Search Results (97)

Maintaining good indoor air quality (IAQ) is essential for student health, cognitive performance, and overall well-being. Traditional ventilation strategies, particularly constant air volume systems and manual window operation, often fail to maintain optimal IAQ while simultaneously increasing building energy consumption. In response, smart ventilation systems have emerged as a promising alternative capable of dynamically modulating airflow based on occupancy patterns and real-time pollutant levels. This study presents a systematic review of fourteen carefully selected peer-reviewed studies (2015–2025) that represent the most recent and methodologically robust research on smart ventilation applications in school environments across diverse climatic conditions. The selected studies encompass experimental, simulation-based, and hybrid methodologies, and classify control strategies into demand-controlled, temperature-adaptive, occupancy-based, AI-enhanced, and building management system (BMS)-integrated approaches. Collectively, the findings demonstrate measurable improvements in IAQ indicators (e.g., carbon dioxide (CO₂), particulate matter (PM_2.5), ozone (O₃), and volatile organic compounds (VOCs)) and significant energy savings, in some cases exceeding 60%, while also identifying system vulnerabilities such as fault sensitivity, short monitoring durations, and limited long-term validation. Importantly, the review reveals critical geographic and climatic research gaps, particularly in hot–arid regions where ventilation-related cooling demand is substantial, as well as limited long-term assessments in cold climates. Furthermore, although smart ventilation systems perform effectively under controlled conditions, insufficient real-world verification, user interaction analysis, and climate-specific optimization constrain broader implementation. Addressing these gaps through climate-dependent performance evaluation and long-term operational studies is essential to unlocking the full potential of smart ventilation systems in delivering healthier, energy-efficient classrooms. 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

Air-to-air heat pumps are a key technology for improving energy efficiency and reducing carbon emissions in residential buildings, yet their optimal control remains challenging under real-world conditions. This study evaluates the performance of a hybrid physical–LSTM model for controlling an air-to-air heat pump in a residential building in Zadar, Croatia. The hybrid framework integrates a first-order energy balance model of the building envelope with LSTM-based temperature correction using adaptive weighting. The physical model was calibrated and validated against 52,128 real IoT measurements collected during the 2024/2025 heating season, achieving high accuracy (RMSE ≈ 0.076 °C). Rolling one-day and continuous multi-day closed-loop simulations (up to 15 days) show that the hybrid model yields slightly lower RMSE in long-term runs compared to the pure physical model. However, this apparent statistical improvement is accompanied by systematic underestimation of indoor temperature and significantly higher simulated energy consumption. The results indicate that the observed effect originates from an implicit virtual heat flux introduced by the LSTM correction, which affects thermodynamic consistency in closed-loop operation. The findings highlight that short-term error metrics such as RMSE alone are insufficient for evaluating hybrid models intended for model predictive control (MPC). The main contribution of this study is the explicit demonstration and quantification of an implicit virtual heat flux generated by the LSTM correction in closed-loop multi-day operation, which leads to misleading statistical improvements while causing significant thermodynamic inconsistency and energy overconsumption. In 15-day continuous simulations the hybrid model (ω = 0.05–0.10) caused an indoor temperature underestimation of 1.25–1.31 °C and increased simulated electricity consumption by more than 300% (316 kWh vs. 72 kWh) compared to the physical model. These results have direct implications for the development of reliable digital twins and model predictive control strategies in residential HVAC systems. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

With the expansion of power grids, the increasing peak-valley load difference threatens grid security, a challenge addressed by time-of-use pricing which incentivizes load shifting. Since central air-conditioning systems account for over 60% of building energy use, optimizing them for efficiency and cost under time-of-use pricing is crucial. This study presents an integrated optimization framework that coordinates photovoltaic generation, battery storage, and grid power. The approach develops a BES-LSTM forecasting model by using the Bald Eagle Search (BES) algorithm to tune Long Short-Term Memory (LSTM) network parameters for accurate cooling-load prediction. A central air-conditioning water-system energy-minimization model is then formulated and solved with an improved BES algorithm that incorporates adaptive opposition-based learning, logistic chaotic mapping, and Lévy flight. Finally, a daily schedule is optimized by partitioning time according to time-of-use price intervals and treating generation output, battery charge/discharge, and grid draw as decision variables. Simulations demonstrate that the framework reduces the central air-conditioning water system’s total energy consumption by an average of 28.7% and lowers energy costs under time-of-use pricing by 22.38%, achieving both significant energy savings and economic benefits. Full article

A comprehensive multidisciplinary review of recent advances in aviation emissions modeling methodologies and mitigation strategies through optimized in-flight operational procedures, and how they could be considered in evaluating their climatic impact is presented. With reference to the Terminal Maneuvering Area (TMA), the article critically examines current and emerging strategies, particularly those enabled by GNSS-based capabilities and Performance-Based Navigation (PBN), to enhance aircraft efficiency and reduce fuel consumption and associated chemical emissions. The study also explores the state-of-the-art methodologies for modeling both CO₂ and non-CO₂ emissions and addresses the problem of contrails formation, highlighting the main relevant aspects that can be useful for the definition of future mitigation strategies. Furthermore, it analyzes evolving optimization techniques aimed at real-time 4D trajectory planning able to consider the atmospheric conditions, with the overall objective of minimizing the aircraft environmental impact while in flight. Finally, the paper discusses suitable metrics for evaluating both short-term local air quality effects and long-term global climate implications, offering an integrated framework for sustainable aviation operations. Full article

The conventional reactive cooling strategy, which relies on static thresholds, has become inadequate for managing dynamically changing heat loads, often resulting in energy inefficiency and increased risk of local hot spots. In this study, we develop a data center cooling optimization system that integrates distributed sensor arrays for predictive analysis. By deploying high-density temperature and humidity sensors both inside and outside server racks, a real-time, high-fidelity three-dimensional digital twin of the data center’s thermal environment is constructed. Time-series analysis combined with Long Short-Term Memory algorithms is employed to forecast temperature and humidity based on the extensive environmental data collected, achieving high predictive accuracy with a root mean square error of 0.25 and an R² value of 0.985. Building on these predictions, a proactive cooling control strategy is formulated to dynamically adjust fan speeds and the opening degree of chilled-water valves in computer room air conditioning units, changing the cooling approach from passive to preemptive prevention of overheating. Compared with conventional proportional–integral–differential control, the developed system significantly reduces overall energy consumption and maintains all equipment within safe operating temperatures. Specifically, the framework has reduced the energy consumption of the cooling system by 37.5%, lowered the overall power usage effectiveness of the data center by 12% (1.48 to 1.30), and suppressed the cumulative hotspot duration (temperature 27 °C) by nearly 96% (from 48 to 2 h). Full article

The model of a liquid desiccant dehumidification air conditioning (LDAC) system is one of the key foundations for achieving efficient cooling, dehumidification and regeneration, and saving energy consumption. The data-driven modeling method does not need to understand the complex heat and mass transfer mechanism and equipment physical information, thus the modeling complexity is greatly reduced. This paper proposes a temperature and humidity prediction model integrating the Black Kite Algorithm (BKA), Bidirectional Temporal Convolutional Network (BiTCN), Bidirectional Long Short-Term Memory (BiLSTM), and Self-Attention mechanism (SA). The model extracts local spatiotemporal features from sequence data through BiTCN, enhances the understanding of contextual dependencies in temporal data using BiLSTM, and employs the SA to assign dynamic weights to different time steps. Furthermore, BKA is adopted to optimize the hyperparameter combinations of the neural network, thereby improving prediction accuracy. To validate the model performance, an experimental platform for an LDAC system was established to collect operational data under multiple working conditions, constructing a comprehensive dataset for simulation analysis. Experimental results demonstrate that compared to conventional time-series prediction models, the proposed model achieves higher accuracy in predicting outlet temperature and humidity across various operating conditions, providing reliable technical support for system real-time control and performance optimization. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

Microscopic Particulate Matter (PM) below 10 µm can enter the respiratory system and affect human health in the short and long term. Railway enclosures are sites with high concentrations of fine PM and technical solutions like mechanical filtration exist to increase the air quality. However, several crucial factors must be evaluated and optimized like energy consumption, maintenance cost/interval, design and control. A fast and adaptable evaluation of decontamination solutions is required to find the optimal solution. To answer this, a 1D multizone model based on station discretization aligned with the track direction is proposed to precisely place decontamination systems along the station. In each zone, a set of ordinary differential equations is used to forecast the daily progression of PM concentrations, based on physical parameters (air and train velocities, and train traffic) used to describe the different physical phenomena (resuspension, deposition, ventilation and generation). Three-dimensional CFD (Computational Fluid Dynamics) simulations are used to characterize the efficiency and range of decontamination products and reproduce their effect in the 1D model. This approach allows for flexible optimization of local and global decontamination efficiencies with multiple parameter changes. PM₁₀ and PM_2.5 (below 10 and 2.5 µm) are studied here as they are often monitored. Full article

The increasing atmospheric carbon dioxide (CO₂) emissions are widely recognized as the primary driving force behind the phenomenon of global warming. Considering environmental concerns and the depletion of fossil fuel reserves, the use of electric vehicles (EVs) in transportation has emerged as one of the most promising technological alternatives to conventional gasoline-powered cars. Compared to their gasoline counterparts, EVs significantly reduce the costs associated with air pollution and mitigate adverse effects on human health. Owing to these characteristics, EVs have become one of the key components of the transition toward a sustainable future, while also steering the transformation of the global automotive industry. This transition is reshaping the structure of the global automobile industry. Many countries aim to achieve their greenhouse gas reduction targets by promoting the adoption of EVs. This study aims to empirically examine the effects of electric vehicles on CO₂ emissions in 15 high-income countries during the period 2010–2023, highlighting both short- and long-term environmental impacts. The analysis also considers economic and socio-demographic variables such as gross domestic product (GDP), urbanization, and fossil fuel consumption. The findings indicate that the share of EVs significantly reduces CO₂ emissions, whereas sales have a short-term increasing effect. Full article

Heating, ventilation, and air conditioning (HVAC) systems are the primary energy consumers in modern office buildings, with chillers consuming the most energy. As critical components of building air conditioning, the effective functioning of HVAC systems holds substantial importance for energy preservation and emission mitigation. To enhance the operational performance of HVAC systems and accomplish energy conservation objectives, precise cooling load forecasting is essential. This research employs an office facility in Binzhou City, Shandong Province, as a case investigation and presents a day-ahead scheduling-based model predictive control (MPC) approach for HVAC systems, which targets minimizing the overall system power utilization. An attention mechanism-based long short-term memory (LSTM) neural network forecasting model is developed to predict the building’s cooling demand for the subsequent 24 h. Based on the forecasting outcomes, the MPC controller adopts the supply–demand equilibrium between cooling capacity and cooling demand as the central constraint and utilizes the particle swarm optimization (PSO) algorithm for rolling optimization to establish the optimal configuration approach for the chiller flow rate and temperature, thereby realizing the dynamic control of the HVAC system. To verify the efficacy of this approach, simulation analysis was performed using the TRNSYS simulation platform founded on the actual operational data and meteorological parameters of the building. The findings indicate that compared with the conventional proportional–integral–derivative (PID) control approach, the proposed day-ahead scheduling-based MPC strategy can attain an average energy conservation rate of 9.23% over a one-week operational period and achieve an energy-saving rate of 8.25% over a one-month period, demonstrating its notable advantages in diminishing building energy consumption. Full article

Real-time prediction of the instantaneous fuel consumption rate (FCR) of any vehicle is the key to improving energy efficiency and reducing emissions. The conventional prediction methods, which include an on-board diagnostic (OBD) system, require the specific vehicle parameters and environmental conditions such as air density. We propose a data-driven Bayesian optimization and Monte Carlo (MC) Dropout methods-based long short-term memory (BMC-LSTM) network FCR prediction model using only the vehicle’s throttle position, velocity, and acceleration data. The cost-effective LSTM network-based solution enhances the high-resolution prediction accuracy within a deep learning framework. The network is integrated with the Bayesian optimization and MC-Dropout methods to ensure a probabilistically optimal hyperparameter set and robust networks. The proposed method presents an FCR model that provides calibrated predictions and reliability against distribution drift by probabilistically tuning hyperparameters with Bayesian optimization and quantifying epistemic uncertainty with the MC-Dropout. Our approach requires only vehicle speed, longitudinal acceleration, and throttle position at inference time. Note, however, that the reference FCR used to train and validate the models was obtained from OBD during data acquisition. The performance of the proposed method is compared with a conventional LSTM and Bidirectional LSTM-based multidimensional models, XGBoost and support vector regression-based models, and first- and fourth-order polynomials, which are derived using the least-squares method. The prediction performance of the method is evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute, and R-squared statistical metrics. The proposed method achieves a superior $R^2$ score and substantially reduces the conventional error metrics. Full article

Indoor air quality (IAQ) is a critical determinant of health, comfort, and productivity, and is strongly connected to building energy demand due to the role of ventilation and air treatment in HVAC systems. This review examines recent applications of Artificial Intelligence (AI) and Machine Learning (ML) for IAQ prediction across residential, educational, commercial, and public environments. Approaches are categorized by predicted parameters, forecasting horizons, facility types, and model architectures. Particular focus is given to pollutants such as CO₂, PM2.5, PM10, VOCs, and formaldehyde. Deep learning methods, especially the LSTM and GRU networks, achieve superior accuracy in short-term forecasting, while hybrid models integrating physical simulations or optimization algorithms enhance robustness and generalizability. Importantly, predictive IAQ frameworks are increasingly applied to support demand-controlled ventilation, adaptive HVAC strategies, and retrofit planning, contributing directly to reduced energy consumption and carbon emissions without compromising indoor environmental quality. Remaining challenges include data heterogeneity, sensor reliability, and limited interpretability of deep models. This review highlights the need for scalable, explainable, and energy-aware IAQ prediction systems that align health-oriented indoor management with energy efficiency and sustainability goals. Such approaches directly contribute to policy priorities, including the EU Green Deal and Fit for 55 package, advancing both occupant well-being and low-carbon smart building operation. Full article

Aware of the nefarious effects of excessive exploitation of natural resources and the greenhouse gases emissions linked to building sector, the concept of smart buildings emerged, referring to a building that uses clean energy efficiently. This requires intelligent control systems to manage the use of residential energy consuming devices, namely the HVAC (Heating, Ventilation, Air-conditioning) system. This system consumes up to 50% of the total energy used by a building. In this paper, we introduce a RL (Reinforcement Learning) and MPC-LSTM (Model Predictive Control-Long-Short Term Memory) hybrid control system that combines DNNs (Deep Neural Networks), through RL, with LSTM’s long-short memory technique and MPC’s control characteristics. The goal of our model is to maintain thermal comfort of residents while optimizing energy consumption. Consequently, to train and test our model, we generate our own dataset using a building model of a corporate building in Casablanca, Morocco, combined with weather data of the same city. Simulations confirm the robustness of our model as it outperforms basic control methods in terms of thermal comfort and energy consumption especially during summer. Compared to conventional methods, our approach resulted in a 45.4% and 70.9% reduction in energy consumption, in winter and summer, respectively. Our approach also resulted in 26 less comfort violations during winter. On the other hand, during summer, our approach found a compromise between energy consumption and comfort with no more than 2.5 °C above ideal temperature limit. Full article