Search Results (1,575)

The residential energy sector contributes substantially to global energy-related emissions. Effective energy management requires an understanding occupant behavior through activity detection and habit identification. Recent advances in artificial intelligence (AI) and machine learning (ML) enable the automatic detection of user activities and prediction of energy needs based on historical consumption data. Non-intrusive load monitoring (NILM) facilitates device-level disaggregation without additional sensors, supporting demand forecasting and behavior-aware control in Home Energy Management Systems (HEMSs). This review synthesizes various AI and ML approaches for detecting user activities and energy habits in HEMSs from 2020 to 2025. The analyses revealed that deep learning (DL) models, with their ability to capture complex temporal and nonlinear patterns in multisensor data, achieve superior accuracy in activity detection and load forecasting, with occupancy detection reaching 95–99% accuracy. Hybrid systems combining neural networks and optimization algorithms demonstrate enhanced robustness, but challenges remain in limited cross-building generalization, insufficient interpretability of deep models, and the absence of dataset standardized. Future work should prioritize lightweight, explainable edge-ready models, federated learning, and integration with digital twins and control systems. It should also extend energy optimization toward occupant wellbeing and grid flexibility, using standardized protocols and open datasets for ensuring trustworthy and sustainability. Full article

The fastest growth in electricity demand in the industrialized world will likely come from the broad adoption of artificial intelligence (AI)—accelerated by the rise of generative AI models such as OpenAI’s ChatGPT. The global “data center arms race” is driving up power demand and grid stress, which creates local and regional challenges because people in the area understand that the additional data center-related electricity demand is coming from faraway places, and they will have to support the additional infrastructure while not directly benefiting from it. So, there is an incentive for the data center operators to manage the fast and unpredictable power surges internally so that their loads appear like a constant baseload to the electricity grid. Such high-intensity and short-duration loads can be served by hybrid energy storage systems (HESSs) that combine multiple storage technologies operating across different timescales. This review presents an overview of energy storage technologies, their classifications, and recent performance data, with a focus on their applicability to AI-driven computing. Technical requirements of storage systems, such as fast response, long cycle life, low degradation under frequent micro-cycling, and high ramping capability—which are critical for sustainable and reliable data center operations—are discussed. Based on these requirements, this review identifies lithium titanate oxide (LTO) and lithium iron phosphate (LFP) batteries paired with supercapacitors, flywheels, or superconducting magnetic energy storage (SMES) as the most suitable HESS configurations for AI data centers. This review also proposes AI-specific evaluation criteria, defines key performance metrics, and provides semi-quantitative guidance on power–energy partitioning for HESSs in AI data centers. This review concludes by identifying key challenges, AI-specific research gaps, and future directions for integrating HESSs with on-site generation to optimally manage the high variability in the data center load and build sustainable, low-carbon, and intelligent AI data centers. Full article

In recent years, the rapid development of commercial satellite projects, such as low-Earth orbit (LEO) communication and remote sensing constellations, has driven the satellite industry toward low-cost, rapid development, and large-scale deployment. Commercial off-the-shelf (COTS) components have been widely adopted across various commercial satellite platforms due to their advantages of low cost, high performance, and plug-and-play availability. However, the space environment is complex and hostile. COTS components were not originally designed for such conditions, and they often lack systematically flight-verified protective frameworks, making their reliability issues a core bottleneck limiting their extensive application in critical missions. This paper focuses on COTS solid-state drives (SSDs) onboard the Jilin-1 KF satellite and presents a full-lifecycle reliability practice covering component selection, system design, on-orbit operation, and failure feedback. The core contribution lies in proposing a full-lifecycle methodology that integrates proactive design—including multi-module redundancy architecture and targeted environmental stress screening—with on-orbit data monitoring and failure cause analysis. Through fault tree analysis, on-orbit data mining, and statistical analysis, it was found that SSD failures show a significant correlation with high-energy particle radiation in the South Atlantic Anomaly region. Building on this key spatial correlation, the on-orbit failure mode was successfully reproduced via proton irradiation experiments, confirming the mechanism of radiation-induced SSD damage and providing a basis for subsequent model development and management decisions. The study demonstrates that although individual COTS SSDs exhibit a certain failure rate, reasonable design, protection, and testing can enhance the on-orbit survivability of storage systems using COTS components. More broadly, by providing a validated closed-loop paradigm—encompassing design, flight verification and feedback, and iterative improvement—we enable the reliable use of COTS components in future cost-sensitive, high-performance satellite missions, adopting system-level solutions to balance cost and reliability without being confined to expensive radiation-hardened products. Full article

Solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid systems exhibit inherent system uncertainties and unmodeled dynamics during operation, which compromise the accuracy of predicting gas turbine power. This poses challenges for system operation analysis and energy management. To enhance the prediction accuracy and stability of gas turbine power in SOFC/GT hybrid systems, a power prediction method capable of incorporating total system disturbance information is investigated. This study constructs a high-fidelity simulation model of an SOFC/GT hybrid system to generate gas turbine power prediction datasets. With fuel utilization (FU) as the input and gas turbine power as the output, this system is assumed to be a first-order dynamic system. Building upon this foundation, an extended state observer (ESO) is employed to extract the total system disturbance ($f$) that affects the power output of the gas turbine, excluding fuel utilization. The total disturbance $f$ and fuel utilization are used as inputs to a Backpropagation (BP) neural network to construct a disturbance-aware power prediction model. The predictive performance of the proposed method is evaluated by comparison with a BP neural network without disturbance estimation information and several benchmark models. Simulation results indicate that incorporating the disturbance term estimated by ESO enhances both the accuracy and stability of the BP neural network’s power prediction, particularly under operating conditions characterized by significant power fluctuations. Quantitatively, when comparing the predictive model with disturbance included to the model without disturbance, including the disturbance reduces the prediction error by approximately 89.33% (MSE) and 67.34% (RMSE), while the coefficient of determination R² increases by 0.1132, demonstrating a substantial improvement in predictive performance under the same test conditions. The research findings indicate that incorporating disturbance information into data-driven prediction models represents a viable modeling approach, providing effective support for predicting gas turbine power in SOFC/GT hybrid systems. Full article

This article examines an integrated approach to data acquisition and transmission within an intelligent thermal conditioning system for engines and vehicles that operates using thermal energy storage and the digital twin concept. The system is characterized by its use of multiple primary energy sources to power internal subsystems and maintain optimal engine and vehicle temperature conditions. Building on a formalized conceptual model of the intelligent thermal conditioning system, the study identifies key technological features required for implementing complex operational processes, as well as the stages necessary for applying the proposed approach during the design and modernization phases throughout the system’s life cycle. A core block diagram of the system’s digital twin is presented, developed using mathematical models that describe support and monitoring processes under real operating conditions. Additionally, an architectural framework for organizing data collection and transmission is proposed, highlighting the integration of digital twin technologies into the thermal conditioning workflow. The article also introduces methods for adaptive data formation, transfer, and processing, supported by a specialized onboard software-diagnostic complex that enables structured information management. The practical implementation of the proposed solutions has the potential to enhance the energy efficiency of thermal conditioning processes and improve the reliability of vehicles employing thermal energy storage technologies. Full article

The integration of smart electricity management models in buildings is a key strategy for improving living comfort and optimizing energy efficiency. The incentive mechanisms introduced by the Italian regulatory framework for widespread self-consumption and energy communities encourage the deployment of smart management systems within Collective Self-Consumption Groups (CSGs) and Renewable Energy Communities (RECs). These mechanisms drive the search for solutions that combine occupant well-being with economic benefits, thereby fostering citizen participation in aggregation models that play a key role in the transition towards a progressively decarbonized electricity system. In this context, an optimization model for the management of residential heat pumps is proposed, aimed at identifying the best compromise between thermal comfort, electricity consumption, and economic benefits. The approach developed in the research encourages citizens to take an active role without the need for burdensome commitments and/or significant changes in their daily habits, in line with the importance that users themselves attribute to these aspects. To demonstrate the potential of the proposed approach, a case study was developed on a residential building located in Sardinia (Italy). The implementation of an optimization model aimed at simultaneously maximizing economic benefits and indoor thermal comfort is simulated. The model’s economic and energy performance is assessed and compared with the results obtained using different advanced heat pump control and management strategies. Full article

The global shipping industry is surging ahead, and with it, a quiet revolution is taking place on the water: marine lithium-ion batteries have emerged as a crucial clean energy carrier, powering everything from ferries to container ships. When these vessels dock, they increasingly rely on shore power charging systems to refuel—essentially, plugging in instead of idling on diesel. But predicting how much power they will need is not straightforward. Think about it: different ships, varying battery sizes, mixed charging technologies, and unpredictable port stays all come into play, creating a load profile that is random, uneven, and often concentrated—a real headache for grid planners. So how do you forecast something so inherently variable? This study turned to the Monte Carlo method, a probabilistic technique that thrives on uncertainty. Instead of seeking a single fixed answer, the model embraces randomness, feeding in real-world data on supply modes, vessel types, battery capacity, and operational hours. Through repeated random sampling and load simulation, it builds up a realistic picture of potential charging demand. We ran the numbers for a simulated fleet of 400 vessels, and the results speak for themselves: load factors landed at 0.35 for conventional AC shore power, 0.39 for high-voltage DC, 0.33 for renewable-based systems, 0.64 for smart microgrids, and 0.76 when energy storage joined the mix. Notice how storage and microgrids really smooth things out? What does this mean in practice? Well, it turns out that Monte Carlo is not just academically elegant, it is practically useful. By quantifying uncertainty and delivering load factors within confidence intervals, the method offers port operators something precious: a data-backed foundation for decision-making. Whether it is sizing infrastructure, designing tariff incentives, or weighing the grid impact of different shore power setups, this approach adds clarity. In the bigger picture, that kind of insight matters. As ports worldwide strive to support cleaner shipping and align with climate goals—China’s “dual carbon” ambition being a case in point—achieving a reliable handle on charging demand is not just technical; it is strategic. Here, probabilistic modeling shifts from a simulation exercise to a tangible tool for greener, more resilient port energy management. Full article

Despite the immense potential of digital intelligence technologies to enhance corporate profitability, manufacturing enterprises often face the “digital–green paradox”, which indicates that while companies invest in digital and intelligent transformation, their energy consumption increases rather than promoting green transition. To provide reasonable transformation solutions for manufacturers still caught in this paradox, this paper adopts a single-case study approach from a product lifecycle perspective. Focusing on FAW-Volkswagen—a manufacturing enterprise demonstrating outstanding performance in digital-intelligent green transformation—this study conducts an in-depth investigation into the stage characteristics and underlying mechanisms. The results show that the following: (1) The digital-intelligent green transformation of manufacturing enterprises is an iterative process evolving from “green design, low-carbon production, intelligent service to enterprise spiral value-added”, with distinct digital-intelligent empowerment models at each stage. (2) By leveraging digital-intelligent technologies, manufacturing enterprises can build a multi-tiered “internal-external dual circulation” green development system encompassing the “enterprise—industrial chain—full ecosystem,” driving comprehensive green upgrades across the entire industry and ecosystem. This paper reveals the intrinsic mechanisms through which digital-intelligent technologies facilitate manufacturing enterprises’ green transformation. It expands and enriches the research context and theoretical implications of product lifecycle management, offering management insights and strategic references for other enterprises pursuing green transformation and upgrading pathways in the digital-intelligent economy era. Full article

Organic phase change materials show potential for thermal energy storage, but their scalable implementation is limited by fixed phase change temperatures, molten leakage, and low thermal conductivity. To address the temperature constraint, a binary eutectic system of 1-tetradecanol and 1,10-decanediol is prepared, expanding the operational temperature range for building thermal management. Compositing the eutectic with expanded graphite yields a composite material that exhibits a low leakage and a markedly improved thermal conductivity of 4.642 W/(m·K), which is approximately 12 times that of the pure eutectic. The composite maintains distinct phase transition properties, with melting and solidification temperatures of 37.77 °C and 29.38 °C and corresponding latent heats of 218.80 J/g and 216.66 J/g. It also demonstrates a good cycling stability, retaining over 87% of the original latent heat after 2000 thermal cycles. While these findings remain valid under controlled conditions, further studies are required to evaluate their practical feasibility and long-term durability in real-world scenarios. This work establishes a systematic approach for fabricating composite phase change materials and provides a promising candidate for building thermal management applications. Full article

The emulsification of a molten fusible metal alloy in a liquid epoxy matrix with its subsequent curing is a novel way to create a highly concentrated phase-change material. However, numerous challenges have arisen. The high interfacial tension between the molten metal and epoxy resin and the difference in their viscosities hinder the stretching and breaking of metal droplets during stirring. Further, the high density of metal droplets and lack of suitable surfactants lead to their rapid coalescence and sedimentation in the non-cross-linked resin. Finally, the high differences in the thermal expansion coefficients of the metal alloy and cross-linked epoxy polymer may cause cracking of the resulting phase-change material. This work overcomes the above problems by using nanosilica-induced physical gelation to thicken the epoxy medium containing Wood’s metal, stabilize their interfacial boundary, and immobilize the molten metal droplets through the creation of a gel-like network with a yield stress. In turn, the yield stress and the subsequent low-temperature curing with diethylenetriamine prevent delamination and cracking, while the transformation of the epoxy resin as a physical gel into a cross-linked polymer gel ensures form stability. The stabilization mechanism is shown to combine Pickering-like interfacial anchoring of hydrophilic silica at the metal/epoxy boundary with bulk gelation of the epoxy phase, enabling high metal loadings. As a result, epoxy shape-stable phase-change materials containing up to 80 wt% of Wood’s metal were produced. Wood’s metal forms fine dispersed droplets in epoxy medium with an average size of 2–5 µm, which can store thermal energy with an efficiency of up to 120.8 J/cm³. Wood’s metal plasticizes the epoxy matrix and decreases its glass transition temperature because of interactions with the epoxy resin and its hardener. However, the reinforcing effect of the metal particles compensates for this adverse effect, increasing Young’s modulus of the cured phase-change system up to 825 MPa. These form-stable, high-energy-density composites are promising for thermal energy storage in building envelopes, radiation-protective shielding, or industrial heat management systems where leakage-free operation and mechanical integrity are critical. Full article

Urban Water Systems (UWS) are complex infrastructures that interact with energy, food, ecosystems and socio-political systems, and are under growing pressure from climate change and resource depletion. Planning circular interventions in this context requires system-level analysis to avoid fragmented, siloed decisions. This paper develops the Hydrosocial Resource Urban Nexus (HRUN) framework that integrates hydrosocial thinking with the Water–Energy–Food–Ecosystems (WEFE) nexus to guide UWS design. We conduct a structured literature review and analyse different configurations of circular interventions, mapping their synergies and trade-offs across socioeconomic and environmental functions of hydrosocial systems. The framework is operationalised through a typology of circular interventions based on their circularity purpose (water reuse, resource recovery and reuse, or water-cycle restoration) and management scale (from on-site to centralised), while greening degree (from grey to green infrastructure) and digitalisation (integration of sensors and control systems) are treated as transversal strategies that shape their operational profile. Building on this typology, we construct cause–effect matrices for each intervention type, linking recurring operational patterns to hydrosocial functionalities and revealing associated synergies and trade-offs. Overall, the study advances understanding of how circular interventions with different configurations can strengthen or weaken system resilience and sustainability outcomes. The framework provides a basis for integrated planning and for quantitative and participatory tools that can assess trade-offs and governance effects of different circular design choices, thereby supporting the transition to more resilient and just water systems. Full article

Building resilient and sustainable energy systems is a critical challenge for disaster-prone regions in the Global South. This study investigates the energy management of a networked microgrid (NMG) system on Lombok Island, Indonesia, a region frequently exposed to natural disasters (NDs) and characterized by vulnerable grid infrastructure. A multi-objective optimization framework is developed to jointly minimize operational costs, load-not-served, and environmental impacts under both normal and abnormal operating conditions. The proposed strategy employs the Multi-objective JAYA (MJAYA) algorithm to coordinate photovoltaic generation, diesel generators, battery energy storage systems, and inter-microgrid power exchanges within a 20 kV distribution network. Using real load, generation, and electricity price data, we evaluate the NMG’s performance under five representative fault scenarios that emulate ND-induced outages, including grid disconnection and loss of inter-microgrid links. Results show that the interconnected NMG structure significantly enhances system resilience, reducing load-not-served from 366.3 kWh in fully isolated operation to only 31.7 kWh when interconnections remain intact. These findings highlight the critical role of cooperative microgrid networks in strengthening community-level energy resilience in vulnerable regions. The proposed framework offers a practical decision-support tool for planners and governments seeking to enhance energy security and advance sustainable development in disaster-affected areas. Full article

The relevance of this study is driven by the increasing requirements for the energy efficiency and indoor comfort of residential and public buildings, particularly in regions with extreme climatic conditions characterized by substantial daily and seasonal temperature fluctuations. Effective management of heat transfer through building envelopes has become a key factor in reducing energy consumption and improving indoor comfort. This paper presents the results of an experimental–numerical investigation of the thermal behavior of an adaptive exterior wall system with a controllable air cavity. Steady-state and transient simulations were performed for three envelope configurations: a baseline design, a design with vertical air channels, and an adaptive configuration equipped with adjustable openings. Quantitative analysis showed that during the winter period, the adaptive configuration increases the interior surface temperature by 1.5–2.3 °C compared to the baseline design, resulting in a 12–18% reduction in the specific heat flux through the wall. In the summer period, the temperature of the exterior cladding decreases by 3–5 °C relative to the baseline, which reduces heat gains by 8–14% and lowers the cooling load. Additional analysis of temperature fields demonstrated that the presence of vertical air channels has a limited effect during winter: temperature differences at the surfaces do not exceed 1 °C. A similar pattern is observed in warm periods; however, due to controlled air circulation, the adaptive configuration provides an improved thermal regime. The results confirm the effectiveness of the adaptive wall system under the climatic conditions of southern Kazakhstan, characterized by high solar radiation and large diurnal temperature variations. The practical significance of the study lies in the potential application of adaptive façades to enhance the energy efficiency of buildings during both winter and summer seasons. Full article

Sustainable Urban Drainage Systems (SUDSs) are essential for stormwater management in urban areas, with varying hydrological, social, ecological, and economic benefits. Nevertheless, choosing the SUDS most appropriate for public spaces poses a challenge when balancing details/specifications against community decisions, primarily social implications and perceptions. Building on the SUDS design pillar of the amenity, this study outlines a three-phase methodological framework for selecting SUDS based on social facilitation. The first phase introduces the application of the Partial Least Squares Structural Equation Modeling (PLS-SEM) and Classificatory Expectation–Maximization (CEM) techniques by modeling complex social interdependencies to find critical components related to urban planning. A Likert scale survey was also conducted with 440 urban dwellers in Tunja (Colombia), which identified three dimensions: Residential Satisfaction (RS), Resilience and Adaptation to Climate Change (RACC), and Community Participation (CP). In the second phase, the factors identified above were transformed into eight operational criteria, which were weighted using the Analytic Hierarchy Process (AHP) with the collaboration of 35 international experts in SUDS planning and implementation. In the third phase, these weighted criteria were used to evaluate and classify 13 types of SUDSs based on the experts’ assessments of their sub-criteria. The results deliver a clear message: cities must concentrate on solutions that will guarantee that water is managed to the best of their ability, not just safely, and that also enhance climate resilience, energy efficiency, and the ways in which public space is used. Among those options considered, infiltration ponds, green roofs, rain gardens, wetlands, and the like were the best-performing options, providing real and concrete uses in promoting a more resilient and sustainable urban water system. The methodology was also used in a real case in Tunja, Colombia. In its results, this approach proved not only pragmatic but also useful for all concerned, showing that the socio-cultural dimensions can be truly integrated into planning SUDSs and ensuring success. Full article

This paper reviews Artificial Intelligence techniques for distributed energy management, focusing on integrating machine learning, reinforcement learning, and multi-agent systems within IoT-Edge-Cloud architectures. As energy infrastructures become increasingly decentralized and heterogeneous, AI must operate under strict latency, privacy, and resource constraints while remaining transparent and auditable. The study examines predictive models ranging from statistical time series approaches to machine learning regressors and deep neural architectures, assessing their suitability for embedded deployment and federated learning. Optimization methods—including heuristic strategies, metaheuristics, model predictive control, and reinforcement learning—are analyzed in terms of computational feasibility and real-time responsiveness. Explainability is treated as a fundamental requirement, supported by model-agnostic techniques that enable trust, regulatory compliance, and interpretable coordination in multi-agent environments. The review synthesizes advances in MARL for decentralized control, communication protocols enabling interoperability, and hardware-aware design for low-power edge devices. Benchmarking guidelines and key performance indicators are introduced to evaluate accuracy, latency, robustness, and transparency across distributed deployments. Key challenges remain in stabilizing explanations for RL policies, balancing model complexity with latency budgets, and ensuring scalable, privacy-preserving learning under non-stationary conditions. The paper concludes by outlining a conceptual framework for explainable, distributed energy intelligence and identifying research opportunities to build resilient, transparent smart energy ecosystems. Full article