Search Results (548)

This paper investigates the potential of localized air-curtain microclimate control to reduce HVAC energy consumption in electric vehicles while maintaining occupant thermal comfort. The study compares conventional full-cabin cooling with driver-focused and passenger-focused air-curtain configurations under controlled ambient conditions of 32 °C. The experimental framework combines analytical airflow and heat-transfer modeling with comparative HVAC performance evaluation using power consumption, time to reach thermal comfort, and Predicted Mean Vote (PMV) analysis. The results show that the air-curtain configurations reduce HVAC power consumption from 3.2 kW for conventional cooling to 2.3 kW and 2.5 kW for the driver- and passenger-focused configurations, corresponding to energy savings of approximately 22–28%. In addition, localized airflow significantly accelerates thermal comfort attainment, reducing stabilization time from 8 min to 4–5 min while maintaining PMV values within acceptable comfort limits. The findings demonstrate that occupant-centered air-curtain microclimate strategies can improve HVAC energy efficiency, reduce auxiliary energy demand, and support more sustainable and range-efficient operation of next-generation electric vehicles. Full article

Achieving sustainability in the manufacturing sector calls for systemic reductions in energy consumption and carbon emissions without compromising productivity. In the global energy consumption landscape, the manufacturing sector accounts for a significant proportion and is a major source of carbon emissions, with manufacturing systems and HVAC (Heating, Ventilation, and Air Conditioning) systems being the principal energy consumers. Existing research typically optimizes these two systems independently, neglecting their dynamic coupling; production scheduling determines equipment power and heat dissipation, which alters building thermal loads and consequently affects HVAC energy consumption. To address this problem and advance sustainable manufacturing practices, this study proposes an energy-aware scheduling framework integrating manufacturing and HVAC control. A WOA-XGBoost energy consumption prediction model is constructed, employing the Whale Optimization Algorithm to tune XGBoost hyperparameters, achieving a prediction accuracy of R² = 0.937 on the Shanghai typical meteorological year dataset. The HVAC decision variables are defined as five operational control variables—supply air flow rate, fan total pressure, ERV sensible/latent heat recovery effectiveness, and ventilation air flow rate—ensuring the physical realizability of scheduling solutions. An integrated scheduling-and-control model incorporating production capacity constraints and electricity demand response is then formulated and solved using a hybrid Particle Swarm Optimization algorithm. Validation on a five-machine, four-buffer flow shop demonstrates that the proposed framework reduces total electricity cost by 8.85% and total energy consumption by 14.88% in summer compared with a physics-based coupling baseline, with all metrics exhibiting coefficients of variation below 4% across ten independent runs. These results demonstrate that the proposed data-driven framework provides a practical and scalable pathway toward sustainable manufacturing by jointly reducing energy use and associated carbon emissions while maintaining full production throughput. Full article

Water pumping systems play a critical role in various industries, including water supply, cooling, heating, and HVAC systems (Heating, Ventilation, and Air Conditioning systems), by ensuring efficient fluid transfer. In the control of pumps, Proportional–Integral–Derivative (PID) algorithms are widely employed for frequency adjustment in Variable Frequency Drives (VFDs). However, the performance of this conventional controller in nonlinear and time-variant systems, as well as its impact on energy consumption, needs further improvement. To overcome these shortcomings, this paper proposes a Modified Particle Swarm Optimization (MPSO)-based PID controller. The novelty of the proposed approach lies in the integration of a linearly decreasing inertia weight strategy with a composite objective function (Minf), which simultaneously considers multiple performance criteria, including overshoot, rise time, settling time, and the integral of absolute error. The proposed controller is experimentally compared with controllers developed using two different objective functions and conventional PSO. The results indicate that the proposed controller not only exhibits superior performance in terms of time response parameters (such as settling time, overshoot, and steady-state error) but also provides significant advantages in terms of energy savings. Full article

The building sector is a major contributor to global energy consumption and greenhouse gas emissions, and multi-family residential buildings play an important role in urban decarbonization and the transition toward sustainable cities and societies. This study proposes decarbonization-oriented case studies for selecting heating, ventilation, and domestic hot water systems by integrating environmental, economic, and social criteria aligned with the Sustainable Development Goals (SDGs), particularly SDG 7 and SDG 11. This research compares selected conventional and low-carbon building-level heating, ventilation, and domestic hot water systems, including gas boilers and heat pumps integrated with renewable energy and heat recovery. The evaluation is based on a calculation-based energy performance assessment using a quasi-static monthly heat balance approach, economic indicator analysis, and environmental assessment based on primary, final, and useful energy demand and CO₂ emissions. Cooling energy demand was not included in the assessment because the analyzed scenarios were limited to heating, ventilation, and domestic hot water preparation. Furthermore, the social implications are examined, considering energy affordability, long-term operating costs, and the potential to mitigate energy poverty. The results indicate that low-carbon HVAC systems, particularly heat pump systems integrated with renewable energy sources, significantly reduce CO₂ emissions and primary energy consumption compared to conventional solutions. Although they require a higher initial investment, they can achieve lower life cycle costs over the building’s lifetime. The study concludes that holistic, decarbonization-oriented technologies can support cost-effective, socially responsible pathways toward low-carbon, energy-efficient multi-family residential buildings and sustainable urban development. Full article

The global energy system is undergoing a significant transformation driven by rapid electrification, urbanization, and the emergence of new categories of electricity consumers. In particular, the increasing load density in low-voltage distribution networks within urban areas requires a reconsideration of conventional methodologies for the placement of transformer substations. Traditional planning approaches are often based on empirical service radii or static demand factors and therefore fail to adequately reflect the complexity of modern urban power systems. This study proposes a multi-objective optimization model for the optimal placement of transformer substations in 10/0.4 kV urban distribution networks. The proposed model simultaneously considers power losses, economic costs, and system reliability. In addition, the design load model is extended through the introduction of a comfort coefficient that captures additional electricity consumers typical of modern urban infrastructure, including HVAC systems, elevators, pumping systems, and electric vehicle charging stations. In contrast to traditional empirical approaches, the transformer service radius is modeled as a physical parameter determined by voltage drop limits, cable thermal constraints, and failure intensity. The optimization problem is solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Each candidate solution generated by the algorithm is validated through AC load-flow simulations performed in the DIgSILENT PowerFactory environment. The proposed methodology is evaluated using real data from a 0.48 km² urban area in the city of Tashkent. The results indicate that increasing the transformer service radius reduces capital investment costs but leads to higher power losses and longer interruption durations. According to the Pareto analysis, a service radius of approximately 300 m represents the optimal compromise between technical, economic, and reliability criteria for the studied area. The proposed methodology can serve as an effective tool for the scientifically grounded planning of urban power supply systems and for improving energy efficiency in modern distribution networks. Full article

Retrofitting existing residential buildings is a critical strategy for achieving urban decarbonization while addressing public health disparities, particularly in communities disproportionately affected by environmental and socioeconomic stressors. This study presents a scalable urban building energy modeling framework that integrates physics-based simulations with machine learning to evaluate and prioritize health-driven retrofit strategies across residential building stocks. Synthetic datasets were generated through parametric simulations of representative building archetypes and retrofit scenarios, capturing variations in envelope performance, HVAC systems, infiltration rates, and ventilation strategies. Machine learning models were trained as surrogate predictors of building energy performance, enabling the rapid evaluation of retrofit impacts. A range of algorithms—including decision trees, random decision forests, gradient-boosting machines, support vector machines, k-nearest neighbors, and artificial neural networks—were evaluated. An artificial neural network implemented as a multilayer perceptron was selected for further analysis due to its strong predictive performance (R² = 0.94) and ability to capture complex nonlinear relationships among retrofit variables. The final model used the Port optimization algorithm for stable convergence and improved generalization. The framework is applied to Seattle’s Duwamish Valley, a community experiencing disproportionate environmental and health burdens, and is generalizable and transferable to other cities with comparable residential building stocks across a range of climatic and environmental contexts. The results highlight retrofit priorities—particularly infiltration reduction, HVAC upgrades, and improved envelope performance—that deliver co-benefits for energy efficiency, indoor environmental quality, and occupant health. The results demonstrate that machine learning-enhanced physics-based UBEM can significantly accelerate retrofit evaluation while preserving the interpretability of simulation-based approaches. The proposed framework provides a scalable approach for identifying health-informed retrofit pathways that support equitable urban decarbonization. Full article

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

Temporary container buildings are widely used because of their rapid construction, flexible deployment, and suitability for construction-site accommodation, emergency facilities, event housing, and other short-term scenarios. However, their energy-saving design still lacks specialized standards. Key parameters such as insulation thickness, window thermal performance, airtightness, and split-air-conditioner efficiency are often selected empirically, which makes it difficult to balance initial investment and operating cost over the actual service life. To address these issues, this study proposes a service-life cost-based coordinated optimization framework. The framework couples DeST hourly load simulation, a TRNSYS-derived dynamic energy-efficiency-ratio (EER) model for split-type air conditioners, an economic model including initial investment and electricity operating cost, and an SLSQP-based optimizer. Field measurements from a three-story container dormitory in Haidian District, Beijing, collected in August and December 2023, are used to validate the HVAC electricity-consumption model through cumulative electricity-consumption errors and CV(RMSE). Using a south-facing single container building in Beijing as the base case, optimization is conducted for design service lives of 1–10 years and further compared under different electricity-pricing models and climate regions. The results show that, within the allowable parameter ranges, the proposed method can reduce service-life cost by up to approximately 32%. In the Beijing 2-year case, the optimized scheme reduces service-life cost by 39.9% compared with the permanent-building-code benchmark and by 11.4% compared with a market sample. The results demonstrate that coordinated envelope–HVAC optimization can avoid redundant initial investment and provide scenario-adaptable design support for temporary container buildings. Full article

Buildings in warm–humid and hot–arid coastal climates experience continuous cooling demand due to high solar radiation, humidity, and extended cooling seasons. Reducing operational energy use and carbon emissions through improved early-stage design is therefore essential. This study investigates a simulation-based evolutionary optimization framework to evaluate energy-efficient design strategies for residential buildings across representative warm–humid and hot–arid climates. A prototype residential building was modeled in DesignBuilder using EnergyPlus and evaluated across four locations: Singapore, Miami, Rio de Janeiro, and Jeddah. Key variables included the window-to-wall ratio, glazing type, wall and roof constructions, cooling setpoint, and HVAC system configuration. An evolutionary search process based on the NSGA-II algorithm was applied to systematically explore high-performing building configurations using energy use intensity (EUI) and operational carbon indicators. The results indicate a consistent tendency toward boundary values within the defined parameter ranges. The window-to-wall ratios consistently approached the minimum tested value (20%), while the cooling setpoints approached the upper bound (26 °C) within the defined parameter ranges. This behavior highlights the influence of solar gains and operational temperature settings on cooling demand. Low-emissivity glazing and insulated envelope assemblies were frequently associated with improved performance. Miami achieved the lowest EUI among the high-performing configurations (75.08 kWh/m²·yr; 27.55 kgCO₂/m²·yr), while other locations showed higher demand due to climatic conditions. These findings emphasize the importance of parameter range selection and demonstrate the effectiveness of simulation-based evolutionary search methods in identifying high-performing configurations within defined constraints. Full article

The reduction in driving range during winter remains a major barrier to the widespread adoption of battery electric buses (BEBs), as battery performance degradation and increased Heating, Ventilation and Air Conditioning (HVAC) energy demand significantly raise total energy consumption. This study investigates the use of proton exchange membrane fuel cells (PEMFCs) as auxiliary power units for range-extended electric buses (FC-REEBs) under low-temperature conditions to address this challenge. A comprehensive dynamic model was developed in MATLAB/Simulink 2025a version, integrating a fuel cell system, lithium-ion battery, power conversion unit, vehicle dynamics, and cabin thermal model. The model was evaluated under the World Harmonized Vehicle Cycle (WHVC) to compare three fuel cell operation strategies defined by fuel cell capacity and operating modes for cabin heating and battery charging. Performance was compared in terms of SOC variation, fuel cell loading patterns, hydrogen consumption, and equivalent fuel economy. Results indicate that the high-capacity strategy improves SOC stability but increases hydrogen consumption and reduces overall efficiency. In contrast, the strategy prioritizing cabin heating with minimal battery charging effectively utilizes waste heat and achieves the highest equivalent fuel economy. These findings highlight key trade-offs among different operating strategies and demonstrate that fuel cells can significantly enhance BEB efficiency and driving performance in cold environments while reducing battery load. Full article

The rapid growth of renewable energy is driving a profound transformation of electricity grids toward architectures characterized by high shares of inverter-based generation, increased decentralization, and extensive digitalization. While wind and solar technologies have matured at the component level, their large-scale integration introduces technical, operational, and institutional challenges that extend beyond conventional power-system design paradigms. This review provides an integrated synthesis of the technologies, control strategies, and management processes that enable renewable energy integration into emerging electricity grids. Key challenges are analyzed across multiple timescales: fast frequency and voltage dynamics in low-inertia systems (milliseconds to seconds), forecasting, optimization, and automated control (real-time to near-real-time), and long-term planning of transmission, storage, and flexibility resources (years to decades). The synthesis covers grid-forming and grid-following inverter control, with quantitative comparison across short-circuit-ratio regimes; HVDC and HVAC transmission technologies; energy storage systems, including emerging electrochemical and mechanical solutions; smart-grid digitalization through EMS, SCADA, and digital twins; artificial intelligence and machine-learning deployments at major transmission system operators; sector coupling involving hydrogen and carbon capture; and cybersecurity considerations. Real-world case studies are used to illustrate practical lessons, with explicit attention to the brownfield–greenfield distinction between modernization of legacy systems and the design of new networks in developing regions. The review concludes by identifying key research and development priorities for achieving reliable, resilient, and economically efficient high-renewable energy systems. Full article

This study evaluates the economic feasibility of retrofitting split-type air-conditioning systems in a university administrative building in a hot-humid tropical climate in Colombia, addressing the need for cost-effective energy-efficiency strategies in such contexts. A measurement-calibrated building energy model was developed using DesignBuilder and EnergyPlus, and a baseline scenario with low-efficiency fixed-speed split units was compared against three retrofit scenarios with higher-efficiency units defined by market-available COP levels. A 10-year life-cycle cost (LCC) analysis was conducted using a discounted cash flow approach, incorporating investment costs, operation and maintenance expenses, electricity tariff escalation, and equipment performance degradation, complemented by a parametric sensitivity analysis. The results show that air-conditioning systems account for the majority of total building electricity consumption, and that retrofit scenarios reduce cooling energy use by approximately 45–53% relative to the baseline. All retrofit options yield lower life-cycle costs despite higher initial investments, achieving total LCC reductions of up to 30%. Sensitivity analysis indicates that the economic ranking of alternatives remains stable under significant variations in electricity prices. Overall, the proposed framework provides a robust and transferable approach for assessing HVAC retrofit strategies, supporting informed decision-making for energy and cost optimization in buildings located in tropical climates. Full article

This study systematically reviews the role of Artificial Intelligence (AI) and Machine Learning (ML) in supporting design decisions to improve energy efficiency in educational buildings, with particular emphasis on Saudi Arabia’s hot-arid climate. A PRISMA-based Systematic Literature Review was conducted using Google Scholar, ScienceDirect, ResearchGate, and the Saudi Digital Library for studies published between 2020 and 2025. Eligible studies included peer-reviewed articles and high-quality conference papers addressing AI/ML applications in building energy performance, optimization, or design decision-making in educational or comparable buildings. Studies published before 2020, non-peer-reviewed sources, irrelevant studies, papers focused solely on non-educational buildings without transferable findings, and studies lacking full-text access were excluded. The search identified 594 records, of which 37 studies met the eligibility criteria, resulting in a final sample of 37 reviewed sources. The review shows that ML models, hybrid methods, and multi-objective optimization techniques are increasingly used to improve energy performance and support early-stage design. The most influential variables include envelope properties, glazing, shading, lighting efficiency, HVAC systems, and renewable energy integration. However, major gaps remain, particularly the limited application of AI-driven optimization in Saudi educational buildings and the lack of real-world validation in hot-arid settings. This review provides a concise foundation for future AI-assisted design strategies aligned with sustainable educational building development and Saudi Vision 2030. Full article

Buildings account for nearly half of global energy consumption, with HVAC systems contributing approximately 40%. Fan coil units (FCUs) and fresh-air systems are widely adopted in commercial buildings for their flexibility. However, this system faces numerous critical challenges in tropical maritime climates, including low temperature control accuracy, high energy consumption, and inadequate coordination between thermal comfort and indoor air quality. This study aimed to optimize the indoor thermal environment and reduce HVAC energy consumption. It compared and analyzed the operational performance of traditional PID control and MPC. Additionally, dynamic CO₂ concentration modeling was performed to evaluate the impact of different outdoor air strategies on indoor air quality. A building simulation model was developed in TRNSYS 18. Based on the simulation data, a multi-objective model predictive control (MPC) model was created in MATLAB/Simulink. Results indicate that MPC significantly outperforms PID control in both temperature stability and energy efficiency across all outdoor air strategies, with the occupancy-based demand-controlled outdoor air strategy achieving the greatest energy savings (16.89%) while maintaining favorable indoor air quality. This study provides a theoretical foundation and practical control guidelines for the coordinated optimization of fan coil units and outdoor air systems in tropical maritime climates, facilitating the development of energy-efficient and comfortable HVAC solutions for commercial buildings. Full article

Buildings account for a substantial proportion of global energy consumption and greenhouse-gas emissions, largely due to the widespread use of conventional heating, ventilation, and air-conditioning (HVAC) systems. Hybrid systems that integrate passive and active technologies have emerged as a promising strategy for reducing energy demand while maintaining adequate indoor environmental conditions. This study evaluates the thermal and airflow performance of a hybrid air-conditioning system (HACS) that combines transparent thermal insulation (TTI) filled with R-410A refrigerant and a pig-fat-based organic phase-change material (PCM). Experimental measurements of heat flux, temperature, airflow velocity, and CO₂ concentration were conducted in a controlled prototype system. In parallel, computational simulations were performed using computational fluid dynamics (CFD) and multizone airflow modeling. The hybrid system incorporates a TTI container acting as a solar absorber and a galvanized-steel PCM container filled with 10 kg of pig fat used as latent heat storage. Heat-flux measurements were obtained using an HFS-5 sensor connected to a data acquisition system, while airflow velocity and temperature were monitored with analog data loggers. Indoor CO₂ concentrations were recorded using a dedicated CO₂ meter and simulated using CONTAMW software version 3.4.0.8. The experimental results show that the TTI and PCM containers reached average heat-flux values of 77.04 W/m² and 55.31 W/m², respectively. Airflow within the system is induced by buoyancy forces arising from temperature gradients generated by heat transfer processes at the surfaces of the TTI and PCM, resulting in a mixed air stream with an average temperature of 37.54 °C during winter operation. Recorded CO₂ concentrations remained between 290 and 413 ppm, indicating high indoor air quality levels. The overall experimental campaign spanned 6 years and 3 months. CFD simulations confirmed the airflow patterns and heat-transfer behavior observed experimentally. The findings demonstrate that hybrid air-conditioning systems combining refrigerant-filled transparent insulation with bio-based phase-change materials can effectively enhance passive thermal performance while maintaining acceptable indoor air quality. The integration of photovoltaic-powered ventilation systems could further the operational autonomy and overall energy efficiency of such hybrid systems. Full article