Search Results (580)

With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the equipment within the chiller plants of central air-conditioning systems. Traditional modeling approaches have been static and have focused on modeling within narrow time frames when a certain amount of equipment operating data has accumulated, thus prioritizing the precision of the model itself while overlooking the fact that energy-saving retrofits are a long-term process. This study proposes a modeling scheme for the equipment within chiller plants throughout the energy-saving retrofit process. Based on the differences in the amount of available operating data for the equipment and the progress of retrofit implementation, the retrofit process was divided into three stages, each employing different modeling techniques and ensuring smooth transitions between the stages. The equipment within the chiller plants is categorized into two types based on the clarity of their operating characteristics, and two modeling schemes are proposed accordingly. Based on the proposed modeling scheme, chillers and chilled-water pumps were selected to represent the two types of equipment. Real operating data from actual retrofit projects was used to model the equipment and evaluate the accuracy of the model predictions. The results indicate that the models established by the proposed modeling scheme exhibit good accuracy at each stage of the retrofit, with the coefficients of variation (CV) remaining below 6.88%. Furthermore, the prediction accuracy improved as the retrofitting process progressed. The modeling scheme performs better on equipment with simpler and clearer operating characteristics, with a CV as low as 0.67% during normal operation stages. This underscores the potential application of the proposed modeling scheme throughout the energy-saving retrofit process and provides a model foundation for the subsequent optimization of the refrigeration system. Full article

Flexibility is advocated as an effective solution to address the growing need to alleviate grid congestion, necessitating efficient energy management strategies for industrial operations. This paper presents a mixed-integer linear programming (MILP)-based optimization framework for a flexible asset in an industrial setting, aiming to minimize operational costs and enhance energy efficiency. The method integrates dynamic pricing and real-time grid analysis, alongside a state estimation model using Extended Kalman Filtering (EKF) that improves the accuracy of system state predictions. Model Predictive Control (MPC) is employed for real-time adjustments. A real-world case studies from aquaculture industries and industrial power grids in Denmark demonstrates the approach. By leveraging dynamic pricing and grid signals, the system enables adaptive pump scheduling, achieving a 27% reduction in energy costs while maintaining voltage stability within 0.95–1.05 p.u. and ensuring operational safety. These results confirm the effectiveness of grid-aware, flexible control in reducing costs and enhancing stability, supporting the transition toward smarter, sustainable industrial energy systems. Full article

With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research. Full article

While personal thermal comfort is critical for well-being and productivity, it is often overlooked by traditional building management systems that rely on uniform settings. Modern data-driven approaches often fail to capture the complex interactions between various data streams. This pilot study introduces a high-accuracy, interpretable framework for thermal comfort classification, designed to identify the most significant predictors from a comprehensive suite of environmental, physiological, and anthropometric data in a controlled group of young adults. Initially, an XGBoost model using the full 24-feature dataset achieved the best performance at 91% accuracy. However, after using SHAP analysis to identify and select the most influential features, the performance of our ensemble models improved significantly; notably, a Random Forest model’s accuracy rose from 90% to 94%. Our analysis confirmed that for this homogeneous cohort, environmental parameters—specifically temperature, humidity, and CO₂—were the dominant predictors of thermal comfort. The primary strength of this methodology lies in its ability to create a transparent pipeline that objectively identifies the most critical comfort drivers for a given population, forming a crucial evidence base for model design. The analysis also revealed that the predictive value of heart rate variability (HRV) diminished when richer physiological data, such as diastolic blood pressure, were included. For final validation, the optimized Random Forest model, using only the top 10 features, was tested on a hold-out set of 100 samples, achieving a final accuracy of 95% and an F1-score of 0.939, with all misclassifications occurring only between adjacent comfort levels. These findings establish a validated methodology for creating effective, context-aware comfort models that can be embedded into intelligent building management systems. Such adaptive systems enable a shift from static climate control to dynamic, user-centric environments, laying the critical groundwork for future personalized systems while enhancing occupant well-being and offering significant energy savings. Full article

Underwater sensor networks (UWSNs) play a crucial role in subsea operations like marine exploration and environmental monitoring. A major challenge for UWSNs is achieving effective and energy-efficient data collection, particularly in deep-sea mining, where energy limitations and long-term deployment are key concerns. This study introduces a Channel-Aware AUV-Aided Data Collection Scheme (CADC) that utilizes deep reinforcement learning (DRL) to improve data collection efficiency. It features an innovative underwater node traversal algorithm that accounts for unique underwater signal propagation characteristics, along with a DRL-based path planning approach to mitigate propagation losses and enhance data energy efficiency. CADC achieves a 71.2% increase in energy efficiency compared to existing clustering methods and shows a 0.08% improvement over the Deep Deterministic Policy Gradient (DDPG), with a 2.3% faster convergence than the Twin Delayed DDPG (TD3), and reduces energy cost to only 22.2% of that required by the TSP-based baseline. By combining a channel-aware traversal with adaptive DRL navigation, CADC effectively optimizes data collection and energy consumption in underwater environments. Full article

Problem: Existing Human–Robot Collaboration (HRC) allocators cannot react at a sub-second scale while accounting for worker fatigue. Objective: We designed a fatigue-aware combinatorial auction executed every 100 ms. Method: A human and a FANUC robot submit bids combining execution time, predicted energy, and real-time fatigue; a greedy algorithm (≤1 ms) with a $1-1/e$ approximation guarantee and O (|Bids| log |Bids|) complexity maximizes utility. Results: In 1000 RoboDK episodes, the framework increases active cycles·min⁻¹ by 20%, improves robot utilization by +10.2 percentage points, reduces per cycle fatigue by 4%, and raises the collision-free rate to 99.85% versus a static baseline (p < 0.001). Contribution: We provide the first transparent, sub-second, fatigue-aware allocation mechanism for Industry 5.0, with quantified privacy safeguards and a roadmap for physical deployment. Unlike prior auction-based or reinforcement learning approaches, our model uniquely integrates a sub-second ergonomic adaptation with a mathematically interpretable utility structure, ensuring both human-centered responsiveness and system-level transparency. Full article

This paper presents an interpretable and modular framework for energy management in smart buildings based on fuzzy logic and the IEEE Std 1855-2016. The proposed system builds upon the JFML-IoT library, enabling the integration and execution of fuzzy rule-based systems on resource-constrained IoT devices using a lightweight and extensible architecture. Unlike conventional data-driven controllers, this approach emphasizes semantic transparency, expert-driven control logic, and compliance with fuzzy markup standards. The system is designed to enhance both operational efficiency and user comfort through transparent and explainable decision-making. A four-layer architecture structures the system into Perception, Communication, Processing, and Application layers, supporting real-time decisions based on environmental data. The fuzzy logic rules are defined collaboratively with domain experts and encoded in Fuzzy Markup Language to ensure interoperability and formalization of expert knowledge. While adherence to IEEE Std 1855-2016 facilitates system integration and standardization, the scientific contribution lies in the deployment of an interpretable, IoT-based control system validated in real conditions. A case study is conducted in a realistic indoor environment, using temperature, humidity, illuminance, occupancy, and CO₂ sensors, along with HVAC and lighting actuators. The results demonstrate that the fuzzy inference engine generates context-aware control actions aligned with expert expectations. The proposed framework also opens possibilities for incorporating user-specific preferences and adaptive comfort strategies in future developments. Full article

Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). RL-PASM dynamically allocates spectrum resources across three traffic classes: high-priority (HP), low-priority (LP), and best-effort (BE), utilizing reinforcement learning (RL). This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. The environment is modeled as a discrete-time Markov Decision Process (MDP), and a context-sensitive reward function guides fairness-preserving decisions for access, preemption, coexistence, and hand-off. Extensive simulations conducted under realistic vehicular load conditions evaluate the performance across key metrics, including throughput, delay, energy efficiency, fairness, blocking, and interruption probability. Unlike prior approaches, RL-PASM introduces a unified multi-objective reward formulation and centralized RSU-based control to support adaptive priority-aware access for dynamic vehicular environments. Simulation results confirm that RL-PASM balances throughput, latency, fairness, and energy efficiency, demonstrating its suitability for scalable and resource-constrained deployments. The results also demonstrate that DQN achieves the highest average throughput, followed by vanilla QL. DQL and AC maintain fairness at high levels and low average interruption probability. QL demonstrates the lowest average delay and the highest energy efficiency, making it a suitable candidate for edge-constrained vehicular deployments. Selecting the appropriate RL method, RL-PASM offers a robust and adaptable solution for scalable, intelligent, and priority-aware spectrum access in vehicular communication infrastructures. Full article

This study presents the design and development of a structurally optimized mobile robotic system with a machine learning-based energy-efficient path planning framework. Topology optimization (TO) and finite element analysis (FEA) were applied to reduce structural weight while maintaining mechanical integrity. The optimized components were manufactured using Fused Deposition Modeling (FDM) with ABS (Acrylonitrile Butadiene Styrene) material. A custom power analysis tool was developed to compare energy consumption between the optimized and initial designs. Real-world current consumption data were collected under various terrain conditions, including inclined surfaces, vibration-inducing obstacles, gravel, and direction-altering barriers. Based on this dataset, a path planning model was developed using machine learning algorithms, capable of simultaneously optimizing both energy efficiency and path length to reach a predefined target. Unlike prior works that focus separately on structural optimization or learning-based navigation, this study integrates both domains within a single real-world robotic platform. Performance evaluations demonstrated superior results compared to traditional planning methods, which typically optimize distance or energy independently and lack real-time consumption feedback. The proposed framework reduces total energy consumption by 5.8%, cuts prototyping time by 56%, and extends mission duration by ~20%, highlighting the benefits of jointly applying TO and ML for sustainable and energy-aware robotic design. This integrated approach addresses a critical gap in the literature by demonstrating that mechanical light-weighting and intelligent path planning can be co-optimized in a deployable robotic system using empirical energy data. Full article

There is a growing global interest in making existing buildings more energy-efficient. However, stakeholders seem to have differing views on the matter, especially in developing countries, thus raising the issue of awareness amongst key stakeholders at the operational stage of existing buildings. This study aimed to examine stakeholders’ awareness of the benefits of passive retrofit in residential buildings using a convergent mixed-methods approach. Quantitative data were collected from 118 property managers and 163 owners of residential buildings, and qualitative data were collected from six government officials in Lagos State, Nigeria. The quantitative data collected were analysed using fuzzy synthetic evaluation, which addresses the fuzziness in judgement-making on multi-criteria phenomena. The results revealed that property managers and owners had a moderately high level of awareness of the environmental, economic, and social benefits of the passive retrofitting of residential buildings. However, while property managers generally had a higher level of awareness than owners, a significant gap was found in their awareness of environmental benefits. Conversely, the qualitative analysis results showed that government officials demonstrated a strong awareness of environmental benefits (energy reduction, air quality, and natural lighting) and economic advantages (cost savings and lower implementation costs). In contrast, their awareness of social benefits was limited to health improvements. The findings have practical implications for policy development and awareness campaigns. Building agencies need to further reinforce their targeted awareness programmes for owners, who demonstrated fair awareness of environmental benefits while leveraging the intermediary role of property managers in promoting home retrofit practices. Economic benefits should also be an integral part of policy frameworks to drive wider adoption across all stakeholder groups. Full article

Power-to-X (ptx) technologies are considered a promising solution for enabling the storage and sectoral integration of renewable energy, playing a vital role in the sustainable transition of industrialized energy systems. This study investigates the public acceptance of ptx technologies in Germany using a quantitative, environmental–psychological framework. Key influencing factors such as social and personal norms, environmental awareness, and openness to innovation are analyzed. A particular focus is placed on generational differences, comparing the perceptions of youth (16–25 years) and adults (>25 years) through a representative online survey. The results reveal a general lack of knowledge about ptx technologies yet a positive assessment of their decarbonization potential. Ecological impact—particularly the ability to reduce CO₂ emissions—emerges as the strongest predictor of acceptance. This is closely tied to conditions such as the use of renewable electricity and sustainable sourcing of carbon and water. Notably, acceptance among youth is also influenced by environmental awareness, prior knowledge, and perceived behavioral control. The results show that, in general, there is still a need for improved science communication to address the existing uncertainties in the population. At the same time, age-specific approaches are required, as perceptions and acceptance factors differ significantly between younger and older age groups. Full article

This study investigates the systemic tail dependence among biodiversity, clean energy, and financial transition assets using a novel partial correlation-based network approach. Analyzing eleven indices from 2019 to 2025, we capture dynamic connectedness across normal and extreme market conditions. Empirical findings indicate that clean energy assets form a central hub of connectedness, while biodiversity-linked instruments increasingly influence systemic behavior under stress. Events such as the COVID-19 vaccine rollout, the Russia–Ukraine war, and El Niño intensify these dynamics. Compared to the traditional Generalized Forecast Error Variance Decomposition (GFEVD) framework, our approach better detects short-term shocks, offering actionable insights for climate-aware investment and risk management. Full article

The integration of high-penetration renewable energy sources (RESs) into transmission networks introduces profound uncertainty that challenges traditional infrastructure planning approaches. Existing transmission expansion planning (TEP) models either rely on static scenario sets or over-conservative worst-case assumptions, failing to capture the operational stress triggered by rare but structurally impactful renewable behaviors. This paper proposes a novel bi-level optimization framework for transmission planning under adversarial uncertainty, coupling a distributionally robust upper-level investment model with a lower-level operational response embedded with physics and market constraints. The uncertainty space was not exogenously fixed, but instead dynamically generated through a physics-informed spatiotemporal generative adversarial network (PI-ST-GAN), which synthesizes high-risk renewable and load scenarios designed to maximally challenge the system’s resilience. The generator was co-trained using a composite stress index—combining expected energy not served, loss-of-load probability, and marginal congestion cost—ensuring that each scenario reflects both physical plausibility and operational extremity. The resulting bi-level model was reformulated using strong duality, and it was decomposed into a tractable mixed-integer structure with embedded adversarial learning loops. The proposed framework was validated on a modified IEEE 118-bus system with high wind and solar penetration. Results demonstrate that the GAN-enhanced planner consistently outperforms deterministic and stochastic baselines, reducing renewable curtailment by up to 48.7% and load shedding by 62.4% under worst-case realization. Moreover, the stress investment frontier exhibits clear convexity, enabling planners to identify cost-efficient resilience strategies. Spatial congestion maps and scenario risk-density plots further illustrate the ability of adversarial learning to reveal latent structural bottlenecks not captured by conventional methods. This work offers a new methodological paradigm, in which optimization and generative AI co-evolve to produce robust, data-aware, and stress-responsive transmission infrastructure designs. Full article

Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. To overcome these limitations, we propose a probabilistic methodology for turbine-level active power prediction and uncertainty estimation using high-frequency SCADA data and farm-wide autoregressive information. The method leverages a Stochastic Variational Gaussian Process with a Linear Model of Coregionalization, incorporating physical models like manufacturer power curves as mean functions and enabling flexible modeling of active power and its associated variance. The approach was validated on a wind farm in the Belgian North Sea comprising over 40 turbines, using only 15 days of data for training. The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. Improvements of around 5% were seen in dominant wind directions (200°–300°) using 2 and 3 Latent GPs, with similar improvements observed on the test set. The model also successfully reconstructs wake effects, with Energy Ratio estimates closely matching SCADA-derived values, and provides meaningful uncertainty estimates and posterior turbine correlations. These results demonstrate that the methodology enables interpretable, data-efficient, and uncertainty-aware turbine-level power predictions, suitable for advanced wind farm monitoring and control applications, enabling a more sensitive underperformance detection. Full article

Low Earth orbit (LEO) satellite networks deliver superior real-time performance and responsiveness compared to conventional satellite networks, despite technical and economic challenges such as high deployment costs and operational complexity. Nevertheless, rapid topology changes and severe energy constraints of LEO satellites make real-time routing a persistent challenge. In this paper, we employ field-programmable gate arrays (FPGAs) to overcome the resource limitations of on-board computers (OBCs) and to manage energy consumption effectively using the Eclipse-Aware Routing (EAR) algorithm, and we implement the K-Shortest Paths (KSP) algorithm directly on the FPGA. Our method first generates multiple routes from the source to the destination using KSP, then selects the optimal path based on energy consumption rate, eclipse duration, and estimated transmission load as evaluated by EAR. In large-scale LEO networks, the computational burden of KSP grows substantially as connectivity data become more voluminous and complex. To enhance performance, we accelerate complex computations in the programmable logic (PL) via pipelining and design a collaborative architecture between the processing system (PS) and PL, achieving approximately a 3.83× speedup compared to a PS-only implementation. We validate the feasibility of the proposed approach by successfully performing remote routing-table updates on the SpaceWire-based SpaceWire Brick MK4 network system. Full article