Energy and cost assessment of packaged ice energy storage implementations using OpenStudio Measures
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Heine, Karl (ORCID:0000000320776634)↗
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Energy Analysis of Combi Heat Pump System Configurations for Space Conditioning and Domestic Hot Water Heating in Residential Buildings
Combi heat pump systems, also referred to multifunctional variable refrigerant flow heat recovery (MF-VRFHR) systems, are specifically designed for residential applications to manage both space conditioning and domestic hot water (DHW). They have attracted attention due to their potential for energy conservation through heat recovery. The incorporation of a hot water tank introduces various system configurations, each characterized by distinct pros and cons related to energy efficiency, system stability, and maintenance. Despite this, a critical gap exists as the specific energy performance remains unquantified under diverse operational modes (e.g., heating mode and heat recovery mode). This paper aims to bridge this gap by conducting a comprehensive comparative analysis of two prevalent system configurations while considering feasible proposed control logics. Configuration 1 integrates a separate hot water tank and a refrigerant-to-water heat exchanger (HEX), also known as a Hydro Kit while Configuration 2 incorporates a refrigerant-wrapped hot water tank. To facilitate this analysis, we developed high-fidelity system models for both configurations in Modelica, capturing system dynamics and detailed control sequences effectively. These system models were built upon the TIL library for HVAC equipment components and the Buildings library for residential building thermal load calculations. The validation of the simulation testbed utilized data from experiments conducted in the PNNL lab home for Configuration 1. To establish the simulation testbed for Configuration 2, we extended the modeling setup derived from Configuration 1. This extension specifically involved substituting the separate hot water tank and Hydro Kit with a refrigerant-wrapped hot water tank of similar sizing sourced from an actual product. The simulation analysis of heating-only and heat recovery modes reveals that Configuration 2 not only saves energy and maintains warmer tank temperatures but also demonstrates faster water heating capabilities. This is attributed to decreased energy loss and improved heat transfer. The study encompasses a wide range of scenarios, considering diverse thermal loads and water usage patterns across heating and heat recovery modes. Overall, the comprehensive results indicate that Configuration 2 achieves energy savings ranging from 3.5% to 12.2% compared to Configuration 1, depending on factors such as water usage patterns, thermal loads, and operational modes.
Configuration, Comparison, Multi-functional, Resid↗
Parametric Study of Panel PCM–Air Heat Exchanger Designs
Heat exchangers, devices for the transfer of heat between two or more working fluids, are extensively used in cooling applications and heating applications. Heat exchangers in buildings are typically components of space-conditioning systems, as well as of water-heating applications. Heat exchangers are also sometimes used in applications that require storage and release of energy at specific times. Phase change materials (PCMs) enhance these heat-exchange processes, given their ability to melt and solidify at a fixed range of temperatures, absorbing or releasing significant amounts of latent heat. Five different configurations of PCM–air heat exchangers for thermal control in buildings are analyzed in this work. The heat exchangers were fitted with PCM encapsulated in plastic and composite pouches of various shapes, and packaged in stackable panel layers. Three-dimensional computational fluid dynamics (CFD) modeling of coupled incompressible fluid and conjugate heat transfer were performed on the designs. The phase change process was numerically modelled using the apparent heat capacity method. Steady-state CFD simulations provided quantification of pressure drop as a function of air flow velocity. Transient simulation results describe the thermal evolution of PCM in the pouches, helping to determine the best performing configuration with respect to total thermal charging time.
10 SYNTHETIC FUELS↗
Hex-C 558 : A new porous metallic carbon allotrope for lithium-ion battery anode
Due to the limited specific capacity of graphite anodes in lithium-ion batteries (LIBs), it is imperative to find alternatives with better performance. Here in this work, we propose, for the first time, a three dimensional (3D) porous metallic 5-5-8 carbon monolith with hexagonal lattice as a potential candidate. With 5-5-8 carbon nanoribbon as the building block, this material, named Hex-C558, goes beyond graphene-nanoribbon-based 3D porous carbon structures. Using first-principles calculations, we show that Hex-C 558 is not only dynamically and thermally stable, but also is energetically more favorable than many other theoretically predicted carbon allotropes. More importantly, Hex-C 558 is metallic with ordered ionic conducting channels and possesses a low mass density of 1.05 gcm -3 , exhibiting great potential for ion-battery applications. As an anode for LIBs, Hex-C558 possesses a large specific capacity of 591 mAhg -1 , low diffusion energy barrier of 0.27 eV (at low Li concentration) and 0.52 eV (at high Li concentration), a low open-circuit voltage of 0.51 V, and a small volume change of 2.4%. This work provides a new route for the design and synthesis of novel carbon materials for battery applications by using pentagon-based building units.
36 MATERIALS SCIENCE↗
Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building
With the recent demand for decarbonization and energy efficiency, advanced HVAC control using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible structures, DRL has been successful in energy reduction for many HVAC systems. However, only a few researches applied DRL agents to manage the entire central HVAC system and control multiple components in both the water loop and the air loop, owing to its complex system structures. Moreover, those researches have not extended their applications by incorporating the indoor air quality, especially both CO2 and PM2.5concentrations, on top of energy saving and thermal comfort, as achieving those objectives simultaneously can cause multiple control conflicts. What's more, DRL agents are usually trained on the simulation environment before deployment, so another challenge is to develop an accurate but relatively simple simulator. Therefore, we propose a DRL algorithm for a central HVAC system to co-optimize energy consumption, thermal comfort, indoor CO2 level, and indoor PM2.5 level in an office building. To train the controller, we also developed a hybrid simulator that decoupled the complex system into multiple simulation models, which are calibrated separately using laboratory test data. The hybrid simulator combined the dynamics of the HVAC system, the building envelope, as well as moisture, CO2, and particulate matter transfer. Three control algorithms (rule-based, MPC, and DRL) are developed, and their performances are evaluated on the hybrid simulator environment with a realistic scenario (i.e., with stochastic noises). The test results showed that, the DRL controller can save 21.4 % of energy compared to a rule-based controller, and has improved thermal comfort, reduced indoor CO2 concentration. The MPC controller showed an 18.6 % energy saving compared to the DRL controller, mainly due to savings from comfort and indoor air quality boundary violations caused by unmeasured disturbances, and it also highlights computational challenges in real-time control due to non-linear optimization. Finally, we provide the practical considerations for designing and implementing the DRL and MPC controllers based on their respective pros and cons.
Guo, Fangzhou↗
Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning
Polyimides have been widely used in modern industries because of their excellent mechanical and thermal properties, e.g., high-temperature fuel cells, displays, and aerospace composites. However, it usually takes decades of experimental efforts to develop a successful product. Aiming to expedite the discovery of high-performance polyimides, we utilize computational methods of machine learning (ML) and molecular dynamics (MD) simulations. Our study provides compelling evidence for the effectiveness of a data-driven approach in discovering novel polyimides. We first build a comprehensive library of more than 8 million hypothetical polyimides based on the polycondensation of existing dianhydride and diamine/diisocyanate molecules. Then we establish multiple ML models for the thermal and mechanical properties of polyimides based on their experimentally reported values, including glass transition temperature, Young’s modulus, and tensile yield strength. The obtained ML models demonstrate excellent predictive performance in identifying the key chemical substructures influencing the thermal and mechanical properties of polyimides. The use of explainable machine learning describes the effect of chemical substructures on individual properties, from which human experts can understand the cause of the ML model decision. Applying the well-trained ML models, we obtain property predictions of the 8 million hypothetical polyimides. Then, we screen the whole hypothetical dataset and identify three (3) best-performing novel polyimides that have better-combined properties than existing ones through Pareto frontier analysis. For an easy query of the discovered high-performing polyimides, we also create an online platform https://polyimide-explorer.herokuapp.com/ that embeds the developed ML model with interactive visualization. Furthermore, we validate the ML predictions through all-atom MD simulations and examine their synthesizability. The MD simulations are in good agreement with the ML predictions and the three novel polyimides are predicted to be easy to synthesize via Schuffenhauer’s synthetic accessibility score. Following the proposed ML guidance, we successfully synthesized a novel polyimide and the experimentally obtained high glass transition/thermal decomposition temperature demonstrated its excellent thermal stability. Here our study demonstrates an efficient way to expedite the discovery of novel polymers using ML prediction and MD validation. The high-throughput screening of a large computational dataset can serve as a general approach for new material discovery in other polymeric material exploration problems, such as organic photovoltaics, polymer membranes, and dielectrics.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Simulation Framework for Analyzing the Impact of Stochastic Occupant Behaviors on Demand Flexibility in Typical Commercial Buildings
As one of the primary users of the electric grid, buildings and building equipment, including heating, ventilation, and air conditioning (HVAC) systems, can be leveraged to provide the flexible demand needed to balance the grid. Typical strategies to achieve demand flexibility are to reduce electricity use during peak or critical periods by shutting down equipment or relaxing system setpoints, which will inevitably impact the occupants’ comfort. When occupants feel uncomfortable, they may take actions to regain their comfort, and some of those actions (such as turning on a personal fan) may have a negative impact on meeting the demand response goal. Therefore, it is important to incorporate occupant behaviors into the assessment ofthe building demand flexibility potential. In this study, a simulation framework that includes simulation of zone thermal loads, an HVAC system, and occupant behaviors, was developed to investigate the impact of occupant behaviors on demand flexibility. A case study was conducted using a small office model from the U.S. Department of Energy (DOE) Commercial Prototype Building Models to simulate the building envelope and zone loads. An agent-based occupant thermal behavior model was adapted to forecast occupants’ thermal comfort and their resulting thermal behaviors. An artificial neural network (ANN) based airflow model trained from a computational fluid dynamics (CFD) model of the zone was adopted to better predict the ambient environment of each occupant. An air-source heat pump simulation model that was calibrated from a real two-stage air-source heat pump system was used as the HVAC system. A typical load shedding event during peak hours was studied. Repeated simulations were conducted to capture the stochastic effects of occupant behaviors. The interplay between the demand flexibility, occupant comfort and behavior were analyzed by evaluating key performance indicators, including the energy use, occupant discomfort duration, and occupant behavior duration during the peak period. The results suggest that this framework can be used to analyze typical commercial buildings and their HVAC systems in terms ofdemand flexibility potential under the impact of occupant behaviors.
Chen, Zhelun↗
Assembling Si 2 BN nanoribbons into a 3D porous structure as a universal anode material for both Li- and Na-ion batteries with high performance
The development of anode materials is critical to the success of sodium ion batteries (SIBs). Because of the size difference between Li and Na, the commercial anode material graphite in Li-ion batteries does not work for Na-ion batteries. Thus, it will be ideal if some universal anode materials could work for both Li- and Na-ion batteries with high performance. Inspired by a recent study on the high performance of a 2D-Si 2 BN sheet as an anode material for Li-ion batteries, we design a three dimensional (3D) porous structure by using the nanoribbons of a Si 2 BN sheet as building blocks. Based on the state-of-the-art ab initio calculations, we find that the resulting 3D porous Si 2 BN structure is stable chemically, dynamically and thermally, exhibiting a high specific capacity of 512.42 (341.61 mA h g -1 ), a low voltage of 0.27 V (0.15 V), a small volume expansion of 2.5% (2.7%), and a low migration energy barrier of 0.44 eV (0.19 eV) for Li- (Na-) ion batteries. Finally, these intriguing features, together with the light mass and rich abundance of Si, B and N, suggest that the 3D porous Si2BN structure is a promising candidate for the anode material of both Li- and Na-ion batteries.
36 MATERIALS SCIENCE↗
Thermal and concentration effects on 1 H NMR relaxation of Gd 3+ -aqua using MD simulations and measurements
We report gadolinium-based contrast agents are key in clinical MRI for enhancing the longitudinal NMR relativity (r 1 ) of hydrogen nuclei ( 1 H) in water and improving the contrast among different tissues. The importance of MRI in clinical practice cannot be gainsaid, yet the interpretation of MRI relies on models with severe assumptions, reflecting a poor understanding of the molecular-scale relaxation processes. In a step towards building a clearer understanding of the relaxation processes, here we investigate thermal and concentration effects on r 1 of the Gd 3+ -aqua complex using both semi-classical molecular dynamics (MD) simulations and measurements. We follow the MD simulation approach recently introduced by [Singer et al., Phys. Chem. Chem. Phys., 2021, 23, 20974], in which no NMR relaxation model or free-parameter is assumed to predict r 1 , thereby bringing new insights into the physics of r 1 on a molecular scale. We expand the autocorrelation function G(t) in terms of molecular modes and determine the thermal activation energies of the two largest modes, both of which are consistent with the range of literature values for rotational diffusion. We also determine the activation energies for translational diffusion and low-field electron-spin relaxation, both of which are consistent with the literature. Furthermore, we validate the MD simulations at human body temperature and concentrations of the paramagnetic ion used in clinical MRI, and we quantify the uncertainties in both simulations and measurements.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Spokane Eco-District Campus Performance Under Alternative Electricity Rates: Benefits for virtual power plant participants and suppliers
Here, the respective benefits for virtual power plant participants and suppliers are revealed and compared under alternative electricity rate structures, including conventional large commercial electricity rates, large commercial electricity rates with special rates for demand-side generation, and dynamic hourly transactive prices. These three scenarios were explored using the capabilities of the Eco-District campus, a virtual power plant in Spokane, Washington, that is supplied electricity by Avista Utilities. The Eco-District Campus was modeled to host solar power generation, battery energy storage, and thermal energy resources that must be coordinated with building heating and cooling needs. First, the electricity supplier’s costs for energy, infrastructure, and energy losses were modeled. Then, the virtual power plant’s performance was modeled while presuming that its manager would minimize its costs under its electricity rate structure. The demand charges of conventional commercial electricity rates managed monthly peak, as would be expected, but hourly dynamic transactive pricing resulted in a striking alignment between the costs incurred by the supplier and the virtual power plant’s energy costs.
Electricity rates↗
Dynamic Earth Energy Storage: Terawatt-year, Grid-scale Energy Storage Using Planet Earth as a Thermal Battery (GeoTES): Phase I Project (Final Report)
Grid-scale energy storage has been identified by the U.S. Department of Energy’s (DOE) Energy Storage Grand Challenge as a necessary technology to support the continued build-out of intermittent renewable energy resources required to attain a carbon-free energy future. To meet this goal, the 2018 Department of Energy Research and Innovation Act mandated the creation of a comprehensive program to accelerate the development and commercialization of next-generation energy storage technologies. One of numerous energy storage technology options is the storage of excess energy as heated geothermal brine in suitable geologic formations. This concept, known as reservoir thermal energy storage (RTES), geologic thermal energy storage (GeoTES), aquifer thermal energy storage (ATES), etc., relies on the storage of thermal energy in geologic formations for recovery and use in large-scale direct use geothermal (e.g., district heating, industrial processes, etc.) and electrical power generation applications. This thermal energy is derived from excess or waste heat from any high-temperature heat source, such as concentrated solar or from conventional thermal/nuclear generation. As such, RTES can potentially play a significant role in meeting the energy storage shortfall in the coming decades. RTES can provide energy arbitrage through both the storage and production of thermal energy stored in geologic formations for direct use applications and can serve as a source of hot fluids that can be used to generate electricity to support peak demand ramping, thus easing stress on transmission and distribution. This energy storage option has geographic benefits in that energy can be stored locally or regionally depending on the various needs/loads. RTES can also be located across an enormous geographic area, without the need for a traditional hydrothermal resource but where thermal gradients and hydrogeology allow economic exploitation of subsurface heat. The work conducted for this project includes (1) a review of lessons learned from past high-temperature RTES international projects; (2) geochemical experimental investigation and numerical simulations of potential domestic sedimentary reservoirs and (3) development of a thermo-hydrological-mechanical (THM) numerical simulation tool for optimizing formation properties and design parameters to maximize thermal energy storage performance.
15 GEOTHERMAL ENERGY↗
Energy performance analysis of smart wall system with switchable insulation and thermal storage capacity
The ever-increasing global energy demand and the issues caused by population growth and unsustainable energy resource usages have several environmental and economic impacts. The on-demand capability of dynamic wall systems with switchable insulation systems can contribute toward energy efficiency and reduced electric cost using "building-as-a-battery." In this paper, the performance of an exterior envelope system that employs a switchable insulation system is investigated. A COMSOL simulation was used to study the envelope performance under three switchable insulation locations in the wall system. The on and off switching cycle included insulating or conducting the exterior, interior, or both sides of the mass wall system. To validate the simulation work, an experimental test was conducted in climate chamber on a 4 × 8 in. wall system with identical wall components used in the COMSOL simulation. Results show a good agreement between the experiment and simulation results. The wall system with switchable insulation placed on the exterior side provided the highest interior inward heat flux when compared with the interior or split insulation. Also, an exterior switchable insulation system in addition to a thermal mass maintained a positive heat flow for more hours when the outdoor temperature was lower than the interior temperature.
Iffa, Emishaw↗
Modeling and Validation of a Residential Multi-Functional Variable Refrigerant Flow Heat Pump System with Heat Recovery
To bridge the existing gap in modeling the variable refrigerant flow heat pump systems with heat recovery (VRFHR), we developed a suite of dynamic VRFHR system models in Modelica. These models are specifically tailored for residential multi-functional VRFHR (MF-VRFHR) applications, including space conditioning and domestic hot water (DHW) heating, utilizing both the TIL library for HVAC equipment and the Buildings library for thermal load calculations. The development comprises essential component models, including the newly developed heat recovery unit (HRU), along with system models that integrate the heat pump system and building envelope. These system models accommodate various operational modes such as heating-only, cooling-only, and heating-recovery (including heating-dominant and cooling-dominant) modes. Furthermore, we propose an efficient optimization-based model calibration method that identifies critical model parameters while utilizing a small amount of data obtained from either real systems or manufacturer's specifications. We demonstrate the effectiveness of these models and the proposed calibration method for a MF-VRFHR system installed in Richland, WA. The developed models are calibrated and validated using data collected under different operational modes during both heating and cooling seasons. The results show that the models capture the system dynamics and achieve high accuracy, with the coefficient of the variation of the root-mean-square-error less than 15% for variables such as outdoor unit power consumption, compressor speed, space temperature and DHW temperature. The validated models serve as a reliable representation of the MF-VRFHR system, facilitating the development and validation of optimized controls needed to realize the full benefits of integrated heat pump systems. Future research will utilize these models to develop advanced controls and optimize system performance for improved energy efficiency and demand flexibility.
Modeling, Variable refrigerant flow (VRF) systems,↗
Spatially resolved phase transformation mapping in 410 stainless steel during additive manufacturing visualized via real-time infrared data
Here, in this study, we demonstrate that spatially resolved cooling curves derived from real-time infrared (IR) thermography during additive manufacturing (AM) can capture spatial variations in phase transformation temperatures through cooling curve analysis (CCA). Using this approach, we show that during laser hot-wire deposition of 410 stainless steel (410SS), the martensite start temperature (M s ) evolves dynamically throughout the build. The M s temperature is spatially nonuniform, ranging from 185 °C to 348 °C, with the lowest values toward the build center and higher values toward the upper region of the deposit. In the lower portion of the build, no M s inflection is detected via CCA, consistent with transformation occurring earlier during thermal cycling followed by tempering during subsequent thermal cycles. These trends in M s are corroborated by characterizing the microstructure by electron backscatter diffraction (EBSD). Traditionally, M s is assumed to be constant, and a single interpass temperature is applied during both deposition and residual stress modeling. Our results demonstrate that IR-derived cooling curves provide a route to spatially and temporally resolved transformation temperature tracking for dynamic interpass control and improved residual-stress modeling.
Additive manufacturing↗
An Open-Source Virtual Testbed for a Real Net-Zero Energy Community
Net zero energy communities (NZECs) are critical to ensure sustainability and resilience of modernized power systems. System modeling helps overcome technical challenges in designing and operating NZECs. In this paper, we present the modeling work based on a real NZEC. Two sets of models are developed: higher-fidelity physics-based models considering the interaction between subsystems of the studied NZEC and capturing fast-dynamics; and lower-fidelity data-driven models requiring less resource to establish and/or run. All models are validated against measurements from this real NZEC. In addition, we create a simulation framework which streamlines the processes for simulation and thus allows using developed models to form a virtual testbed. To demonstrate the usage of the virtual testbed, a case study is conducted where a building-to-grid integration control is evaluated via simulation. The evaluation results suggest that the tested control significantly smooths the power draw of the studied community and doesn’t sacrifice the thermal comfort to a great extent.
Huang, Sen↗
A bioinspired approach for adaptive solid-solid phase change material coatings with optimized surface features for passive thermal regulation
The necessity to reduce global energy consumption calls for innovative strategies in building thermal management. Passive thermal regulation, particularly through bio-inspired designs, offers a promising avenue by mimicking nature's efficient control of optical properties. This research introduces a novel, climate-responsive coating that integrates optimized bio-inspired surface features with a solid-solid phase change material (SS-PCM) to dynamically manage solar absorptivity without adding additional thickness, enabling both heating and cooling as needed. Drawing on the photonic architectures of the Saharan silver ant and Morpho Didius butterfly, we employed a modeling and multi-objective optimization framework to tailor these surface features. Simulations reveal that surface texture, rather than the intrinsic phase transition of the SS PCM, dominates optical control. Relative to a flat SS PCM coating, optimized isotropic random roughness and broader range features yielded the highest passive heating power increase of about 144 % and 319 % respectively suitable for cold climates. Saharan ant-inspired features enhanced passive cooling for hot climates, achieving a 21.8 % improvement. For moderate climates, Butterfly-wing-inspired surface features provided a balanced enhancement of 19 % for heating and 7 % for cooling. Across all cases, the optimized surface features reduced combined heating and cooling energy demand more effectively than the baseline coating, while preserving material thickness. These findings demonstrate that climate-adaptive, optimized bio-inspired surface features can unlock the full potential of SS PCM coatings, providing a versatile pathway to significant energy savings in buildings and other applications. The methodology establishes a framework for designing next-generation adaptive envelopes that leverage natural photonic principles for high-impact, low-cost thermal regulation.
36 MATERIALS SCIENCE↗
Numerical Modeling and Parametric Study of a Dual Purpose Underground Thermal Battery
Buildings’ thermal energy systems for providing space heating/cooling consume a considerable amount of electricity nationwide. Integrating thermal energy storage with buildings’ thermal systems has the potential to shave the peak electric demand and to overcome the mismatch between the intermittent renewable power and the fluctuating daily demand for electricity. A novel dual-purpose underground thermal battery (DPUTB) has been developed to provide diurnal thermal energy storage in conjunction with a dual-source heat pump (DSHP)system. A two-dimensional numerical model is developed to simulate the transient performance of the DPUTB. This model has similar accuracy as a detailed three-dimensional model developed with commercial heat transfer and fluid dynamics simulation program but with 1000 times faster computational speed. A parametric study is performed to identify a design of the DPUTB that can provide 14 kWh thermal storage capacity for each charging and discharging cycle. A system simulation has been developed to assess the performance of the designed DPUTB when it is integrated with a DSHP for conditioning a typical residential building in summer and winter. Simulation results indicate that the DPUTB can meet all the design requirements in both summer and winter with the simple rule-based control strategy and the overall integrated DPUTB and DSHP system can shift the electric load for meeting the thermal demand from the peak hours to off-peak hours during a day, which would result in considerable energy cost savings if a Time of Use electricity tariff is applicable.
25 ENERGY STORAGE↗
Mitigating urban heat island and enhancing indoor thermal comfort using terrace garden
The United Nations advocates for sustainable urban planning and design, emphasizing green infrastructure initiatives to mitigate urban heat island effects and enhance the resilience and livability of cities globally. To address urban heat challenges, a study was conducted in Chennai, India, from April to June 2023. The study focused on assessing temperature dynamics on a building's terrace by comparing a well-maintained garden area with an exposed region. Temperature and humidity sensors were deployed in both the garden and exposed areas of the terrace, as well as within rooms beneath it, to monitor hourly temperature fluctuations. The findings indicate a significant reduction in internal room temperatures in areas with rooftop gardens, ranging from 4 to 11 °C, depending on the time of year and sun's position, compared to rooms with fully exposed roof configurations. Additionally, simulation studies were performed to validate these findings, suggesting that optimizing the distribution of soil beds and plant density across the roof could yield an additional temperature reduction of 3–4 °C, resulting in an overall difference of up to 14–15 °C. The study highlights the efficacy of rooftop gardens in providing cooling effects during daylight hours and maintaining temperature parity post-sunset. Through analysis of sensor data, the research elucidates the intricate relationship between green infrastructure and thermal comfort, offering insights for energy-efficient building design and resilient urban planning. The findings underscore the potential of rooftop gardens in fostering a more comfortable, energy-efficient, and sustainable urban living environment.
54 ENVIRONMENTAL SCIENCES↗