Heating, Ventilation, And Air Conditioning Operation Research Articles

A novel rule mining method for knowledge discovery of relationships among indoor air quality, HVAC operation and occupants’ activities

This paper presents a novel data mining method to discover the occurrence time, co-occurrence and sequential association rules among indoor air quality (IAQ), Heating, Ventilation and Air Conditioning (HVAC) operations and occupants’ activities. The novelty of this work includes a peak detection method to discover the pollutants' peaks and a rule mining method with extended rule selection criteria that considers both the time lags between events and relationships among multiple events. The peak detection method aimed to use moving windows and criteria to find the candidate peaks and the non-maximum suppression to filter the overlapped candidate peaks. The proposed rule mining method is based on the Apriori and Apriori-All with the rule selection criteria extended by labeling the occurrence time to events to find the rules among various occurrence times and multiple events and by limiting the time lags among the occurrences of the events to discover the sequential rules. Two datasets, one from 70 houses and one from a single commercial building, were used to evaluate the performance of this proposed data-driven method. The results showed that 93.19% of the peak detection results matched the peaks detected from the data records. The CO2 peaked frequently at 12:00, 17:00 and from 19:00 to 24:00. The occurrence of occupants’ activities could lead to pollutants’ concentrations and pollutants peaks within 2 hours in the residential buildings. The occurrence of a high Wifi connection number was frequently followed by a CO2 peak within 2 hours in the commercial building. These findings can support the development of control strategies for building HVAC systems to achieve better indoor air quality control.

Design Change and Operational Consideration of the HVAC System during Nuclear Power Plant Decommissioning

The heating, ventilation, and air conditioning (HVAC) system plays a crucial role in ensuring the safety of workers and preventing the release of gaseous radioactive materials into the environment during the decommissioning of a nuclear power plant (NPP). To establish an HVAC operation strategy, decommissioning phases were divided into four stages, and the HVAC systems were reclassified. In addition, assumptions have been made regarding design modifications and maintenance for the reactor containment building (RCB) HVAC, fuel handling building (FHB) HVAC, and main control room (MCR) HVAC. Based on these, for RCB HVAC operation, natural ventilation and RCB purge operation during the transition period are proposed. In the decommissioning stage, recirculation operation, entire ventilation operation consisting of continuous operation and purge operation, and finally partial ventilation operation to purify local space were proposed. Moreover, during the transition period, the FHB HVAC was proposed to operate as normal NPP, and the MCR HVAC was suggested to operate with safety-related equipment removed.

LEVERAGING PROJECT MANAGEMENT TOOLS FOR ENERGY EFFICIENCY IN HVAC OPERATIONS: A PATH TO CLIMATE RESILIENCE

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.

PROJECT MANAGEMENT STRATEGIES FOR ACCELERATING ENERGY EFFICIENCY IN HVAC SYSTEMS AMIDST CLIMATE CHANGE

Amidst the escalating impacts of climate change, optimizing energy efficiency in Heating, Ventilation, and Air Conditioning (HVAC) systems emerges as a critical imperative. This review explores project management strategies aimed at accelerating energy efficiency in HVAC systems amidst climate change challenges. In response to climate change, the urgency to mitigate energy consumption in HVAC systems has intensified. Project management plays a pivotal role in orchestrating systematic approaches to address this challenge. Firstly, effective project management entails comprehensive planning. This involves conducting thorough energy audits and assessments to identify inefficiencies and prioritize improvement opportunities. Utilizing advanced technologies such as Building Energy Management Systems (BEMS) can enhance data collection and analysis for informed decision-making. Secondly, stakeholder engagement is essential for successful project implementation. Collaboration between building owners, facility managers, engineers, and contractors facilitates alignment of goals and resources. Clear communication channels and regular progress updates foster accountability and momentum throughout the project lifecycle. Thirdly, project management methodologies like Agile or Lean can streamline HVAC efficiency initiatives. Iterative development allows for continuous improvement, enabling teams to adapt to evolving requirements and incorporate feedback effectively. Moreover, risk management strategies are vital to anticipate and mitigate potential obstacles. Contingency plans for supply chain disruptions, regulatory changes, and unforeseen technical challenges ensure project resilience and minimize disruptions. Lastly, monitoring and evaluation mechanisms are indispensable for tracking project performance and verifying energy savings. Utilizing Key Performance Indicators (KPIs) and performance benchmarks enables stakeholders to measure success and adjust strategies as needed. Effective project management strategies are indispensable for accelerating energy efficiency in HVAC systems amidst climate change. By emphasizing comprehensive planning, stakeholder engagement, agile methodologies, risk management, and continuous evaluation, organizations can navigate challenges and realize substantial energy savings in HVAC operations.
 Keywords: Project Management, HVAC, Climate Change, Energy, Efficiency, Review.

Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry

Large language models (LLMs) have shown human-level capabilities in solving various complex tasks. However, it is still unknown whether state-of-the-art LLMs master sufficient knowledge related to heating, ventilation and air conditioning (HVAC) systems. It will be inspiring if LLMs can think and learn like professionals in the HVAC industry. Hence, this study investigates the performance of LLMs on mastering the knowledge and skills related to the HVAC industry by letting them take the ASHRAE Certified HVAC Designer examination, an authoritative examination in the HVAC industry. Three key knowledge capabilities are explored: recall, analysis and application. Twelve representative LLMs are tested such as GPT-3.5, GPT-4 and LLaMA. According to the results, GPT-4 passes the ASHRAE Certified HVAC Designer examination with scores from 74 to 78, which is higher than about half of human examinees. Besides, GPT-3.5 passes the examination twice out of five times. It demonstrates that some LLMs such as GPT-4 and GPT-3.5 have great potential to assist or replace humans in designing and operating HVAC systems. However, they still make some mistakes sometimes due to the lack of knowledge, poor reasoning capabilities and unsatisfactory equation calculation abilities. Accordingly, four future research directions are proposed to reveal how to utilize and improve LLMs in the HVAC industry: teaching LLMs to use design tools or software in the HVAC industry, enabling LLMs to read and analyze the operational data from HVAC systems, developing tailored corpuses for the HVAC industry, and assessing the performance of LLMs in real-world HVAC design and operation scenarios.

Impact of Building Automation on Indoor Air Quality and HVAC Performance

In response to the growing emphasis on healthier indoor environments and sustainable building practices, this research delves into the intricate relationship between building automation and key facets of indoor environmental quality. Specifically, the study focuses on the impact of building automation systems on Indoor Air Quality (IAQ) and the overall performance of Heating, Ventilation, and Air Conditioning (HVAC) systems. With the increasing demand for enhanced IAQ, building automation emerges as a crucial tool for real-time monitoring and optimization of various IAQ parameters. The research explores the comprehensive integration of automation technologies within building systems, elucidating their multifaceted effects on IAQ. By scrutinizing the intricate interplay between automation systems and HVAC components, the study unveils the ways in which automation technology influences and contributes to IAQ management. The investigation encompasses a thorough examination of sensor technologies, control algorithms, and data analytics employed in building automation to ensure a nuanced understanding of their impact on IAQ. Furthermore, the study elucidates the broader implications of building automation on HVAC system performance. It investigates how automated control mechanisms, coupled with real-time data analytics, contribute to the efficient operation of HVAC systems in maintaining optimal IAQ. The research contemplates the synergies between automation technologies and HVAC components, emphasizing their role in not only ensuring healthier indoor environments but also in achieving energy-efficient and sustainable HVAC operation.

Multi-objective optimization of building HVAC operation: Advanced strategy using Koopman predictive control and deep learning

Predictive control is an effective method for addressing the increasing demand for heating, ventilation, and air conditioning (HVAC) systems to operate with greater flexibility and efficiency. In this paper, the multi-objective problem of energy-efficient HVAC operation while tracking desired setpoints is addressed through an advanced control strategy, including a novel supervisory data-driven predictive control (DPC) and a local loop with a PI controller. The supervisory level is based on the integration of the DPC framework and the bilinear Koopman predictor. A deep neural network architecture is developed to realize the bilinear building thermal predictor. In addition, the integral input-to-state stability (iISS) constraint is enforced in the training procedure to obtain a global iISS control-oriented thermal predictor that enables the application of predictive control purely based on historical data. The effectiveness of the proposed approach is demonstrated through the simulation of a three-zone office in the transient system simulation software (TRNSYS). The proposed control configuration is implemented using a coupled TRNSYS-MATLAB simulation framework. It has shown significant potential for keeping the desired environment in line with energy conservation. Furthermore, a comparison is conducted between the proposed approach, the conventional controller, and another recent DPC approach, which demonstrates the suggested method’s superiority.

Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach

With global warming intensifying and resource conflicts escalating, the world is undergoing a transformative shift toward sustainable practices and energy-efficient solutions. With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). Within a BEMSplatform, regulating the operation of Heating, Ventilation, and Air Conditioning (HVAC) systems is more important, noting that HVAC systems account for about 40% of the total energy cost in the commercial sector.This paper offers a Deep Reinforcement Learning (DRL) algorithm as a data-driven approach to controlling HVAC operation to enhance the energy efficiency of commercial buildings with open offices while ensuring thermal comfort for occupants in different zones. Compared to alternative methods such as rule-based models and model-predictive control, data-driven models have shown promising results in optimizing building energy consumption without the need for building-specific thresholds, prior knowledge about the underlying physics of heat distribution, and digital mapping of the airflow. Despite the astonishing performance of modern DRL methods in controlling energy management, a particular energy-saving solution for open-plan offices with multiple Variable Air Volume (VAV) systems, where different zones cannot be treated independently, is still missing. Also, some of the existing methods suffer from key issues such as long training time and lack of generalizability for using over-complicated models, incorporating external factors that are hard to model and characterize, and including factors that are not typically accessible. To solve these issues, we propose a low-complexity DRL-based model with multi-input multi-output architecture for the HVAC energy optimization of open-plan offices, which uses only a handful of controllable and accessible factors. The efficacy of our solution is evaluated through extensive analysis of the overall energy consumption and thermal comfort levels compared to a baseline system based on the existing HVAC schedule from a real case. This comparison shows that our method achieves 37% savings in energy consumption with minimum temperature violation (<1%) of the desired temperature range during work hours. It takes only a total of 40 min for 5 epochs (about 7.75 min per epoch) to train a network with superior performance and covering diverse conditions for its low-complexity architecture; therefore, it easily adapts to changes in the building setups, weather conditions, occupancy rate, etc. Moreover, by enforcing smoothness on the control strategy, we suppress the frequent and unpleasant on/off transitions on HVAC units to avoid occupant discomfort and potential damage to the system. The generalizability of our model is verified by applying it to different building models and under various weather conditions.

Assessment methodology for dynamic occupancy adaptive HVAC control in subway stations integrating passenger flow simulation into building energy modeling

Heating, ventilation, and air conditioning (HVAC) systems in subway stations are often pre-scheduled to non-discriminatorily control large areas, ignoring uneven and dynamic thermal demand. Although building energy modeling (BEM) contributes to optimizing HVAC design and operation, it is limited in investigating dynamic occupancy adaptive HVAC control strategies in large open subway stations. This research formalized an integrated thermal control methodology (ITCM) that integrates passenger flow simulation and BEM to assess the dynamic occupancy adaptive HVAC control strategies for subway stations. To evaluate the applicability and effectiveness of ITCM, we developed a prototype coupling AnyLogic and EnergyPlus and carried out a simulation-based case study using a Hong Kong subway station. Based on the case study, we identified that ITCM enables subway station development and management teams to assess the comprehensive dynamic occupancy adaptive HVAC control strategies (e.g., decomposed thermal zones considering the layouts of diffusers and variable air volume boxes) in a timely and consistent manner. In addition, the case study results show that adaptive control with ceiling diffusers could save between 1.8% and 24.4% of cooling energy without considerably sacrificing thermal comfort.

Data-Driven Modeling of HVAC Systems for Operation of Virtual Power Plants Using a Digital Twin

Confronted with the climate crisis, the world is making tremendous efforts in energy transition, such as expanding renewable energy that does not emit carbon. The importance of virtual power plant (VPP) operation technology has emerged to secure grid flexibility in response to the expanding renewable energy implemented due to these efforts. Accordingly, VPPs, which include photovoltaics, wind turbines, heating, ventilation, and air conditioning (HVAC), load, and EV, have been constructed. HVAC, one of the component resources, is a system that controls and regulates temperature, humidity, and airflow. Since it responds sensitively to the building’s heat capacity and changes in the external environment, it requires continuous and stable control. In this paper, we used data-based modeling to implement the HVAC required for the optimal operation of VPP. Since accurately creating an equation-based HVAC model was difficult considering building information modeling and external environment variables, we used historical HVAC operation data to perform data-based modeling. The model was implemented using nonlinear regression and machine learning, such as a support vector machine and artificial neural network. Then, the data-based HVAC and the actual HVAC operation results were comparatively analyzed based on a case study, and the model’s goodness-of-fit was evaluated based on performance metrics. Model performance indicators confirmed that the ANN-based HVAC model was most similar to the actual HVAC system.

Data-driven control of airborne infection risk and energy use in buildings

The global devastation of the COVID-19 pandemic has led to calls for a revolution in heating, ventilation, and air conditioning (HVAC) systems to improve indoor air quality (IAQ), due to the dominant role of airborne transmission in disease spread. While simple guidelines have recently been suggested to improve IAQ mainly by increasing ventilation and filtration, this goal must be achieved in an energy-efficient and economical manner and include all air cleaning mechanisms. Here, we develop a simple protocol to directly, quantitatively, and optimally control transmission risk while minimizing energy cost. We collect a large dataset of HVAC and IAQ measurements in buildings and show how models of infectious aerosol dynamics and HVAC operation can be combined with sensor data to predict transmission risk and energy consumption. Using this data, we also verify that a simple safety guideline is able to limit transmission risk in full data-driven simulations and thus may be used to guide public health policy. Our results provide a comprehensive framework for quantitative control of transmission risk using all available air cleaning mechanisms in an indoor space while minimizing energy costs to aid in the design and automated operation of healthy, energy-efficient buildings.

Data-driven online energy management framework for HVAC systems: An experimental study

The optimization of the operation of heating, ventilation, and air conditioning (HVAC) systems has gained considerable attention due to its energy conservation potential. Collecting various building operation data presents an opportunity to improve the efficiency of energy management and reduce consumption. In this paper, a data-driven online energy management framework is proposed for enhancing the effectiveness of HVAC systems and minimizing energy consumption. To streamline the modeling process and improve the accuracy of temperature estimation, our proposed framework adopts a symbolic regression approach to estimate building thermodynamics based on the collected data. It also formulates a model predictive control (MPC)-based HVAC scheduling strategy to optimize HVAC operations while maximizing thermal comfort and reducing energy consumption and peak power demand. We conducted on-site experiments to evaluate the proposed framework and demonstrated a notable reduction in total energy consumption in both HVAC cooling and heating operations: average reductions of 4.9% and 30.2%, and 49.3% and 73.9% for best cases in the on-site experiment under certain conditions. In the heating operation, the proposed data-driven HVAC management framework efficiently utilized indoor/outdoor heat generation to maintain comfortable temperature levels without activating the HVAC. Such effective utilization of heat sources shortened the HVAC runtimes and further reduced the total energy consumption. With the proposed framework, the average HVAC peak power demand fell by 25.8% and 35.1% during cooling and heating operations in the on-site experiment with certain scenarios. On-site experiment results demonstrated the proposed framework can efficiently suppress peak power demand, reduce energy consumption in both cooling and heating operation under certain conditions. The framework’s data-driven approach provides an effective means of optimizing HVAC operations, reducing energy consumption, and improving the thermal comfort of the building’s occupants.

Analysis of the Influence of Office Building Operating Characteristics on Carbon Emissions in Cold Regions

Reducing buildings’ operational carbon dioxide emissions has become a crucial element in China’s efforts to achieve carbon peak and carbon neutrality targets. This study focus on the influence of office building operating characteristics on carbon emissions in cold regions. By utilizing DesignBuilder v7.0.0.096 to conduct numerical simulations of 10 different operating conditions for heating, ventilation and air-conditioning (HVAC) and lighting systems, this study solves the problems in the past of poor comfort and high energy consumption with manual management and achieves a win-win situation for health and environmental protection. The study shows that by implementing a mixed mode of mechanical ventilation and natural ventilation based on outdoor climate conditions and design requirements, unsatisfied hours can be reduced by 202 h compared to the traditional air condition heating operation mode for both winter and summer seasons. Furthermore, compared to a year-round HVAC operation mode, the air-conditioning energy consumption can be reduced by 19%, resulting in a carbon emissions reduction of 1.45 kg CO2/(m2·a). Additionally, for every 2 °C increase in the outdoor temperature, the cooling energy consumption decreases by 2–5%. In terms of lighting, the intelligent lighting mode can reduce energy consumption by 31.04%, leading to a carbon emissions reduction of 3.04 kg CO2/(m2·a). The coupling operation characteristics of mixed mode, intelligent lighting, and energy-saving lamps can achieve a maximum saving of 83.46 MWh of electricity and approximately CNY 72,000 every year, with a static payback period of approximately 2.7 years. This operational strategy, which fully considers the utilization of natural ventilation and daylighting in conjunction with traditional design approaches, improves indoor air quality and ventilation conditions, while also maximizing the energy-saving and carbon reduction potential. The study results provide valuable design and operational guidance for new and existing office buildings in cold regions, to effectively reduce carbon emissions, while offering significant investment returns.

Collective comfort optimization in multi-occupancy environments by leveraging personal comfort models and thermal distribution patterns

Buildings with shared spaces present a unique challenge for maintaining thermal comfort due to their dynamic occupancy patterns and the potential for wide variation in occupant thermal preferences and tolerances. Conventional approaches aim to achieve relatively uniform temperature conditions throughout a space, which implies that the thermal environment will likely be suboptimal for many of the individual occupants. Recent research efforts have integrated personal comfort models with heating, ventilation and air conditioning (HVAC) controls and have shown promising improvements by taking a highly individualistic approach to evaluating thermal comfort and adjusting HVAC operations accordingly. In this work, we aim to further advance occupant-centric controls by evaluating the benefits that could be gained by explicitly influencing and leveraging the development of non-uniform thermal conditions within a space. In particular, we consider the context of a multi-occupancy open office space shared by six occupants, where the thermal distribution patterns can be influenced by controlling the direction and flow rate of conditioned air being supplied through a central diffuser. Computational fluid dynamics was used to model and simulate thermal distribution patterns. Six probabilistic thermal comfort profiles were used to quantify the likelihood of each occupant being comfortable under the various control settings and location assignments. We analyzed three control strategies with incremental complexity, and collective comfort probability was shown to improve by 11%, 22%, and 30%, respectively. Our results also showed the potential energy-saving pathway by altering supply airflow direction instead of changing supply airflow rate to adjust thermal conditions in shared environments.

Thermal comfort-conscious eco-climate control for electric vehicles using model predictive control

In the heating, ventilation, and air-conditioning (HVAC) system of electric vehicles (EVs), an electric heater is often used for a reheating process that warms up the chilled evaporator outlet air for thermal comfort before it is supplied to the cabin. The usage of the electric heater can significantly reduce the driving range of an EV. Therefore, optimal control of the HVAC system with consideration of the reheating process becomes essential for an increased driving range. In addition, considering the primary role of cabin climate control, it is desirable to intelligently consider the passengers’ thermal comfort in climate control design. In this paper, thermal comfort-conscious eco-climate control (TCC-ECC) based on model predictive control (MPC) is proposed to enhance the energy efficiency of the HVAC operation while ensuring passengers’ thermal comfort. The MPC design uses a reduced-order HVAC system model based on an ideal vapor-compression cycle. For the integration of thermal comfort into the MPC, a new approach is proposed to obtain an approximate solution of a predictive mean vote (PMV)-based thermal comfort model, which aims to balance computational efficiency and prediction accuracy. With the proposed TCC-ECC, a parametric study is conducted to analyze the impact of weighting factors on energy consumption and thermal comfort under two different thermal load conditions. Then, the performance of the tuned TCC-ECC is evaluated in comparison with a rule-based (RB) controller and the baseline eco-climate control (ECC) without considering thermal comfort. In the performance evaluation, the proposed TCC-ECC demonstrates that with the inclusion of thermal comfort it performs better in terms of energy efficiency and thermal comfort than manually adjusting a target cabin temperature depending on the environmental thermal load. The energy consumption of the proposed TCC-ECC is 22.5% and 35.24% less than that of the RB controller at an ambient temperature of 24 °C and 35 °C, respectively, and 14.5% and 18.5% less than the baseline ECC at the same conditions, respectively.

The Role of IoT in Enhancing HVAC Control Systems

This comprehensive research paper investigates the paradigm shift in Heating, Ventilation, and Air Conditioning (HVAC) control systems propelled by the transformative integration of the Internet of Things (IoT). In an era marked by the convergence of digital technologies, the infusion of IoT into HVAC systems heralds a new era of dynamic, interconnected control mechanisms. This study undertakes a thorough examination of the evolving landscape, shedding light on the profound advancements, discernible benefits, and nuanced challenges intrinsic to harnessing IoT for the augmentation of HVAC control systems. The journey begins by elucidating the fundamental shifts catalyzed by the assimilation of IoT in HVAC systems. The traditional boundaries of HVAC control are transcended as interconnected devices seamlessly communicate, fostering an environment where each component becomes an intelligent node in a networked ecosystem. Real-time data exchange becomes the bedrock, facilitating a level of monitoring and control hitherto unseen. The paper explores the intricacies of this interconnectedness, unveiling the potential for granular control and adaptability that IoT ushers into HVAC operations. A focal point of this research is the exploration of the tangible benefits that arise from this symbiosis of IoT and HVAC control. The paper meticulously examines how real-time monitoring empowers system operators with unprecedented insights into performance metrics, energy consumption patterns, and environmental conditions. Harnessing this wealth of data, IoT-equipped HVAC systems demonstrate an unparalleled capacity for adaptive control, responding dynamically to fluctuating demands and external variables. The consequential improvements in energy efficiency and resource utilization contribute not only to operational cost savings but also align with global sustainability objectives. However, in the pursuit of technological advancement, challenges inevitably emerge. This research critically evaluates the impediments and challenges inherent in the integration of IoT into HVAC control systems. Security concerns, data privacy issues, and the evolving landscape of technology standards are among the multifaceted challenges explored in depth. The paper endeavors to provide a nuanced understanding of these challenges, offering insights that can inform the development of robust and resilient IoT-enabled HVAC control systems

Energy Efficiency in HVAC Systems through Building Automation

This research delves into the imperative realm of enhancing energy efficiency within Heating, Ventilation, and Air Conditioning (HVAC) systems by harnessing the capabilities of building automation. While HVAC systems are vital for maintaining optimal indoor comfort, they often contribute significantly to energy consumption. Building automation stands out as a strategic solution, optimizing HVAC operations based on real-time data, occupancy patterns, and external conditions. This paper scrutinizes the current landscape of energy efficiency in HVAC systems, delineates the role of building automation, and elucidates the manifold benefits of integrating smart technologies in both commercial and residential buildings. In the pursuit of sustainable and energy-efficient building practices, this research examines the intricate relationship between HVAC systems and building automation technologies. It emphasizes the critical need to address energy consumption challenges associated with HVAC systems, considering both environmental impacts and economic considerations. The investigation encompasses an in-depth analysis of the current state of energy efficiency in HVAC systems, shedding light on prevalent inefficiencies and challenges faced by traditional systems. As a solution, building automation emerges as a transformative force, utilizing advanced control strategies and real-time insights to optimize HVAC operations.

Wireless, AI-enabled wearable thermal comfort sensor for energy-efficient, human-in-the-loop control of indoor temperature

The conventional heating, ventilation, and air conditioning (HVAC) systems are based on a set-point control approach that only considers the temperature of the environment without reflecting the thermophysiological status of the occupant. This approach not only fails to fully satisfy individual thermal preferences, but it also makes an HVAC operation energy-inefficient. One possible solution is to control the indoor thermal condition based on an accurate prediction of the occupant's thermal comfort to prevent any unnecessary energy consumption. Here, we present an artificial intelligence (AI) wearable sensor-based human-in-the-loop HVAC control system that is operated on a real-time basis reflecting the thermophysiological condition of the occupant to automatically improve their thermal comfort while reducing the energy consumption of the building. The wristband-type, AI-based, three-point wearable temperature sensor offers excellent thermal comfort prediction accuracy (93.9%), enabling a human-centric HVAC control operation. A proof-of-concept demonstration of closed human-in-the-loop HVAC control using the AI-enabled wearable sensor system confirms both the accuracy of the thermal comfort prediction and the energy-efficiency of this approach, demonstrating its potential as a new solution that improves the occupant's thermal comfort and provides building energy savings.

Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings

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.

Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends

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.