Heating, Ventilation And Air Conditioning System Research Articles

Optimizing ventilation systems considering operational and embodied emissions with life cycle based method

Efforts to enhance the energy efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems have been bolstered by technical advancements and stringent regulations. However, HVAC systems not only emit during operation due to energy consumption but also have significant embodied emissions, which recent studies show can exceed those of the operational phase. This study introduces an optimization framework aimed at minimizing lifetime emissions—both operational and embodied—for ventilation systems, an area previously underexplored. The optimization framework incorporates detailed calculations of pressure drop, fan power and newly developed life cycle ventilation inventory data with a life cycle assessment perspectiveA case study of ventilation ductwork in a “BREEAM Excellent” certified energy-efficient building in Norway applies this optimization framework. Findings indicate that for this case, optimizing ductwork dimensions can reduce lifetime emissions of the ventilation system by 15 %, compared to the existing designs with an emission intensity of 0.3 kg CO2/kWh. Further, the study examines how the emission intensity of electricity generation and service lifetime influence total emissions, highlighting the growing importance of embodied emissions as electricity generation becomes cleaner. This underscores the necessity of considering both operational and embodied emissions in design decisions. This study presents an optimization tool for low-emissions ventilation design, capable of processing diverse layouts and aiding in decarbonizing ventilation systems towards achieving zero-emission buildings. Future integration with building information modeling (BIM) and artificial intelligence (AI) could further enhance autonomous low-carbon design and decision-making.

Accelerating chiller sequencing using dynamic programming

Effective management of heating, ventilation, and air conditioning (HVAC) systems is crucial for enhancing building energy efficiency. Chillers, a significant component of HVAC systems, are responsible for more than 50 % of energy usage of the whole cooling plant, underscoring the importance of optimizing chiller sequencing. Although Model Predictive Control (MPC) has demonstrated efficacy in enhancing chiller performance, its widespread adoption is hindered by its high computational complexity. This study introduces a dynamic programming approach coupled with a 2-step optimization method to expedite chiller sequencing optimization while upholding MPC’s real-time control capabilities. The MPC method implemented in this study integrates a load forecasting model with an artificial neural network (ANN) based chiller model. Through simulation tests utilizing historical HVAC operational data from a commercial building located in Shanghai, the proposed chiller sequencing strategy achieves a 9.73 % decrease in energy consumption. Moreover, the dynamic programming approach, especially in conjunction with the 2-step optimization process, significantly reduces the MPC solution time at each control step from 165 min to 3.61 s, making it a viable solution for real-time MPC deployment. Additionally, the research delves into the influence of the chiller model’s complexity, the optimization strategy, and control horizon on computational efficiency. This research provides a feasible solution to implement chiller sequencing in real buildings to pick up the low-hanging fruits in an effective way.

Investigating the Reliability of Heating, Ventilation, and Air Conditioning Systems Utilized in Passenger Vehicles

A Heating, Ventilation, and Air Conditioning (HVAC) system is often utilized in passenger vehicles to enhance the comfort of both the driver and the passengers. The reliability of an HVAC system refers to the probability that a component within the system will fulfil its intended function during a specified timeframe while operating according to the predefined operational and environmental conditions. Conducting a reliability analysis for the HVAC system of a passenger vehicle is crucial to ensure safety, comfort, cost-effectiveness, and a positive standing. A methodology for analyzing the reliability analysis of a HVAC system using field failure data were developed to identify the critical failure modes, components, and subsystems. A detailed Pareto analysis was applied at subsystem and failure mode levels in order to prioritize them accordingly to their failure frequency. The analysis showed that the A/C evaporator and blower front sides were observed to be the most critical subsystems, contributing to approximately 50% of all failures. Furthermore, the leakages at the joints and vibrations are the primary failure modes of the HVAC system. The Weibull++ software package (version 2021) was used to estimate the best-fit probability distributions for each subsystem and system reliability modelling using a Reliability Block Diagram. The results show that the exponential distribution fits well for several subsystem’s Time-To-Failure (TTF) data and show that the failures were random and due to external reasons.

Fault detection research on novel transfer learning-based method for cross-condition, cross-system and cross-operation in public building HVAC sensors

Transfer learning (TL) has the inspiring potential for artificial intelligence in heating, ventilation and air conditioning (HVAC) system with insufficient data labels. However, traditional TL-based methods are limited when applied across different conditions, systems, and operations.Unfortunately, public building HVAC systems encounter challenges related to data acquisition and richness, making it difficult to obtain data from similar HVAC systems conditions, scenarios and operations. It proposes a novel TL-based method that combines energy and mass balance constraint equation (EBCe) to diagnose the sensor faults in HVAC systems across different systems, conditions and operations.Firstly, it utilizes laboratory data as the source domain data and constructes EBCe based on the common physical laws of HVAC system to reduce the data differences between laboratory and public buildings. Then, an laplacian kernel domain-adaptive neural network (LkDaNN) is proposed to generalize more efficiently feature differences between the source domain data and target domain data. Finally, experiment analyzes the non-fault and four control-sensors fault under both cross-operation and non-cross operation conditions. The experimental results demonstrate that the EBCe-LkDaNN method achieves satisfactory fault detection and diagnosis (FDD) performance.The overall FDD accuracy of porposed method can reach 90.72 % and 88.64 % under different cross-operation, respectively. Practical application of the EBCe-LkDaNN strategy for HVAC sensor FDD are discussed at last.

Cross-condition fault diagnosis of chillers based on an ensemble approach with adaptive weight allocation

The Heating, Ventilation and Air Conditioning (HVAC) systems are complex and prone to failures during operation, often leading to significant energy waste. Timely and accurate Fault Detection and Diagnosis (FDD) can enhance energy efficiency. The HVAC system operates under diverse conditions, data-driven models trained under existing conditions may experience performance degradation when faced with new conditions. Transfer learning offers an effective solution to this issue. This study proposes a novel transfer learning ensemble model based on adaptive weights, leveraging different transfer learning strategies to improve diagnosis performance under new conditions. Multiple cross-condition transfer learning tasks were implemented to test the proposed method, and its effectiveness was validated through multiple experiments to minimize the impact of randomness. Results showed that, compared to fine-tuning (FT), domain-adversarial neural network (DANN), and baseline models, the proposed method outperforms the other models. The average accuracy of multiple experiments improved by 0.21 % to 2.34 % compared to FT. Additionally, modifying DANN to utilize a small amount of labeled information from the target domain has led to greater overlap between the feature distributions of the source and target domains, resulting in improved performance that is close to that of FT. Finally, we analyzed the impact of target domain data volume on the performance of the four methods. The performance of the baseline model improved significantly with the increase in data volume, while the other models showed less improvement. Meanwhile, the diagnostic results of the baseline model were significantly influenced by experimental randomness when there is less training data, whereas the FT diagnostic results were relatively more stable.

Fuzzy logic-supported building design for low-energy consumption in urban environments

Climate, building materials, occupancy patterns, and HVAC (heating, ventilation, and air conditioning) systems all interact in complex ways, making it difficult to design low-energy buildings. Thus, innovative architectural and engineering design strategies are required to meet the worldwide need to decrease building energy usage. To improve the calculation of energy consumption of buildings, this work introduces the FCR-BCS (fuzzy clustering rule-based building control systems), which integrates fuzzy logic concepts into computational simulations. FCR-BCS can contemplate real-world uncertainties and fluctuations using linguistic factors and approximate reasoning for more precise and trustworthy results in energy-efficient building design. This method's significance rests in its potential to significantly reduce energy use, advance sustainability, and improve urban residents' quality of life; architects and engineers can thus employ FCR-BCS to enhance the efficiency of HVAC systems and insulation. The outcomes of FCR-BCS simulation assessments show that it is capable of making buildings more energy efficient. The experimental outcomes demonstrate that the suggested model increases the sensitivity analysis by 99.4 %, energy efficiency analysis by 99.8 %, occupancy patterns analysis by 97.5 %, temperature profile analysis by 98.8 %, and energy consumption analysis by 99.6 % compared to other existing models.

Experimental evaluations of Berkeley thermal sensation and comfort models in electric vehicle cabin under cold outdoor conditions

As the automobile industry is transitioning toward electric vehicles, manufacturers have started implementing local warmers alongside cabin heating, ventilation, and air conditioning (HVAC) systems for effective thermal comfort management. However, optimal operating strategies need to be developed for integrating local warmers with HVAC systems. Although the Berkeley models comprising local/overall thermal sensation and comfort models offer insights in this regard, they lack follow-up assessments for occupants transitioning from very cold states. In this study, Berkeley models were evaluated using two sets of experimental data collected in a transient vehicle cabin under cold outdoor conditions: test (A) with cabin HVAC alone and test (B) with both HVAC and local warmers. The findings confirm the satisfactory performances of the Berkeley models for predicting overall sensation and comfort, with a maximum root mean-squared error (RMSE) of 0.15. The local comfort model performed poorly with the original coefficients across both datasets (maximum RMSE of 1.96). Therefore, the model coefficients were regressed for the dataset from test A and validated against the dataset from test B to achieve a maximum RMSE of 0.49. With these regressed coefficients, it was observed that moving toward a neutral whole-body state diminished the potential to maximize local comfort. Conversely, the local sensation model showed poor agreement (maximum RMSE of 1.9); we confirmed that accurate adaptive setpoint temperatures are a prerequisite for ensuring good predictions from the model. These findings are expected to contribute toward future efforts in using Berkeley models to formulate effective local warmer–HVAC operational strategies in electric vehicles.

Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative Models

The design process of heating, ventilation, and air conditioning (HVAC) systems is complex and time consuming due to the need to follow design codes. Since the design standards are not fixed, the final outcome often depends on the designer’s experience. The development of building information modeling (BIM) technology has made information throughout the building lifecycle more integrated. BIM-based forward design is now widely used, providing a data foundation for combining HVAC system design with machine learning. This paper proposes an unsupervised learning method based on deep graph generative models to uncover hidden design patterns and optimization strategies from the design results. We trained and validated four deep graph generative models—GAE, GNF, GAN, and diffusion—using HVAC system terminal pipeline layout data. Accuracy and precision metrics were used to compare the generated designs with automated forward design solutions, assessing the models’ ability to capture both local variations and broader changes in design logic. A graph-neural-network-based evaluation method was employed to measure the models’ capacity to detect changes. The results indicate that all four models achieved prediction accuracies exceeding 90% and precision rates above 75%. The models effectively captured both local modifications made by designers and global design changes, showing greater sensitivity to global layout adjustments than to local updates. When comparing the results generated by deep graph generative models and the actual design, it is obvious that the accuracy of the predictions varies significantly due to the complexity of the test buildings.

Recent Advances in Desiccant Materials for Energy‐Efficient and Sustainable Heat Transfer Systems

The expanding prevalence of heating, ventilation, and air conditioning (HVAC) systems, currently reliant on fossil fuel‐derived energy, necessitates energy‐saving measures to address climate change concerns. Implementing desiccants with high water vapor affinity reduces latent heat input, impacting the heat exchanger's functionality based on system and desiccant characteristics. This review analyzes the materials in solid desiccants, binders, and substrates, emphasizing advancements for enhanced heat transfer efficiency. Some notable materials explored include metal–organic frameworks, activated carbon, silica gel, polymers, and zeolite. In system level, the article discusses about the dual‐channel heat exchanger which emerges as a solution to drawbacks in existing dehumidification systems, offering higher performance and lower latent heat load in modern solid desiccant‐coated heat exchangers. Despite the environmental impact of material usage, future research should focus on sustainable materials, such as those derived from biomass or recycled sources, to maintain efficiency while minimizing waste in HVAC systems.

Electric Vehicle Thermal System Concept Development for Multiple Variants Using Digital Prototype and AI

The automotive industry is experiencing a surge in system complexity driven by the ever-growing number of interacting components, subsystems, and control systems. This complexity is further amplified by the expanding range of component options available to original equipment manufacturers (OEMs). OEMs work in parallel on more than one vehicle model, with multiple vehicle variants for each vehicle model. With the increasing number of vehicle variants needed to cater to diverse regional needs, development complexity escalates. To address this challenge, modern techniques like Model-Based Systems Engineering (MBSE), digitalization, and Artificial Intelligence (AI) are becoming essential tools. These advancements can streamline concept development, optimize thermal and HVAC system design across variants, and accelerate the time-to-market for next-generation EVs. The development of battery electric vehicles (BEVs) needs a strong focus on thermal management systems (TMSs) and heating, ventilation, and air conditioning (HVAC) systems. These systems play a critical role in maintaining optimal battery temperature, maximizing range and efficiency, and ensuring passenger comfort. This article proposes a digital prototype (DP) and AI-based methodology to specify BEV thermal system and HVAC system components in the concept phase. This methodology uses system and variant thinking in combination with digital prototype (DP) and AI to verify BEV thermal system architecture component specifications for future variants without extensive simulation. A BEV cabin cooling requirement of 22 °C to be achieved within 1800s at a high ambient temperature (45 °C) is required, and its verification is used to prove this methodology.

Barriers to implement energy efficiency strategies to heating, ventilation, air conditioning of airside system in commercial buildings in Sri Lanka

At present, debates on climate change, fossil fuel depletion, and energy saving highlight the need for a sustainable built environment to reduce energy consumption and emission trends. The Heating, Ventilation, and Air Conditioning (HVAC) system is a priority due to its significant share of a building’s electricity consumption. While energy efficiency strategies for central air conditioning systems focus on both waterside and airside components, implementing airside efficiency strategies is notably lacking in Sri Lanka. This is a concern because airside inefficiencies lead to excessive energy use, higher operational costs, and compromised indoor air quality. Thus, the study aims to address this gap by identifying the barriers to implementing energy efficiency strategies for HVAC airside systems in Sri Lankan commercial buildings. To explore these barriers, a qualitative research approach was employed, involving interviews with 17 industry experts. The study identified 27 barriers, with key barriers being a lack of technical expertise, rapid technological advancements, misalignment with organisational objectives, limited financial resources, inadequate training and technical knowledge, and insufficient regulatory support. The insights gained from this study can assist industry professionals in enhancing HVAC energy performance and encourage further academic research on this sub-branch of HVAC systems in the Sri Lankan context.

Adaptive fusion graph convolutional network based interpretable fault diagnosis method for HVAC systems enhanced by unlabeled data

Fault diagnosis is critical in maintaining the operational stability and reliability of Heating, Ventilation, and Air Conditioning (HVAC) systems, which are crucial for ensuring indoor environmental quality and energy efficiency in buildings. However, traditional fault diagnosis methodologies face substantial challenges in accurately capturing the dynamic interactions within these systems and effectively utilizing unlabeled data. To overcome these limitations, an innovative fault diagnosis approach utilizing the Adaptive Fusion Graph Convolution Network (AFGCN) is proposed in this paper. This method significantly enhances the model's learning and inference abilities, particularly in scenarios with limited labeled data, by adaptively integrating the associative graph features of both unlabeled and labeled data. Furthermore, to augment the transparency and trustworthiness of the diagnostic outcomes, this paper introduces an interpretability analysis module. This module is designed to quantify the contribution of each sensor node in the fault diagnosis process. Experimental evaluations using the ASHRAE RP-1043, actual building operating chillers datasets, and LBNL FDD Data Sets_SDAHU indicate that this method offers substantial performance improvements in diagnosing faults in HVAC systems.

Real-time optimal hierarchy control of HVAC system with maximized ancillary service support in smart buildings: An experimental approach

This article proposes a real-time optimum control algorithm for Heating, Ventilation, and Air Conditioning (HVAC) units of a commercial building aimed to maximize the ancillary power available for frequency regulation services. First, the prediction of energy consumption of air conditioning zones using long short-term memory (LSTM) is achieved, which helps forecast the aggregated regulation capacity bid of the building in the energy markets. Furthermore, to minimize energy costs and thermal discomfort of building dwellers, an optimization algorithm is designed considering the day-ahead electricity tariffs. Moreover, using installed sensors in a lab-scaled hardware prototype, the proposed algorithm is also shown to be capable of interacting with a Building Energy Management System (BEMS) and investigating the electrical and thermal properties of the HVAC unit. The BEMS controller uses an ethernet protocol to interface with the sensors installed at various operational points throughout the HVAC system. The optimal operation of the HVAC system has been executed with the real-time thermal feedback system, which counterbalances the uncertain thermal load or renewable power availability in the building confronted in real-time operation. The simulation results show the performance of the Artificial Neural Network (ANN) based compressor model in deep learning integrated with the other components of the HVAC unit and thermal model of the air-conditioning zone. Finally, the experimental results display the real-time implementation of multiple thermal and electrical conditions within the proposed control scheme of the HVAC unit. This shows the potential of ancillary services capacity through commercial buildings can be maximized with flexible loads and thereby help in power grid support.

Prediction of train interior noise due to the HVAC system

In recent years, the significance of acoustic comfort in rail vehicles has grown considerably, owing to its direct impact on passenger well-being. As a consequence, interior noise levels are progressively becoming more restrictive. Meeting these requirements is a key challenge for rolling stock manufacturers. The predominant source in stationary condition is the HVAC (Heating, Ventilation, and Air Conditioning) system. To effectively assess and potentially reduce the noise generated by the HVAC system, the availability of tools and methodologies for predicting the propagation of noise through the air ducts and interior noise levels in the passenger compartment during the design phase is crucial. This paper presents a simulation methodology for predicting interior noise levels by evaluating various alternatives within a commercial software. Input data for the simulations encompass the geometry and materials of the supply and return ducts, along with the acoustic power of each outlet measured at the source. This data is associated with the nominal operating mode of the HVAC system. Finally, the paper establishes a correlation between the simulation results and in-situ measurements conducted on the completed train, with the HVAC equipment operating under nominal conditions.

Improving solar HVAC performance by incorporating sutterby SiO2–Cu/C3H8O2 hybrid nanofluid flow via a collocation based Gegenbauer wavelets technique: Effects of electromagnetic control and Smoluchowksi's temperature slippage

AbstractThe utilization of solar energy in heating, ventilation, and air conditioning (HVAC) systems has gained significant attention as a sustainable and environmentally friendly solution to meet the increasing energy demands. Therefore, this research work introduces a novel engineering study that explores solar‐HVAC systems. However, the study utilized hybrid nanofluids (HNFs) consisting of silicon dioxide and copper nanoparticles, with propylene glycol serving as the base fluid. Furthermore, this exploration features a magnetohydrodynamics (MHD) driven rotating flow with activation energy to enhance the heat transfer performance of solar‐HVAC systems. To increase the model novelty, 3D mathematical models were developed to characterize boundary conditions, which include the speed slippery and Smoluchowski temperature slippage. The partial differential equations in the model are transformed into ordinary differential equations (ODEs) through the use of similarity transformations. The Wavelets and Gegenbauer wavelets method in Mathematica was utilized to solve ODEs and investigate physical attributes such as plate friction, Nusselt number, Sherwood number, and mass flux. The findings show that the solar thermal radiation and magnetic field improve the thermal transfer efficiency of solar‐HVAC. Therefore, the findings of the research have practical applications in solar energy for HVAC systems and can contribute to technological improvements in the manufacturing industry.

U-Net architecture for high-resolution thermal image segmentation: Enhancing CFD models in smart HVAC design

This paper presents an enhanced U-Net architecture for accurately segmenting high-resolution thermal images to facilitate the design of intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems using Computational Fluid Dynamics (CFD) models. Indoor thermal comfort for occupants will improve under the same energy load by imposing comfort demands. Segmented data is fed into CFD simulations using a U-Net variant for thermal imagery. Integrating the segmented thermal data into the CFD simulations increases the model’s accuracy by 15 % for segmentation and 20% for prediction when compared to a traditional U-Net architecture. Experimental findings demonstrate the potential to achieve energy savings of up to 25 % in the simulated scenario by using the trained and validated model on a dataset of 1,500 high-resolution thermal images of various building styles. Compared to existing methods, the proposed method saves 30% in total simulation time for complex building models. Deep learning enables building the next generation of HVAC systems by pushing the boundaries of energy-efficient and environmentally friendly buildings. This study shows that advanced computer vision can be leveraged to create more innovative and sustainable built environments, leading to drastic improvements in energy efficiency.

User-centric approach to optimizing thermal comfort in university classrooms: Utilizing computer vision and Q-XGBoost reinforcement learning

The ’one-size-fits-all’ setting of heating, ventilation, and air conditioning (HVAC) systems in university classrooms not only compromises occupant comfort but also leads to potential energy wastage. This study proposes a user-centered approach to optimize the thermal environment in university classrooms by integrating computer vision and Q-XGBoost learning. Computer vision facilitates non-intrusive and real-time sensing of thermal comfort in dynamic university classroom environments. Simultaneously, the Q-XGBoost model identifies optimal operations for HVAC systems by learning from the interactions between occupants and their environment, ensuring a balance between comfort and energy efficiency. These innovative methodologies are harmoniously integrated into a smart air conditioning control box (SACC-Box), achieving intelligent operation of HVAC systems in the real world. Subsequently, a controlled study was conducted to test the user-centered approach’s performance in improving thermal comfort and energy efficiency. The results reveal that classrooms equipped with the SACC-Box significantly improve occupant satisfaction with thermal comfort by approximately 13.5 % while concurrently achieving up to a 5 % reduction in energy consumption compared to classrooms operating with traditional built-in smart control systems. This investigation offers a viable solution to balance energy efficiency and user comfort in university classrooms, underlining the transformative potential of integrating intelligent technologies that inspire a greener, more adaptable, and user-centric futures in higher education establishments.

Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

Investigating the influence of uncertainty on office building energy simulation through occupant-centric control and thermal comfort integration

Buildings with occupant-centric control (OCC) systems can adjust heating, ventilation, and air conditioning (HVAC) operations based on occupant status to minimize unnecessary energy use. However, because occupant behavior is stochastic rather than constant, and preferred setpoints can vary across occupant groups, this randomness significantly impacts energy use in buildings with OCC systems.This study used a stochastic simulation approach to model the uncertainty inherent in occupancy patterns and evaluate its impact on the energy consumption of an office building with OCC. In this case, the setpoint temperature was determined based on the Predicted Mean Vote (PMV) method so that each zone could be controlled to the preferred temperature according to the occupant group.The results showed that the uncertainty of the annualized sum of electricity consumption for heating and cooling was 4.3 % and 1.8 %, respectively, with 95 % confidence intervals. Notably, a negative correlation was observed between the number of occupants and the uncertainty of energy consumption. Thus, as the uncertainty in the setpoint temperature increased during periods of low occupancy, the uncertainty in the energy consumption of the HVAC system also tended to increase. These insights underscore the significant influence of occupancy-related factors on building energy utilization.

The role of passive, active, and operational parameters in the relationship between urban heat island effect (UHI) and building energy consumption

The energy performance of buildings located in urban areas is strongly influenced by the UHI phenomenon, which usually leads to higher cooling energy consumption and lower heating energy consumption. Despite the known effects of UHI on building energy consumption, there is a lack of detailed knowledge on the role of building design and operational parameters in determining the effects of UHI on building energy use. To address this knowledge gap, this study analyzes the impacts of UHI on annual, heating, and cooling energy consumption of 16 prototype buildings located in 16 climate zones in the United States. The objective is to determine the relative relevance of building types (e.g., residential, commercial, healthcare facilities etc.), occupancy parameters (e.g., occupancy schedule) and the Heating, Ventilation and Air Conditioning (HVAC) systems in governing the impact of UHI on the building energy use matrices. Additionally, the thermal properties of the building envelope were evaluated to determine their relative importance in mitigating the effects of UHI on building energy performance. We found that, among the studies building types, while the cooling energy use of restaurant and outpatient healthcare buildings was most affected by UHI (higher cooling energy demands), the outpatient healthcare buildings were most impacted by UHI in terms of their heating energy use (lower heating energy use). The selection of HVAC systems type was found to have negligible influence on the energy impacts of UHI. Lastly, with regard to the thermal properties of the building envelope, window insulation was noted to be the most influential thermal property, followed by roof and wall insulation. Also, the role of insulation as well as other thermal properties was higher in the context of UHI’s impact on heating energy consumption as compared to cooling energy consumption. Not only are the results of this study useful for urban planning purposes to determine the vulnerability of different buildings to UHI, but they also are beneficial to UHI-resilient building designs.