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Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings
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Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 29380

Special Issue Editor

Dr. Nassif Nabil

E-Mail Website
Guest Editor
Department of Civil and Architectural Engineering, University of Cincinnati, Cincinnati, OH 45221, USA
Interests: modeling, analysis, optimization, and control of HVAC systems; building mechanical systems and refrigeration systems; HVAC system design and installation; control system and HVAC system optimization; artificial intelligence applications and smart capabilities in building energy systems; energy efficiency and technologies in buildings; fault detection and diagnosis of cooling and heating energy system; continuous and retro-commissioning of HVAC systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Buildings are major consumers of global energy. Improvements in the design and operation of building energy systems, specifically HVAC systems can reduce energy costs in homes and commercial buildings, which represents a significant economic opportunity. Reliable models, optimization techniques, and advanced control strategies are essential to achieve the maximum overall performance efficiency of HVAC systems and thereby reduce building energy uses. Thus, the aim of this Special Issue is to address the needs of new modeling techniques for the design and operation of building energy systems, advanced operation of HVAC systems through better control and control sequence strategies, data-enabled modeling and optimization methods, advanced computational methods for buildings, and any innovative design and operation techniques that can lead to better building energy system efficiency.

We invite high-quality, cutting-edge articles for this Special Issue on “Modeling, Analysis, Optimization, and Control of HVAC Systems in Buildings”; Possible topics include but not are limited to the following:

  • Building energy system design and operation
  • Modeling and optimization of HVAC systems
  • HVAC system analysis
  • HVAC system control and optimization
  • Applications of Artificial intelligence and computational methods to building energy systems
  • Advanced computational methods and modern data analysis techniques for buildings

Dr. Nassif Nabil
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Building energy system
  • Building energy efficiency
  • Buildings
  • HVAC system
  • Modeling and optimization
  • HVAC system control
  • Chilled water system
  • Artificial intelligence method
  • Computational intelligence

Published Papers (10 papers)

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Research

14 pages, 1751 KiB  
Article
Energy Cost Driven Heating Control with Reinforcement Learning
by Lotta Kannari, Julia Kantorovitch, Kalevi Piira and Jouko Piippo
Buildings 2023, 13(2), 427; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13020427 - 03 Feb 2023
Cited by 2 | Viewed by 1869
Abstract
The current energy crisis raised concern about the lack of electricity during the wintertime, especially that consumption should be cut at peak consumption hours. For the building owners, this is visible as rising electricity prices. Availability of near real-time data on energy performance [...] Read more.
The current energy crisis raised concern about the lack of electricity during the wintertime, especially that consumption should be cut at peak consumption hours. For the building owners, this is visible as rising electricity prices. Availability of near real-time data on energy performance is opening new opportunities to optimize energy flexibility capabilities of buildings. This paper presents a reinforcement learning (RL)-based method to control the heating for minimizing the heating electricity cost and shifting the electricity usage away from peak demand hours. Simulations are carried out with electrically heated single-family houses. The results indicate that with RL, in the case of varying electricity prices, it is possible to save money and keep the indoor thermal comfort at an appropriate level. Full article
(This article belongs to the Special Issue Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings)
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20 pages, 6928 KiB  
Article
A Model Predictive Control for Heat Supply at Building Thermal Inlet Based on Data-Driven Model
by Liangdong Ma, Yangyang Huang, Jiyi Zhang and Tianyi Zhao
Buildings 2022, 12(11), 1879; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12111879 - 04 Nov 2022
Cited by 5 | Viewed by 1511
Abstract
At present, the traditional control strategy of heating systems is still unable to achieve building heating on demand, which enhances the energy consumption of heating and affects the thermal comfort of buildings. Therefore, this study puts forward a novel data-driven MPC for building [...] Read more.
At present, the traditional control strategy of heating systems is still unable to achieve building heating on demand, which enhances the energy consumption of heating and affects the thermal comfort of buildings. Therefore, this study puts forward a novel data-driven MPC for building thermal inlet, which allows the optimal operation of the district heating system and has been verified by simulation with three public buildings. In this method, the indoor temperature at the next moment reaches the temperature set value by changing the current flow rate. First, based on the energy consumption monitoring platform and the measured data of the buildings, the building indoor temperature prediction model at the next moment is established by using long short-term memory (LSTM). Compared with subspace model identification (SMI), LSTM has higher prediction accuracy, and the R2 was about 0.9 in three buildings. Second, the particle generated by particle swarm optimization, which represents the flow variation, is input to the trained LSTM to predict the indoor temperature. By minimizing the objective function, the optimal flow change at the current time can be calculated. The results showed that the MPC based on a data-driven model can adjust the flow rate in time to maintain a stable indoor temperature with ±0.5 °C error. In addition, when the temperature setting needs to be changed, the indoor temperature can reach the new set value in 3 h, which outperforms the PID control. The method proposed in this paper can greatly reduce the influence of regulation lag by adjusting the flow in advance. Full article
(This article belongs to the Special Issue Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings)
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18 pages, 7939 KiB  
Article
Hybrid Model for Forecasting Indoor CO2 Concentration
by Ki Uhn Ahn, Deuk-Woo Kim, Kyungjoo Cho, Dongwoo Cho, Hyun Mi Cho and Chang-U Chae
Buildings 2022, 12(10), 1540; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12101540 - 27 Sep 2022
Cited by 4 | Viewed by 1674
Abstract
Indoor CO2 concentration is considered a metric of indoor air quality that affects the health of occupants. In this study, a hybrid model was developed for forecasting the varying indoor CO2 concentration levels in a residential apartment unit in the presence [...] Read more.
Indoor CO2 concentration is considered a metric of indoor air quality that affects the health of occupants. In this study, a hybrid model was developed for forecasting the varying indoor CO2 concentration levels in a residential apartment unit in the presence of occupants by controlling the ventilation rates of a heat recovery ventilator. In this model, the mass balance equation for a single zone as a white-box model was combined with a Bayesian neural network (BNN) as a black box model. During the learning process of the hybrid model, the BNN estimated an aggregated unknown ventilation rate and transferred the estimation to the mass-balance equation. A parametric study was conducted by changing the prediction horizons of the hybrid model from 5 to 15 min, and the forecasting performance of the hybrid model was compared with the stand-alone mass balance equation. The hybrid model showed better forecasting performance than that of the mass balance equation on the experimental dataset for a living room and bedroom. The average MBE and CVRMSE of the hybrid model for the prediction horizon of 15 min were 0.65% and 5.23%, respectively, whereas those of the mass balance equation were 0.99% and 9.30%, respectively. Full article
(This article belongs to the Special Issue Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings)
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26 pages, 7287 KiB  
Article
HVAC Control System Using Predicted Mean Vote Index for Energy Savings in Buildings
by Daniel Fernando Espejel-Blanco, José Antonio Hoyo-Montaño, Jaime Arau, Guillermo Valencia-Palomo, Abel García-Barrientos, Héctor Ricardo Hernández-De-León and Jorge Luis Camas-Anzueto
Buildings 2022, 12(1), 38; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12010038 - 03 Jan 2022
Cited by 14 | Viewed by 3784
Abstract
Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of [...] Read more.
Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade. Full article
(This article belongs to the Special Issue Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings)
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25 pages, 2835 KiB  
Article
A Comprehensive Evaluation Method for Air-Conditioning System Plants Based on Building Performance Simulation and Experiment Information
by Yan Lyu, Yiqun Pan, Xiaolei Yuan, Mingya Zhu, Zhizhong Huang and Risto Kosonen
Buildings 2021, 11(11), 522; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings11110522 - 06 Nov 2021
Cited by 1 | Viewed by 2372
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings)
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33 pages, 15429 KiB  
Article
Development and Validation of Building Control Algorithm Energy Management
by Yerim Han and Woohyun Kim
Buildings 2021, 11(3), 131; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings11030131 - 21 Mar 2021
Cited by 1 | Viewed by 2375
Abstract
In this paper, a building control algorithm is proposed to reduce the electricity consumption of a building with a variable refrigerant flow (VRF) system. The algorithm uses sequence-to-sequence long short-term memory (seq2seq LSTM) to set target electricity consumption, and uses a VRF air [...] Read more.
In this paper, a building control algorithm is proposed to reduce the electricity consumption of a building with a variable refrigerant flow (VRF) system. The algorithm uses sequence-to-sequence long short-term memory (seq2seq LSTM) to set target electricity consumption, and uses a VRF air conditioner system to reduce electricity consumption. After setting target electricity consumption, the algorithm is applied as a method of updating target electricity consumption. In addition, we propose two methods to increase the performance of the seq2seq LSTM model. First, among the feature selection methods, random forest is used to select, among the numerous features of the data, only those features that are most relevant to the predicted value. Second, we use Bayesian optimization, which selects the optimal hyperparameter that shows the best model performance. In order to control the air conditioners, the priority of air conditioners is designated, the method of prioritization being the analytical hierarchy process (AHP). In this study, comparison of the performance of seq2seq LSTM model with and without Bayesian optimization proved that the use of Bayesian optimization achieved good performance. Simulation and demonstration experiments using the algorithm were also conducted, and showed that building electricity consumption decreased in a similar manner to the reduction rate by means of the algorithm. Full article
(This article belongs to the Special Issue Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings)
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14 pages, 3788 KiB  
Article
Modeling of Building Energy Consumption by Integrating Regression Analysis and Artificial Neural Network with Data Classification
by Iffat Ridwana, Nabil Nassif and Wonchang Choi
Buildings 2020, 10(11), 198; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings10110198 - 02 Nov 2020
Cited by 18 | Viewed by 3523
Abstract
With the constant expansion of the building sector as a major energy consumer in the modern world, the significance of energy-efficient building systems cannot be more emphasized. Most of the buildings are now equipped with an electric dashboard to record consumption data which [...] Read more.
With the constant expansion of the building sector as a major energy consumer in the modern world, the significance of energy-efficient building systems cannot be more emphasized. Most of the buildings are now equipped with an electric dashboard to record consumption data which presents a significant scope of research by utilizing those data in energy modeling. This paper investigates conventional regression modeling in building energy estimation and proposes three models with data classifications to improve their performance. The proposed models are regression models and an artificial neural network model with data classification for predicting hourly or sub-hourly energy usage in four different buildings. Energy data is collected from a building energy simulation program and existing buildings to develop the models for detailed analysis. Data classification is recommended according to the system operating schedules of the buildings and models are tested for their performance in capturing the data trends resulting from those schedules. Proposed regression models and an ANN model with the recommended classification show very accurate results in estimating energy demand compared to conventional regression models. Correlation coefficient and root mean squared error values improve noticeably for the proposed models and they can potentially be utilized for energy conservation purposes and energy savings in the buildings. Full article
(This article belongs to the Special Issue Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings)
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13 pages, 3906 KiB  
Article
Adjustment of Multiple Variables for Optimal Control of Building Energy Performance via a Genetic Algorithm
by Nam-Chul Seong, Jee-Heon Kim and Wonchang Choi
Buildings 2020, 10(11), 195; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings10110195 - 29 Oct 2020
Cited by 6 | Viewed by 2445
Abstract
Optimizing the operating conditions and control set points of the heating, ventilation, and air-conditioning (HVAC) system in a building is one of the most effective ways to save energy and improve the building’s energy performance. Here, we optimized different control variables using a [...] Read more.
Optimizing the operating conditions and control set points of the heating, ventilation, and air-conditioning (HVAC) system in a building is one of the most effective ways to save energy and improve the building’s energy performance. Here, we optimized different control variables using a genetic algorithm. We constructed and evaluated three optimal control scenarios (cases) to compare the energy savings of each by varying the setting and number and type of the optimized control variables. Case 1 used only air-side control variables and achieved an energy savings rate of about 5.72%; case 2 used only water-side control variables and achieved an energy savings rate of 16.98%; and case 3, which combined all the control variables, achieved 25.14% energy savings. The energy savings percentages differed depending on the setting and type of the control variables. The results show that, when multiple control set points are optimized simultaneously in an HVAC system, the energy savings efficiency becomes more effective. It was also confirmed that the control characteristics and energy saving rate change depending on the location and number of control variables when optimizing using the same algorithm. Full article
(This article belongs to the Special Issue Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings)
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17 pages, 3602 KiB  
Article
Optimization-Based Data-Enabled Modeling Technique for HVAC Systems Components
by Rand Talib, Nassif Nabil and Wonchang Choi
Buildings 2020, 10(9), 163; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings10090163 - 13 Sep 2020
Cited by 12 | Viewed by 3815
Abstract
Most of the energy consumed by the residential and commercial buildings in the U.S. is dedicated to space cooling and heating systems, according to the U.S. Energy Information Administration. Therefore, the need for better operation mechanisms of those existing systems become more crucial. [...] Read more.
Most of the energy consumed by the residential and commercial buildings in the U.S. is dedicated to space cooling and heating systems, according to the U.S. Energy Information Administration. Therefore, the need for better operation mechanisms of those existing systems become more crucial. The most vital factor for that is the need for accurate models that can accurately predict the system component performance. Therefore, this paper’s primary goal is to develop a new accurate data-driven modeling and optimization technique that can accurately predict the performance of the selected system components. Several data-enabled modeling techniques such as artificial neural networks (ANN), support vector machine (SVM), and aggregated bootstrapping (BSA) are investigated, and model improvements through model structure optimization proposed. The optimization algorithm will determine the optimal model structures and automate the process of the parametric study. The optimization problem is solved using a genetic algorithm (GA) to reduce the error between the simulated and actual data for the testing period. The models predicted the performance of the chilled water variable air volume (VAV) system’s main components of cooling coil and fan power as a function of multiple inputs. Additionally, the packaged DX system compressor modeled, and the compressor power was predicted. The testing results held a low coefficient of variation (CV%) values of 1.22% for the cooling coil, and for the fan model, it was found to be 9.04%. The testing results showed that the proposed modeling and optimization technique could accurately predict the system components’ performance. Full article
(This article belongs to the Special Issue Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings)
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27 pages, 9162 KiB  
Article
Mitigation Strategies for Overheating and High Carbon Dioxide Concentration within Institutional Buildings: A Case Study in Toronto, Canada
by Claire Tam, Yuqing Zhao, Zaiyi Liao and Lian Zhao
Buildings 2020, 10(7), 124; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings10070124 - 09 Jul 2020
Cited by 12 | Viewed by 3594
Abstract
Indoor air quality and thermal conditions are important considerations when designing indoor spaces to ensure occupant health, satisfaction, and productivity. Carbon dioxide (CO2) concentration and indoor air temperature are two measurable parameters to assess air quality and thermal conditions within a [...] Read more.
Indoor air quality and thermal conditions are important considerations when designing indoor spaces to ensure occupant health, satisfaction, and productivity. Carbon dioxide (CO2) concentration and indoor air temperature are two measurable parameters to assess air quality and thermal conditions within a space. Occupants are progressively affected by the indoor environment as the time spent indoors prolongs. Specifically, there is an interest in carrying out investigations on the indoor environment through surveying existing Heating, Ventilation, Air Conditioning (HVAC) system operations in classrooms. Indoor air temperature and CO2 concentration in multiple lecture halls in Toronto, Canada were monitored; observations consistently show high indoor air temperature (overheating) and high CO2 concentration. One classroom is chosen as a representative case study for this paper. The results verify a strong correlation between the number of occupants and the increase in air temperature and CO2 concentration. Building Energy Simulation (BES) is used to investigate the causes of discomfort in the classroom, and to identify methods for regulating the temperature and CO2 concentration. This paper proposes retro-commissioning strategies that could be implemented in institutional buildings; specifically, the increase of outdoor airflow rate and the addition of occupancy-based pre-active HVAC system control. The proposed retrofit cases reduce the measured overheating in the classrooms by 2-3 °C (indoor temperature should be below 23 °C) and maintain CO2 concentration under 900 ppm (the CO2 threshold is 1000 ppm), showing promising improvements to a classroom’s thermal condition and indoor air quality. Full article
(This article belongs to the Special Issue Modeling, Analysis, Optimization and Control of HVAC Systems in Buildings)
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