Heating, Ventilation And Air Conditioning System Research Articles

Demand Response and HVAC Controls in Smart Grid Integration

This extensive research paper meticulously investigates the intricate synergies between demand response strategies and Heating, Ventilation, and Air Conditioning (HVAC) controls within the dynamic realm of smart grid integration. As the global energy landscape undergoes transformative changes driven by technological advancements and sustainability imperatives, the study critically examines the symbiotic relationship between consumers, HVAC systems, and the smart grid. The paramount importance of this interaction is underscored, emphasizing its pivotal role in achieving elevated levels of energy efficiency, grid reliability, and sustainability. The research explores the multifaceted potential inherent in demand response programs and advanced HVAC controls, unraveling their collective capacity to reshape the energy ecosystem. It goes beyond the theoretical framework by delving into real-world applications, case studies, and cutting-edge technological advancements. By doing so, the study seeks to provide not only a theoretical understanding but also practical insights into the dynamic and evolving landscape of demand-side management. This investigation is poised to contribute significantly to the existing body of knowledge by illustrating how the harmonious coordination of consumer behaviors and HVAC systems can be strategically harnessed. This strategic collaboration aims to optimize energy consumption, mitigate peak loads, and contribute substantively to the establishment of a more sustainable, adaptive, and responsive energy infrastructure. As the global community grapples with the imperative to transition towards cleaner and more efficient energy systems, this research positions itself as a valuable resource for policymakers, researchers, and industry stakeholders seeking innovative solutions for a resilient and sustainable energy future.

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.

Attention-empowered transfer learning method for HVAC sensor fault diagnosis in dynamic building environments

The Heating, Ventilation and Air Conditioning (HVAC) system is a key system in buildings for providing energy-saving and occupant-centered indoor services. Sensor fault diagnosis (SFD) is essentially important for HVAC systems since incorrect sensory measurements may destabilize system operations. Most SFD methods for HVAC systems require comprehensive manual knowledge or massive labeled data which is hardly available in diverse buildings. To reduce labor cost, transfer learning method is adopted in diagnosing HVAC sensor faults, however, it still encounters critical challenges, such as imbalanced data distribution and insignificant fault features. In this paper, an attention-empowered transfer learning method is innovatively proposed to enhance the capability of SFD in HVAC systems. Firstly, we introduce the multi attention-based module to characterize sensor faults in spatio-temporal domain. Secondly, we explore the relationship between source domain and target domain, and establish the model that enables useful knowledge mapping. Thirdly, to alleviate the effect of data discrepancy existed in new domains, a joint domain loss function is designed to enhance the domain adaptive ability. Experimental validation is carried out on three datasets from completely different real-world scenarios. Compared with existing methods (i.e., TCA, JAN and DDC), the proposed method achieves the highest average accuracy of 93 % in cross-domain building environments. The ablation studies also demonstrate the effectiveness of our method under diverse parameter settings, such as attention modules, fault types and data volume.

Numerical simulation of thermosolutal natural convection of power-law non-Newtonian fluids in a parallelogram with sensitivity analysis by response surface methodology

Thermosolutal natural convection originates in a fluid when the density changes due to the presence of two distinct components with varying rates of diffusion. This convection is driven by buoyancy resulting from concurrent temperature and concentration gradients. This study focuses on the thermosolutal free convection flow within a parallelogrammic enclosure. The functioning fluid under consideration is non-Newtonian and formulated by the viscosity model (power-law). The top and bottom walls of the chamber are adiabatic and are assumed to be impermeable. The temperature and concentration on the left side are maintained at a high level, while the conditions on the right side are cold and low. The controlling parameters for the present case include Rayleigh number Lewis number (Le = 5, 10), Prandtl number (Pr = 6.2), buoyancy ratio (N = 0.8, −0.8) along with power-law index (n = 0.6, 1, 1.4). The results obtained from this numerical study are validated with existing results and indicate the correctness of the code. The output of the study is shown in terms of velocity and temperature profiles, streamlines, isotherms, and iso-concentrations, the average rate of heat and mass transmission in terms of the average Nusselt number () and the average Sherwood number (). The correlation equation derived from the RSM approach demonstrates the relationship between the output responses and the input parameters. It concludes that n negatively affects both heat and mass transfer, while Ra affects positively. The findings of this study could be helpful for understanding of thermosolutal natural convection behavior of non-Newtonian (power-law) fluid in an enclosure and therefore accelerate the industrial application of such fluid in the relevant field, such as HVAC (heating, ventilation, and air conditioning) system.

An unsupervised data mining-based framework for evaluation and optimization of operation strategy of HVAC system

The evaluation and optimization of existing operation strategies are significant for energy-saving in heating, ventilation, and air conditioning (HVAC) systems. The personalized characteristics of HVAC systems, however, pose a challenge in developing the baseline. This paper proposes an unsupervised data mining-based framework for the evaluation and optimization of HVAC system operation strategies. The framework involves several steps: firstly, data preprocessing is performed to clean and reduce the raw data. Then, an improved clustering method is used to divide the external conditions of system operation, followed by an association rule algorithm to mine the evaluation baseline for operation strategies. Finally, the low-energy efficiency operating strategies can be identified and optimized using the baseline. Validity of the proposed framework is confirmed by practical cases and data. The results indicate that the mined operation evaluation baseline effectively identifies low-energy efficiency operating strategies that can be improved, resulting in energy savings of 6.9 %. Notably, optimizing the operation number of chillers and water pumps has the best energy-saving potential. Furthermore, compared to model-based operation evaluation methods, this data-driven approach offers advantages such as low computational costs and quick execution.

Investigating the impact of HVAC and Sanitizer system design on the transmission of SARS-CoV-2 in Hospital Isolation Units

The COVID-19 pandemic has presented unprecedented challenges to healthcare facilities worldwide. Hospital isolation units have played a crucial role in the management of COVID-19 patients. However, the transmission of the virus within these units can lead to severe consequences for patients, healthcare workers, and the broader community. This research proposes a framework to evaluate the impact of HVAC (Heating, Ventilation, and Air Conditioning) and sanitizer system design on the transmission of SARS-CoV-2 in isolation unit at Saniat Rmel Hospital in Tetouan Morocco by using computational fluid dynamics (CFD) and the realizable k−ϵ turbulence model. Furthermore, the study investigates the effect of different inlet air velocities on the dispersion of airborne viral particles in the isolation unit. The results indicate that the air change rate plays a critical role in the spread and concentration of pathogens in the isolation unit. It was determined that the high inlet air velocity can lead the particles fluid including the virus reaches the exhaust port in a short time, in addition, it can facilitate the distribution of sanitizer throughout an isolation unit, which could potentially reduce the concentration of the virus. The outcome suggests that the placement and orientation of the air conditioner combined with the sanitizer system have a significant impact on the movement characteristics of fluid particles within the enclosed environment.

Fault detection, diagnosis and calibration of heating, ventilation and air conditioning sensors by combining principal component analysis and improved bayesian inference

Sensors are crucial components in heating, ventilation and air conditioning (HVAC) systems. When sensors fault, the HVAC system deviates from normal operation, resulting in an increase in energy consumption and a decline in equipment lifespan. Therefore, it is vital to detect, diagnose and calibrate the sensor faults. Bayesian Inference (BI) in data-driven methods is highly effective for sensor calibration. However, its application faces two major shortcomings. First, the conventional BI method cannot effectively calibrate sensor drift faults. Second, the BI method relies on sensor fault detection and diagnosis (FDD). To address these shortcomings, this study proposed an improved BI (imBI) method that enhanced the capability of BI to calibrate drift faults by updating the calibration constant x. Moreover, sensor fault detection, diagnosis and calibration (FDDC) were conducted by combining Principal Component Analysis (PCA) with the imBI method (PCA-imBI). The proposed methods were validated through two case studies involving a chiller system and an air handling unit (AHU) system, having different degrees of bias faults and drift faults in temperature sensors, differential pressure sensors and flow sensors. The results demonstrated that the PCA-imBI method effectively achieved FDDC for both bias and drift faults. Considering the case studies, the average calibration rates for bias and drift faults reached 99.74 % and 95.29 %, respectively. For drift fault calibration, the imBI method outperformed the dynamic calibration method and PCA reconstruction method by 16.04 % and 4.31 %, respectively.

Development of an HVAC system control method using weather forecasting data with deep reinforcement learning algorithms

Heating, ventilation, and air conditioning (HVAC) systems account for a significant proportion of the energy consumption of a building. With the global energy demand increasing every year, optimal HVAC control methods that reduce energy consumption while maintaining the thermal comfort of the occupants have become more important. Weather forecasting data have thus attracted significant research attention for the optimization of building energy consumption in model-based studies. However, these HVAC control approaches have failed to consider the reduction in the predictive error of the incorporated weather model, leading to performance limitations. Accordingly, model-free reinforcement learning (RL)-based HVAC systems that can be controlled dynamically have been proposed, and these have exhibited significantly better performance than model-based methods. However, RL algorithms using weather forecasting data have not yet been assessed for use in optimal HVAC control for buildings. In this study, we propose WDQN-temPER, a hybrid HVAC control method combining a deep Q-network with a novel prioritized experience replay (PER) technique (referred to as temPER) and a gated recurrent unit (GRU) model. The GRU model predicts the future outdoor temperature and this is used as a state variable in the RL model. The proposed temPER algorithm facilitates the use of samples in the RL training process with larger changes in the outdoor temperature based on the predicted temperature. We experimentally demonstrate in EnergyPlus simulations that our proposed WDQN-temPER model outperforms a rule-based baseline model in terms of HVAC control, with energy savings of up to 58.79%.

Barriers and drivers for implementation of automatic fault detection and diagnosis in buildings and HVAC systems: An outlook from industry experts

This study aimed to assess the current status of Fault Detection and Diagnosis (FDD) implementation in building and Heating, Ventilation and Air Conditioning (HVAC) systems in the building industry. Semi-structured qualitative interviews were conducted with 29 experts from different HVAC company types in the building industry. In addition, a literature review was performed to investigate academic research on FDD implementation. The study identified barriers and drivers to implementing FDD systems, these included; technological and technical, economic and business, users, social and societal, and regulatory. An Automatic Fault Detection and Diagnosis (AFDD) implementation matrix was developed to evaluate FDD implementation in building systems, and all interviewed companies were classified based on their FDD knowledge, services, and type. Results show that expert-rule systems are still prevalent in the industry. The literature review revealed a scarcity of FDD implementation studies in academic research due to challenges in testing and validating results in actual building operation conditions. Lastly, this study discusses the key findings: 1) FDD does not sell, 2) Lack of actively engaging and promoting FDD services, 3) FDD seems to be an academic definition, 4) The bottlenecks: The fault handling process and user’s mindset towards FDD, and 5) Governmental regulations and legislatives drive the implementation focus.

Portable Air Purifiers’ Predicted Efficacy in Mitigating Airborne Pathogen Transmission in an Office Room Featuring Mixing Ventilation

Portable air purifiers have been extensively used to improve indoor air quality and mitigate the transmission of airborne diseases. However, the efficacy of mitigation is strongly affected by the interactions between jet flows of processed air from the air purifiers and the background airflows driven by the ventilation system. Critical factors in this context include the position and capacity of air purifiers and the ventilation rate of the heating ventilation and air-conditioning (HVAC) system. These factors are investigated in this study via computational fluid dynamics (CFD) simulations and the infection probability for different scenarios is quantified using the latest airborne infection predictive model incorporating recent pathological and clinical data for SARS-CoV-2. The results show that the use of air purifiers can significantly reduce the concentration of particulate matter, thus contributing to a generally lower risk of airborne transmission. However, the position of air purifiers affects their overall efficacy remarkably. Comparatively, a central HVAC system is more efficient at removing airborne particles under an equivalent ventilation rate assuming it uses a mixing ventilation scheme.

A hybrid control strategy for frequency regulation with variable refrigerant flow air conditioning system

Heating, ventilation and air conditioning (HVAC) systems of buildings are abundant sources for the Frequency Regulation (FR) ancillary service, whose actuating devices are controlled to follow the real-time power reference signals with the contractual capacity. For HVAC practice, model-based FR controls are hindered by expensive model acquisition. This paper presents a two-layer control strategy for HVAC systems to perform energy efficient FR service, with a rather low model requirement. At the lower level, the FR operation with a selected actuator is realized with a sliding mode control which is robust to power map variation. At the higher level, system efficiency is optimized in real time via a model-free constrained extremum seeking control (CESC). A conceptual variable refrigerant flow (VRF) system is used as the illustrative application, for which the outdoor unit fan offers the FR power reference tracking with SMC. At the outer loop, the compressor discharge pressure is adjusted by a CESC to minimize the total power consumption while the zonal temperature regulation is reinforced as equality constraint. The proposed strategy is evaluated with simulations on a Modelica simulation, and the results validate its effectiveness. The Net Energy Cost can reduce by 15% to 23% under different load levels.

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.

Analysis of demisting strategies for electric vehicles

Worldwide expansion of electric vehicles is more than obvious nowadays. However, there are still problems related to their driving range. Consequently, solutions for continuous improvement of electric vehicles range are required. In this context, more efficient HVAC (Heating, Ventilation, and Air Conditioning) systems, including defogging and defrosting, are extremely important as the use of these systems inevitably leads to significant decrease of electric vehicles range. Accordingly, the goal of this study is to investigate demisting windshield strategies in terms of energy consumption (without posing safety road concerns related to driver’s visibility). The approach is based on Computational Fluid Dynamics (CFD) full-scale simulations of a vehicle cabin, including special (innovative) model concerning windshield condensation. Different demisting strategies are analyzed, based on the developed numerical model: several supply air velocities for demisting grids and levels of power for heating up the windshield. The results indicate that, under the conditions of this study, choosing lower supply air velocity in conjunction with higher power levels of heated windscreen can lead to more energy efficient demisting strategies. Finally, the methodology proposed in this work can be easily extrapolated for other conditions to achieve comprehensive databases concerning the optimization of windscreen demisting strategies for electric vehicles.

Quantification of HVAC energy savings through occupancy presence sensors in an apartment setting: Field testing and inverse modeling approach

While many simulation-based studies have demonstrated the energy-saving potential of occupancy-driven smart thermostats in residential buildings, field testing of these devices remains relatively limited. The objective of this study is to propose a new field-testing approach to evaluate the heating, ventilation and air-conditioning (HVAC) energy-saving potential of occupancy-driven thermostats and occupancy-centric controls (OCC) for HVAC in a residential apartment in Texas, U.S. A proprietary prototype occupancy sensing system was chosen for this study, coupled with a Raspberry Pi-based hardware framework to facilitate OCC implementation by linking occupancy sensors to the existing HVAC system. Energy consumption of the HVAC system during both non-OCC baseline and OCC modes was measured using various sensors installed at the test site. Furthermore, XGBoost models were developed, used onsite measurements, to consider the effects of various factors like occupancy and outdoor weather conditions on HVAC energy consumption to facilitate a fair comparison. The scope of the analysis was further expanded to cover the period from 2018 to 2022 and Typical Meteorological Year 3 (TMY3) by incorporating a statistical occupancy schedule generator. The results suggest that approximately 15.1% cooling energy consumption could be saved during the testing period, equivalent to around 109 kWh in electricity savings. Moreover, OCCs have the potential to achieve electricity savings ranging from 300 to 330 kWh in the months between April and September, depending on the weather in each year. This corresponds to a cooling energy saving ratio ranging from 19% to 24%. Despite energy savings, OCC had some minor adverse effects on their thermal comfort and perceptions of Indoor Environmental Quality (IEQ). Moreover, residents' ratings for indoor thermal comfort dropped from neutral to slightly dissatisfied after the implementation of OCCs.

Handling fault detection and diagnosis in incomplete sensor measurements for BAS based HVAC system

Due to the development of sensor networks, Building Automation System (BAS) based heating, ventilation, and air conditioning (HVAC) has gradually replaced traditional HVAC to improve indoor thermal comfort and decrease energy consumption. Because of inevitable faults in sensors or networks, the real-time sensor data may be incomplete with corrupted, lost or undetected missing values. Therefore, Improved Fireworks Algorithm (IFWA)-Long Short-Term Memory (LSTM) is proposed to recover incomplete measurements in BAS HVAC sensors. Because sensor data loss mostly results from incorrect control commands from the BAS system or sensor damage, there is a high probability of concurrent sensor fault. Therefore, Independent Component Analysis (ICA)-K-Nearest Neighbors (KNN) is proposed to monitor the sensor's healthy state under the measurements recovered. This paper discusses the possible causes of incomplete measurement in BAS HVAC sensors and simulates the distribution by experiment. The experiment analyzes the data recovery capability of IFWA-LSTM for different types of missing sensor data in different sensor measurement loss scenarios. The result shows that compared to the comparative methods, IFWA-LSTM achieves a 6.45 %–86.92 % improvement in recovery accuracy and a 0.0041 to 0.6340 improvement in correlation coefficients. Subsequently, it discusses the fault diagnosis rate of ICA-KNN under full measurements and incomplete measurement recovered by IFWA-LSTM. Overall, ICA-KNN achieves an average fault diagnosis rate of 99.86 % and an average fault alarm rate of 0 % under full measurement. When applied to incomplete measurement recovered by IFWA-LSTM, ICA-KNN exhibits an average fault diagnosis rate of 92.18 % and an average fault alarm rate of 1.22 %.

Scheduling of the HVAC system in a real commercial building considering equipment cycling and rebound effects

Commercial buildings consume a large portion of energy and the heating, ventilation, and air conditioning (HVAC) system is the major contributor to this high consumption. Though there are many reported works on the optimal scheduling of HVAC systems, practical considerations such as equipment cycling and possible rebound effects are rarely considered. This paper provides insights into the optimal scheduling of the HVAC system in an actual 3-floor commercial building where the cycling and rebound effects are addressed in the optimization. The experimental results prove that energy cost reduction can be achieved while ensuring temperature comfort and longer life expectancy of the HVAC. The results also show that considering the cycling and rebounds will affect the potential cost reduction.

Experimental Validation of the HVAC Humming-type Noise and Vibration in Model and Vehicle System Levels

The presence of noises in the vehicle cabin is an annoyance phenomenon which is significantly affected by the heating, ventilation, and air conditioning (HVAC) system. There are very limited studies reported on the specific type of noise characterisation and validation for both model and vehicle system levels. The present study developed a model of HVAC system that reflects the operation as in real vehicle, and the investigation of the HVAC components were carried out individually to determine which component contributes to the humming-type noise and vibration. The study was conducted under two conditions; idle speed of engine (850 rpm) and operating condition (850–1400 rpm). A ixed blower speed and fullface setting were applied throughout the experimental process. Three different sensors were used for the experiment, which are: accelerometer, tachometer, and microphone. From the results, the compressor and AC pipe components have contributed the most in generating the noise and vibration for both the model and vehicle systems. The findings also highlight that the humming-type noise and vibration were produced in the same operating frequency of 300–400 Hz and 100–300 Hz for idle and operating conditions, respectively, and this result was validated for both model and vehicle system levels.

Numerical Analysis of Air-Water Heat Exchanger as Passive Cooling System in Air Ventilation

Indonesia has a humid tropical climate and many major cities are located along the coast, causing a large energy demand for thermal comfort. It takes a lot of energy to get thermal comfort. So one of the effective strategies for energy efficiency of HVAC (Heating, Ventilation, and Air Conditioning) systems is passive cooling techniques. Therefore, savings are made by using heat transfer devices as passive cooling in air vents. This research was conducted to see the comparison between experimental data and Computational Fluid Dynamic (CFD) simulations. The implementation method in this research was carried out on Solidoworks 2020 software. Based on experimental data, the inlet temperature is 32.35˚C, the outlet temperature is 28.4˚C and the water temperature is 27.58˚C and the mass flow rate is 0.0936 kg/s, the effectiveness is 0.228, then a variation with a mass flow rate of 0.1872 kg/s is obtained with an effectiveness of 0.122 and a mass flow rate variation of 0.2808 kg/s is obtained with an effectiveness of 0.083.

EECO: An AI-Based Algorithm for Energy-Efficient Comfort Optimisation

Environmental comfort takes a central role in the well-being and health of people. In modern industrial, commercial, and residential buildings, passive energy sources (such as solar irradiance and heat exchangers) and heating, ventilation, and air conditioning (HVAC) systems are usually employed to achieve the required comfort. While passive strategies can effectively enhance the livability of indoor spaces with limited or no energy cost, active strategies based on HVAC machines are often preferred to have direct control over the environment. Commonly, the working parameters of such machines are manually tuned to a fixed set point during working hours or throughout the whole day, leading to inefficiencies in terms of comfort and energy consumption. Albeit effective, previous works that tackle the comfort–energy tradeoff are tailored to the specific environment under study (in terms of geometry, characteristics of the building, etc.) and thus cannot be applied on a large industrial scale. We address the problem from a different angle and propose an adaptive and practical solution for comfort optimisation. It does not require the intervention of expert personnel or any customisations around the environment while it implicitly analyses the influence of different agents (e.g., passive phenomena) on the monitored parameters. A convolutional neural network (CNN) predicts the long-term impact on thermal comfort and energy consumption of a range of possible actuation strategies for the HVAC system. The decision on the best HVAC settings is taken by choosing the combination of ON/OFF and set point (SP), which optimises thermal comfort and, at the same time, minimises energy consumption. We validate our solution in a real-world scenario and through software simulations, providing a performance comparison against the fixed set point strategy and a greedy approach. The evaluation results show that our solution achieves the desired thermal comfort while reducing the energy footprint by up to approximately 16% in a real environment.

Coupling BIM and detailed modelica simulations of HVAC systems in a common data environment

In current building design practices, the operation and performance of heating, ventilation and air conditioning (HVAC) systems are rarely documented in detail. This is mainly caused by the manual burden of generating detailed HVAC simulation models. To lower the barrier for detailed HVAC simulations, this paper presents an automated toolchain that generates and simulates models in Modelica language utilizing building information structured in a web-based common data environment. This approach differs from previous approaches by its focus on HVAC systems and integration with a common data environment over file-based BIM, which increases interoperability and allows users to run simulation studies in the cloud. The tool successfully generated and simulated a model of the HVAC systems in a small building under ideal and faulty operation of the heating system and thus demonstrated a fully interoperable data exchange between a common data environment and a simulation environment while showcasing the potential of analyzing HVAC systems with Modelica.