than 80% of energy in the residential sector is used for space heating and domestic hot water (DHW) production. This study aimed to model and compare the performance of four different systems, using solar energy as a renewable energy source for DHW production. A novel microchannel (MC) solar thermal collector and a microchannel-based hybrid photovoltaic/thermal collector (PVT) were fabricated (utilizing a microchannel heat exchanger in both cases), mathematical models were created, and performance was simulated in TRNSYS software. A water-to-water heat pump (HP) was integrated with these two collector-based solar systems, namely MCPVT-HP and MCST-HP, to improve the total solar fraction. System performance was then compared with that of a conventional solar-thermal-collector-based system and that of a PV-resistance (PV-R) system, using a monocrystalline PV collector. The heat pump was added to the systems to improve the systems’ efficiency and provide the required DHW temperatures when solar irradiance was insufficient. Comparisons were performed based on the temperature of the preheated water storage tank, the PV panel efficiency, overall system efficiency, and the achieved solar fraction. The microchannel PVT-heat pump (MCPVT-HP) system has the highest annual solar fraction among all the compared systems, at 76.7%. It was observed that this system had 10% to 35% higher solar fraction than the conventional single-tank solar-thermal-collector-based system during the wintertime in a cold climate. The performance of the two proposed MC-based systems is less sensitive than the two conventional systems to collector tilt angle in the range of 45 degrees to 90 degrees. If roof space is limited, the MCPVT-HP system is the best choice, as the MCPVT collector can perform effectively when mounted vertically on the facades of high-rise residential and commercial buildings. A comparison among five Canadian cities was also performed, and we found that direct beam radiation has a great effect on overall system solar faction.
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Eslamirad, Nasim, Soheil Malekpour Kolbadinejad, Mohammadjavad Mahdavinejad, and Mohammad Mehranrad. "Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran." Smart and Sustainable Built Environment 9, no. 4 (April 28, 2020): 361–74. http://dx.doi.org/10.1108/sasbe-03-2019-0028.
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PurposeThis research aims to introduce a new methodology for integration between urban design strategies and supervised machine learning (SML) method – by applying both energy engineering modeling (evaluating phase) for the existing green sidewalks and statistical energy modeling (predicting phase) for the new ones – to offer algorithms that help to catch the optimum morphology of green sidewalks, in case of high quality of the outdoor thermal comfort and less errors in results.Design/methodology/approachThe tools of the study are the way of processing by SML, predicting the future based on the past. Machine learning is benefited from Python advantages. The structure of the study consisted of two main parts, as the majority of the similar studies follow: engineering energy modeling and statistical energy modeling. According to the concept of the study, at first, from 2268 models, some are randomly selected, simulated and sensitively analyzed by ENVI-met. Furthermore, the Envi-met output as the quantity of thermal comfort – predicted mean vote (PMV) and weather items are inputs of Python. Then, the formed data set is processed by SML, to reach the final reliable predicted output.FindingsThe process of SML leads the study to find thermal comfort of current models and other similar sidewalks. The results are evaluated by both PMV mathematical model and SML error evaluation functions. The results confirm that the average of the occurred error is about 1%. Then the method of study is reliable to apply in the variety of similar fields. Finding of this study can be helpful in perspective of the sustainable architecture strategies in the buildings and urban scales, to determine, monitor and control energy-based behaviors (thermal comfort, heating, cooling, lighting and ventilation) in operational phase of the systems (existed elements in buildings, and constructions) and the planning and designing phase of the future built cases – all over their life spans.Research limitations/implicationsLimitations of the study are related to the study variables and alternatives that are notable impact on the findings. Furthermore, the most trustable input data will result in the more accuracy in output. Then modeling and simulation processes are most significant part of the research to reach the exact results in the final step.Practical implicationsFinding of the study can be helpful in urban design strategies. By finding outdoor thermal comfort that resulted from machine learning method, urban and landscape designers, policymakers and architects are able to estimate the features of their designs in air quality and urban health and can be sure in catching design goals in case of thermal comfort in urban atmosphere.Social implicationsBy 2030, cities are delved as living spaces for about three out of five people. As green infrastructures influence in moderating the cities’ climate, the relationship between green spaces and habitants’ thermal comfort is deduced. Although the strategies to outside thermal comfort improvement, by design methods and applicants, are not new subject to discuss, applying machines that may be common in predicting results can be called as a new insight in applying more effective design strategies and in urban environment’s comfort preparation. Then study’s footprint in social implications stems in learning from the previous projects and developing more efficient strategies to prepare cities as the more comfortable and healthy places to live, with the more efficient models and consuming money and time.Originality/valueThe study achievements are expected to be applied not only in Tehran but also in other climate zones as the pattern in more eco-city design strategies. Although some similar studies are done in different majors, the concept of study is new vision in urban studies.
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Lami, Mohammed, Faris Al-naemi, Hussein A. Jabbar, Hameed Alrashidi, and Walid Issa. "Optimisation of wasted air utilisation in thermal loss reduction in double-glazed windows of commercial buildings in cold regions." International Journal of Energy and Environmental Engineering, May 20, 2022. http://dx.doi.org/10.1007/s40095-022-00499-0.
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AbstractVentilating of multi pane-glazed windows using wasted air of buildings is an effective technique to minimize heat loss through windows and save heating energy in cold regions. In low-scaled occupancy buildings with high WWR ratio, buildings supply a low flow rate of wasted air to windows ventilation systems, resulting in a declination in its thermal performance. Therefore, this study introduces methods of managing the utilisation of wasted air in windows ventilation to optimise the energy saving. Two methods have been implemented experimentally on a small-scaled room. The first method is a time-based division of air pump operation, an air pump ventilates multiple windows, one window at a time repetitively. The second method shares the available wasted air to multiple windows. The experimental results and mathematical heat transfer model have been employed to evaluate thermal performance of the system in different methods. The first method showed a best energy saving with a duty cycle of 50% for the air pump, and on/off operation every 10 s. An energy saving of 42.6% has been realized compared to the traditional double-glazed windows, and the heat transfer coefficient was declined from 3.82 to 2.8 W/m2 K. The second method showed an optimum thermal performance when the available flow rate of wasted air was shared with three double-glazed windows. An energy saving of 83.1% was achieved compared to the traditional double-glazed windows, and the heat transfer coefficient dropped from 3.82 to 2.36 W/m2 K.
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Mawson, Victoria Jayne, and Ben Richard Hughes. "Coupling simulation with artificial neural networks for the optimisation of HVAC controls in manufacturing environments." Optimization and Engineering, October 6, 2020. http://dx.doi.org/10.1007/s11081-020-09567-y.
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Abstract Manufacturing remains one of the most energy intensive sectors, additionally, the energy used within buildings for heating, ventilation and air conditioning (HVAC) is responsible for almost half of the UK’s energy demand. Commonly, these are analysed in isolation from one another. Use of machine learning is gaining popularity due to its ability to solve non-linear problems with large data sets and little knowledge about relationships between parameters. Such models use relationships between inputs and outputs to make further predictions on unseen data, without requiring any understanding regarding the system, making them highly suited to dealing with the stochastic data sets found in a manufacturing environment. This has been seen in literature for determining electrical energy demand for residential or commercial buildings, rather than manufacturing environments. This study proposes a novel method of coupling simulation with machine learning to predict indoor workshop conditions and building energy demand, in response to production schedules, outdoor conditions, building behaviour and use. Such predictions can subsequently allow for more efficient management of HVAC systems. Based upon predicted energy consumption, potential spikes were identified and manufacturing schedules subsequently optimised to reduce peak energy demand. Coupling simulation techniques with machine learning algorithms eliminates the requirement for costly and intrusive methods of data collection, providing a method of predicting and optimising building energy consumption in the manufacturing sector.