Search Results (86)

As the power density of office computing clusters rises to 200–250 W per chip, the substantial heat generated during operation not only impairs chip performance and shortens lifespan but also compels heating, ventilation, and air conditioning (HVAC) systems to operate at high loads. This increases energy consumption by 30–40% and causes indoor temperature fluctuations that reduce office workers’ comfort. Targeting centralized thermal management for such clusters, this study proposes a hybrid cooling strategy integrating outdoor natural cold air (as a continuous heat sink) with phase change materials (PCMs, for transient heat peak absorption). Six adjustable heating plates (power range: 50–250 W per unit, simulating 7 nm office chips) mimicked heat dissipation in a six-chip cluster. Latent heat storage (LHS) units served as passive cooling, with fan coils as auxiliary for natural/forced convection. By using PCMs (melting point: 48 °C) to absorb transient peaks and coils to utilize outdoor cold air, the system maintained circulating water at approximately 60 °C (steady-state equilibrium temperature under full-load conditions) and kept chip temperatures below 80 °C (industrial safety threshold). The hybrid system reduced combined pump and fan power to 125 W, achieving 75% energy savings compared to the HVAC system (500 W) and 40% savings compared to using only natural cold air (210 W pump and fan power). Positive pressure in the outdoor unit (increasing coil air velocity by 1.2 m/s relative to natural convection) further improved heat dissipation efficiency by 15%. Finally, this study quantifies the influence of PCM thermal conductivity and filling mass on the system’s temperature control performance through numerical simulations, providing direct evidence for parameter design of LHS units. Full article

Artificial intelligence (AI) is rapidly redefining the management of urban energy systems by coupling predictive analytics with closed-loop control across buildings, power grids, and mobility networks, positioning cities as critical leverage points in global decarbonization efforts. Contemporary urban environments generate vast, heterogeneous datasets that enable advanced machine learning applications; however, limitations remain, including interpretability–fairness trade-offs, fragmented data governance, interoperability gaps, cybersecurity risks, and insufficient long-term validation across diverse climatic and socio-economic contexts. This review evaluates AI-driven strategies for energy-efficient urban systems and identifies the technical and governance conditions required for scalable impact. The evidence synthesized indicates that supervised and ensemble learning models achieve high predictive accuracy for electricity demand and chiller performance, with models such as Random Forest Regression achieving R² values up to 0.9835 in electricity consumption forecasting, while unsupervised approaches detect latent inefficiencies in HVAC systems, delivering measurable savings typically around 6% under controlled benchmarking conditions. Deep learning architectures improve multi-building forecasting and real-time control, with hybrid CNN–LSTM models achieving prediction accuracies up to 97% and outperforming traditional statistical approaches in weekly energy demand forecasting achieving higher prediction accuracy and significant energy savings in complex urban subsystems with reported reductions of approximately 21–23% in residential and educational buildings and up to 37% in office HVAC systems. Hybrid and physics-informed AI models embed thermodynamic principles into data-driven frameworks, improving robustness, interpretability, and generalization. IoT sensor networks and edge-computing architectures support adaptive HVAC, demand–response, and smart-grid management, while integrated building–grid–mobility systems enhance load balancing, storage use, and carbon reduction. AI-enhanced strategies offer a credible pathway toward measurable reductions in urban energy use and emissions with deep reinforcement learning in digital twin environments reducing HVAC energy demand by 10–35% while maintaining thermal comfort within ±0.5 °C in line with ASHRAE standards, and overall energy savings reaching up to 44% in optimized systems when supported by interoperable infrastructures, secure digital-twin architectures, and standardized measurement and verification protocols. Full article

This study develops an advanced optimization framework for heating, ventilation, and air conditioning (HVAC) systems in multi-zone buildings with highly dynamic and uncertain internal heat loads. Unlike conventional models that assume static occupancy, the proposed approach captures time-varying, spatially heterogeneous thermal disturbances driven by occupant activity. The building is modeled as a coupled cyber-physical system integrating multi-zone thermal dynamics, nonlinear HVAC energy consumption, and behavior-driven heat generation. To address uncertainty, a distributionally robust optimization framework based on Wasserstein ambiguity sets is employed, enabling reliable performance without requiring precise probability distributions. In addition, a physics-informed reinforcement learning mechanism is incorporated to derive adaptive control policies while ensuring thermodynamic feasibility. A multi-zone coordination strategy is further introduced to manage spatial thermal interactions and maintain stable comfort across different areas. Case study results demonstrate that the proposed method reduces peak energy consumption by 28–32%, decreases comfort violation rates by 65–75%, and improves thermal stability, with temperature variance reduced by over 60% compared to baseline strategies. Full article

Conventional Heating, Ventilation, and Air Conditioning (HVAC) control systems primarily rely on environmental and physiological parameters, largely ignoring the critical influence of psychological states on thermal comfort. Overlooking this factor often leads to suboptimal occupant satisfaction, energy inefficiency and thus carbon dioxide (CO₂) emissions. To this aim, this study introduces a novel mood-adaptive HVAC control system integrating psychological feedback to decrease CO₂ emissions in office buildings by reducing energy consumption and optimizing comfort. A total of 7000 thermal facial measurement records and high-resolution camera images were collected across seven mood state conditions using video stimuli and the Profile of Mood States (POMS) questionnaire to evaluate mood variations. A dual artificial intelligence system was developed: a Convolutional Neural Network (CNN) for analyzing facial expressions and an Artificial Neural Network (ANN) for processing facial temperatures via thermal imaging. These models collectively predict occupant mood in real-time, and a custom-designed wearable necklace interface transmits this data to dynamically adjust HVAC setpoints. To evaluate system performance, energy consumption was directly measured in real-life operations using an energy analyzer, without relying on simulations. Results indicate that this prototype personalized mood-driven system has the potential to enhance perceived thermal comfort while achieving up to a 20% reduction in carbon emissions compared to conventional systems. This human-centered approach significantly advances intelligent building management and climate change mitigation. Full article

This paper presents a multimodal transformer-based framework for the joint prediction of indoor thermal comfort and energy efficiency using real-world building management system (BMS) datasets. Unlike traditional comfort models that rely on fixed physical assumptions and subjective surveys, the proposed approach adopts physics-guided, data-driven learning to capture nonlinear and time-dependent interactions among environmental conditions, HVAC operation, and occupancy-related variables. Thermal comfort labels are computed using the PMV–PPD formulation defined by ASHRAE Standard 55, assuming standard metabolic rate and clothing insulation due to the lack of direct measurements in routine BMS data. A temperature-driven baseline HVAC energy proxy is derived using change-point regression. The proposed transformer architecture fuses multivariate temporal sequences to jointly predict both comfort and energy baseline targets through a dual-head regression formulation. The model is validated on two complementary datasets representing steady-state and dynamically perturbed thermal conditions. The proposed approach consistently outperforms linear regression, random forest, and LSTM baselines, achieving mean absolute errors below 0.03 and $R^2$ values exceeding 0.98 with corresponding RMSE values below 0.035 for both targets. Residual and calibration analyses confirm stable, unbiased prediction behavior across wide temperature ranges. The results highlight the strong potential of attention-based multimodal learning for future comfort-aware building energy optimization and digital twin integration. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems used in modern buildings are among the largest contributors to energy consumption. Therefore, it is necessary to carefully balance between thermal comfort and energy efficiency when operating these systems. This study proposes a Symbolic Discrete Controller Synthesis (SDCS) approach for HVAC management that simultaneously enforces comfort-band constraints at the supervisory level and optimizes energy efficiency. Unlike traditional continuous controllers tuned per zone, the proposed method coordinates zone-level actuation through discrete power levels and node-level constraints (including an aggregate peak cap), exploiting thermal inertia to redistribute service over time without increasing comfort-band violations. Experimental evaluations on a multi-zone building model demonstrate that the SDCS approach provides comparable small comfort violations and provides superior energy savings when benchmarked against Model Predictive Control (MPC) and traditional Proportional-Integral-Derivative (PID) controllers. These results highlight the potential of SDCS as a robust and scalable solution for sustainable building management and energy-aware HVAC coordination in multi-zone buildings. Full article

The increasing frequency of interior renovation and fit-out in office buildings raises concerns about indoor environmental quality, occupant health, and sustainability performance, yet existing certification systems remain largely design-stage or whole-building oriented and provide limited guidance for recurring renovation cycles. This study develops a health-oriented assessment framework for office interior renovation as a structured decision-support tool for practitioners and policymakers. We adopted an integrated approach combining a targeted literature review, expert consultation, the Fuzzy Delphi Method (FDM) for indicator screening, and the Analytic Hierarchy Process (AHP) for hierarchical weighting, based on an expert panel of 20 professionals spanning green building certification, architecture/interior design, MEP engineering, property/facility management, and energy/environmental consulting. Through consensus screening and weighting, four assessment dimensions and eighteen key indicators were identified and prioritized. Environmental quality was ranked highest (39.2%), followed by safety management (23.0%), functional usability (21.1%), and resource efficiency and circularity (16.7%). At the indicator level, indoor air quality management, Heating, Ventilation and Air Conditioning (HVAC) energy efficiency, space-friendly layout, preliminary assessment and planning, and thermal comfort emerged as the top priorities. Overall, the framework bridges the gap between certification-oriented evaluation and the operational realities of office renovation, enabling more consistent integration of health and sustainability considerations across renovation decision-making. Full article

Electric vehicle (EV) performance can vary substantially under real-world operating conditions, particularly due to ambient temperature effects on energy consumption, battery behavior, and thermal management requirements. This study quantifies how weather conditions, daily driving patterns, and State-of-Charge (SOC) usage strategies jointly influence EV driving range, charging frequency, and overall energy efficiency. A detailed and experimentally validated Autonomie vehicle model is developed, integrating a powertrain, a mono-zonal cabin model, and a battery electro-thermal model. Three battery sizes (200-, 300-, and 400-mile homologated ranges) are assessed across five commute profiles (20–200 miles) and six ambient temperatures (−18 °C to 50 °C), including scenarios with and without preconditioning. Results show that extreme temperatures could significantly decrease the maximum achievable range by up to 55% in cold conditions (−18 °C) and 40% in hot conditions (50 °C), relative to moderate conditions. Larger battery packs retain a greater fraction of their nominal range under thermal stress, while smaller packs experience sharper relative penalties due to the higher contribution of thermal loads to total energy demand. The analysis further demonstrates that limiting operation to partial SOC windows (e.g., 80–20%), a common real-world practice, significantly reduces achievable range and increases charging frequency, particularly in cold weather. Thermal preconditioning while plugged in is shown to mitigate these effects for short trips, reducing energy consumption by up to 31% in hot conditions and 7% in cold conditions. The findings demonstrate how climate, SOC usage behavior, and thermal management jointly shape the practical driving capability of EVs, highlighting the importance of efficient thermal management and realistic user charging strategies for ensuring reliable EV operation across diverse climatic scenarios. Full article

The three key design criteria for nearly zero-energy buildings (nZEBs) and climate-neutral buildings are minimizing energy use, ensuring high occupant comfort, and reducing environmental impact. Thermal comfort is one of the main components of indoor environmental quality (IEQ), strongly affecting occupants’ health, well-being, and productivity. As energy-efficiency requirements become more demanding, the appropriate selection of heating systems, their automated control, and the management of solar heat gains are becoming increasingly important. This study investigates the influence of two low-temperature radiant heating systems—underfloor and wall-mounted—and the use of Venetian blinds on perceived thermal comfort in a highly glazed public nZEB building located in a densely built urban area within a temperate climate zone. The assessment was based on the PMV (Predicted Mean Vote) index, commonly used in IEQ research. The results show that both heating systems maintained indoor conditions corresponding to comfort or slight thermal stress under steady state operation. However, during periods of strong solar exposure in the room without blinds, PMV values exceeded 2.0, indicating substantial heat stress. In contrast, external Venetian blinds significantly stabilized the indoor microclimate—reducing PMV peaks by an average of 50.2% and lowering the number of discomfort hours by 94.9%—demonstrating the crucial role of solar protection in highly glazed spaces. No significant whole-body PMV differences were found between underfloor and wall heating. Overall, the findings provide practical insights into the control of thermal conditions in radiant-heated spaces and highlight the importance of solar shading in mitigating heat stress. These results may support the optimization of HVAC design, control, and operation in both residential and non-residential nZEB buildings, contributing to improved occupant comfort and enhanced energy efficiency. Full article

This paper presents the development and validation of an IoT-enabled temperature-sensing device for real-time monitoring of the thermal insulation properties of glass windows. The system integrates contact and non-contact temperature sensors into a compact PCB platform equipped with WiFi connectivity, enabling seamless integration into smart home and building management frameworks. By continuously assessing window insulation performance, the device addresses the challenge of energy loss in buildings, where glazing efficiency often degrades over time. The collected data can be transmitted to cloud-based services or local IoT infrastructures, allowing for advanced analytics, remote access, and adaptive control of heating, ventilation, and air-conditioning (HVAC) systems. Experimental results demonstrate the accuracy and reliability of the proposed system, confirming its potential to contribute to energy conservation and sustainable living practices. Beyond energy efficiency, the device provides a scalable approach to environmental monitoring within the broader future internet ecosystem, supporting the evolution of intelligent, connected, and human-centered living environments. Full article

This paper presents an innovative solution based on the Indirect Regenerative Evaporative Cooling (IREC) concept for high-power density electronics. The technology relies on forced convective cooling by air that is additionally cooled via evaporation. The system comprises dry and wet channels for the cooled and wet air, respectively; water is delivered through porous membranes in the wet channels. The novelty relative to HVAC-type exchangers (based on IREC technology) is a full flow return configuration, in which the entire stream from the dry channels is redirected into the wet channels. The performance benefits become pronounced at high ambient temperatures, where traditional forced convection may be insufficient; inlet air absolute humidity is a key factor governing efficiency. The authors present a developed prototype, a simplified thermal analysis, measurement results, and a discussion of IREC applicability to electronics cooling. The results indicate feasibility and highlight the potential of the proposed design for the energy-efficient thermal management of sensitive electronic equipment. Full article

Buildings account for nearly 40% of global energy consumption, mainly due to the demands of artificial lighting and heating, ventilation, and air-conditioning (HVAC) systems. The integration of semi-transparent photovoltaic (STPV) modules into building envelopes presents a sustainable strategy to lower energy use while simultaneously replacing conventional roofs and façades. However, the performance of STPV systems is strongly influenced by incident solar radiation and building orientation, and elevated surface temperatures can further diminish their efficiency. In this study, the performance of an STPV module was assessed by placing it on a horizontal surface and varying its orientation relative to a 90° reference. To mitigate thermal effects and improve efficiency, a thermal management system incorporating a calcium chloride hexahydrate-based phase change material (PCM) was employed. The PCM was enhanced with nanomaterials—graphene oxide (GO) and aluminum oxide (Al₂O₃)—at weight fractions of 0%, 0.25%, 0.5%, and 1%. The thermophysical properties of the nano-enhanced PCM were analyzed using differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and thermal conductivity measurements. Under incident solar radiation of 941 W/m², the electrical efficiencies of the PV, PV–PCM1, and PV–PCM2 modules were measured at 13.75%, 16.84%, and 15.28%, respectively, demonstrating the potential of nano-enhanced PCM to improve STPV performance. Full article

Auxiliary systems, particularly HVAC and thermal management, significantly influence electric vehicle (EV) range under diverse weather conditions. Accurate prediction of auxiliary power demand remains challenging due to nonlinear temperature dependencies and driving dynamics. Here we develop an integrated physics-based decomposition combined with an XGBoost machine learning model trained on 95,028 real-world measurements from EVs operating across multi-seasonal conditions (−8 °C to +33.5 °C). The model achieves an R² of 0.9986 and a mean absolute error of 35 W, revealing that auxiliary loads contribute variably from 75% while idle to 12% during highway driving, with heating power dominating cooling by a 7:1 ratio and increasing 44-fold at low temperatures. Feature importance analysis identifies accelerator pedal position and heating efficiency per temperature differential as primary predictors, indicating coupling between propulsion and auxiliary loads. These findings underscore the necessity of context-aware auxiliary power prediction to enhance EV energy management and range forecasting, particularly in cold climates where heating demands critically impact efficiency. Full article

To reduce emissions in the existing transportation system and lower carbon dioxide (CO₂) output, battery electric vehicles (BEVs) offer a promising approach due to their higher energy efficiency. However, their driving range still falls short compared to conventional vehicles. Optimizing the heating, ventilation, and air conditioning (HVAC) system can help save energy and improve passenger comfort. This study investigates an advanced thermal management system for an electric truck cabin with heating panels and added insulation. A one-dimensional (1D) cabin thermal model was also developed and validated with experimental data. The model integrates insulation, heating panels, and a 1D comfort simulation. It is functional mock-up unit (FMU) compatible and connects to larger system simulations and real-time applications. The results show that energy consumption can be reduced by up to 50% with these thermal measures. In the future, further research and new approaches will be necessary to identify even more efficient subsystems and cost-effective solutions. Full article

Buildings with electrified heat pump systems, onsite photovoltaic (PV) generation, and energy storage offer strong potential for demand flexibility. This study compares two storage configurations, thermal energy storage (TES) and battery energy storage (BESS), to evaluate their impact on cooling performance and cost savings. A Model Predictive Control (MPC) framework was developed to optimize system operations, aiming to minimize costs while maintaining occupant comfort. Results show that both configurations achieve substantial savings relative to a baseline. The TES system reduces daily operating costs by about 50%, while the BESS nearly eliminates them (over 90% reduction) and cuts grid electricity use by more than 65%. The BESS achieves superior performance because it can serve both the controllable heating, ventilation, and air conditioning (HVAC) system and the home’s broader electrical loads, thereby maximizing PV self-consumption. In contrast, the TES primarily influences the thermal load. These findings highlight that the choice between thermal and electrical storage greatly affects system outcomes. While the BESS provides a more comprehensive solution for whole-home energy management by addressing all electrical demands, further techno-economic evaluation is needed to assess the long-term feasibility and trade-offs of each configuration. Full article