Search Results (11,371)

From September 8th to 12th, 2024, the 19th SDEWES Conference on Sustainable Development of Energy, Water, and Environment Systems was successfully held in Rome. This event drew 700 researchers, scientists, and practitioners from 62 nations across six continents, with 570 participating in person and another 130 joining virtually. A total of seven papers were selected to be published in Energies, and the corresponding literature published in the most recent year is here reviewed. The main topics of the selected papers regard the adoption of district heating and cooling and their integration with renewable energies (such as geothermal or solar, the use of innovative bifacial PV panels, the use of biomass energy for the bio-synthetic natural gas production, the short-term electric load forecasting for industrial applications, and others. The reviewed papers show that several energy measures can be addressed to reach the decarbonization goals of 2050 and that the scientific community continues to find novel, sustainable, and efficient methods for the reduction in energy consumption and related CO₂ emissions. Full article

With the expansion of vehicle-to-everything (V2X) networks and the rising demand for intelligent services, vehicle edge computing encounters heightened requirements for more efficient task offloading. This study proposes a task offloading technique that utilizes federated collaboration and multi-agent deep reinforcement learning to reduce system latency and energy consumption. The task offloading issue is formulated as a Markov decision process (MDP), and a framework utilizing the Multi-Agent Dueling Double Deep Q-Network (MAD3QN) is developed to facilitate agents in making optimal offloading decisions inside intricate environments. Secondly, Federated Learning (FL) is implemented during the training phase, leveraging local training outcomes from many vehicles to enhance the global model, thus augmenting the learning efficiency of the agents. Experimental results indicate that, compared to conventional baseline algorithms, the proposed method decreases latency and energy consumption by at least 10% and 9%, respectively, while enhancing the average reward by at least 21%. Full article

This work presents a control strategy designed to reduce the energy consumption of direct current motors by implementing smooth motion trajectories in a point-to-point control system, utilizing a fuzzy logic controller based on the Tsukamoto inference method. The proposed controller’s energy performance was experimentally compared to that of a conventional PID controller, considering three motion profiles: parabolic, trapezoidal, and S-curve. The results demonstrate that the combination of the fuzzy controller with smooth trajectories effectively reduces energy consumption without compromising motion accuracy. Under no-load conditions, average energy savings of 11.77% for the parabolic profile, 9.27% for the trapezoidal profile, and 3.45% for the S-curve profile were achieved. This improvement remained consistent even when a load was introduced to the system. To validate these findings, the coefficient of variation was calculated, revealing lower dispersion in the fuzzy controller’s results, indicating greater consistency in energy efficiency. Furthermore, Welch’s t-tests were conducted for each profile and load condition, with all p-values falling below the 0.05 significance threshold, confirming the statistical relevance of the observed differences. Full article

This paper explores the feasibility and implications of developing a privacy-preserving, data-driven cloud service for predicting the energy consumption of industrial robots. Using machine learning, we evaluated three neural network architectures—dense, LSTM, and convolutional–LSTM hybrids—to model energy usage based on robot trajectory data. Our results show that models incorporating manually engineered features (angles, velocities, and accelerations) significantly improve prediction accuracy. To ensure secure collaboration in industrial environments where data confidentiality is critical, we integrate privacy-preserving machine learning (ppML) techniques based on secure multi-party computation (SMPC). This allows energy inference to be performed without exposing proprietary model weights or confidential input trajectories. We analyze the performance impact of SMPC on different network types and evaluate two optimization strategies, using public model weights through permutation and evaluating activation functions in plaintext, to reduce inference overhead. The results highlight that network architecture plays a larger role in encrypted inference efficiency than feature dimensionality, with dense networks being the most SMPC-efficient. In addition to model development, we identify and discuss specific stages in the MLOps workflow—particularly model serving and monitoring—that require adaptation to support ppML. These insights are useful for integrating ppML into modern machine learning pipelines. Full article

Regarding the impact of hull roughness on ship resistance and propulsive performance, most existing studies rely heavily on numerical hulls or simplified models, while systematic analysis focusing on the heterogeneous roughness of actual ships remains insufficient. Taking the 2433 TEU container ship SITC CAGAYAN as the research object, this study adopts a method that combines CFD numerical simulation with actual ship operation data. It employs a resistance prediction model based on the “roughness influence factor” to explore the mechanism by which local roughness affects ship resistance. Meanwhile, this study innovatively proposes the index of “fuel consumption increment per unit wetted surface area” and the concept of “fuel consumption factor,” thereby realizing the quantitative characterization of the impact of local rough areas on fuel consumption. The purpose of this study is to provide theoretical support and technical pathways for the optimization of ship energy efficiency and the development of green shipping. Full article

The rapid increase in global warming requires that sustainable energy choices aimed at achieving net-zero greenhouse gas emissions be implemented as soon as possible. This objective, emerging from the European Green Deal and the UN Climate Action, could be achieved by using clean and efficient energy sources such as hydrogen produced from nuclear power. “Renewable” hydrogen plays a fundamental role in decarbonizing both the energy-intensive industrial and transport sectors while addressing the global increase in energy consumption. In recent years, several strategies for hydrogen production have been proposed; however, nuclear energy seems to be the most promising for applications that could go beyond the sole production of electricity. In particular, nuclear advanced reactors that operate at very high temperatures (VHTR) and are characterized by coolant outlet temperatures ranging between 550 and 1000 °C seem the most suitable for this purpose. This paper describes the potential use of nuclear energy in coordinated and coupled configurations to support clean hydrogen production. Operating conditions, energy requirements, and thermodynamic performance are described. Moreover, gaps that require additional technology and regulatory developments are outlined. The intermediate heat exchanger, which is the key component for the integration of nuclear hybrid energy systems, was studied by varying the thermal power to determine physical parameters needed for the feasibility study. The latter, consisting of the comparative cost evaluation of some nuclear hydrogen production methods, was carried out using the HEEP code developed by the IAEA. Preliminary results are presented and discussed. Full article

Deep reinforcement learning (DRL)-based HVAC control has shown clear advantages over rule-based and model predictive methods. However, most prior studies remain limited to HVAC-only optimization or simple coordination with operable windows. Such approaches do not adequately address buildings with fixed glazing systems—a common feature in high-rise offices—where the lack of operable windows restricts adaptive envelope interaction. To address this gap, this study proposes a multi-zone control framework that integrates HVAC systems with electrochromic windows (ECWs). The framework leverages the Q-value Mixing (QMIX) algorithm to dynamically coordinate ECW transmittance with HVAC setpoints, aiming to enhance energy efficiency and thermal comfort, particularly in high-consumption buildings such as offices. Its performance is evaluated using EnergyPlus simulations. The results show that the proposed approach reduces HVAC energy use by 19.8% compared to the DQN-based HVAC-only control and by 40.28% relative to conventional rule-based control (RBC). In comparison with leading multi-agent deep reinforcement learning (MADRL) algorithms, including MADQN, VDN, and MAPPO, the framework reduces HVAC energy consumption by 1–5% and maintains a thermal comfort violation rate (TCVR) of less than 1% with an average temperature variation of 0.35 ${}^{\circ }\mathrm{C}$ Moreover, the model demonstrates strong generalizability, achieving 16.58–58.12% energy savings across six distinct climatic regions—ranging from tropical (Singapore) to temperate (Beijing)—with up to 48.2% savings observed in Chengdu. Our framework indicates the potential of coordinating HVAC systems with ECWs in simulation, while also identifying limitations that need to be addressed for real-world deployment. Full article

One of the nanoscale technologies that shows its capability of implementing integrated digital circuits with low power, high speed, and high density is quantum-dot cellular automata (QCA). The fundamental device for designing and implementing circuits in QCA is majority logic. In this paper, a novel energy-efficient QCA design of three-input AND/OR logic functions is proposed. This design can perform both AND and OR logic operations using the same structure with an achievement of 58% and 64% approximate reductions in power consumption compared to majority-based structures, and 31% and 32% approximate reductions in power consumption compared to the best available circuits, respectively. In addition, other physical constraints such as area and latency are improved and have better or similar results compared to the best existing circuits. The proposed circuit can be considered as a fundamental and better alternative to the majority gate for energy-efficient circuit design in QCA. This will pave the way for developing efficient large-scale QCA-based sequential and combinational circuits. Full article

The strategic development of the low-altitude economy requires efficient urban logistics solutions. The existing Unmanned Aerial Vehicle (UAV) truck delivery system faces severe challenges in dealing with dynamic weather constraints and multi-agent coordination. This article proposes a neural network-based optimisation framework that integrates constrained K-means clustering and a three-stage neural architecture. In this work, a mathematical model for heterogeneous vehicle constraints considering time windows and UAV energy consumption is developed, and it is validated through reference to the Solomon benchmark’s arithmetic examples. Experimental results show that the Truck–Drone Cooperative Traveling Salesman Problem (TDCTSP) model reduces the cost by 21.3% and the delivery time variance by 18.7% compared to the truck-only solution (Truck Traveling Salesman Problem (TTSP)). Improved neural network (INN) algorithms are also superior to the traditional genetic algorithm (GA) and Adaptive Large Neighborhood Search (ALNS) methods in terms of the quality of computed solutions. This research provides an adaptive solution for intelligent low-altitude logistics, which provides a theoretical basis and practical tools for the development of urban air traffic under environmental uncertainty. Full article

Porous media gas bearings utilize gas as a lubricating medium to achieve non-contact support technology. Compared with traditional liquid-lubricated bearings or rolling bearings, they are more efficient and environmentally friendly. With the uniform gas film pressure of gas bearings, the rotating shaft can achieve mechanical motion with low friction, high rotational speed, and long service life. They have significant potential in improving energy efficiency and reducing carbon emissions, enabling oil-free lubrication. By eliminating the friction losses of traditional oil-lubricated bearings, porous media gas bearings can reduce the energy consumption of industrial rotating machinery by 15–25%, directly reducing fossil energy consumption, which is of great significance for promoting carbon neutrality goals. They have excellent prospects for future applications in the civil and military aviation fields. Based on the three-dimensional flow characteristics of the bearing’s fluid domain, this paper considers the influences of the transient flow field in the variable fluid domain of the gas film and the radial pressure gradient of the gas film, establishes a theoretical model and a three-dimensional simulation model for porous media gas bearings, and studies the static–dynamic pressure coupling mechanism of porous media gas bearings. Furthermore, through the trial production of bearings and performance tests, the static characteristics are verified, and the steady-state characteristics are studied through simulation, providing a basis for the application of gas bearings made from porous media materials in the civil and military aviation fields. Full article

The development and implementation of scientifically substantiated solutions for the improvement and modernization of electromechanical devices, systems, and complexes, including electric drives, is an urgent theoretical and applied task for energetics, industry, transport, and other key areas, both in global and national contexts. The aim of this paper is to identify a rational model of an induction motor that balances computational simplicity and control system performance based on predictive approaches while ensuring maximum energy efficiency and reference tracking during the operation in dynamic modes. Five main mathematical models of an induction machine with different levels of detail have been selected. Three predictive control models have been implemented using GRAMPC (v 2.2), Matlab MPC Toolbox (v 24.1), and fmincon (R2024a) (from Matlab Optimization Toolbox). It has been established that in the dynamic mode of operation, the equivalent induction motor circuit with parameters $R_{fe} =const$, $L_{\mu }=f\left(I_{1d}\right)$, and $T_F=f\left({\omega }_{Rm}\right)$ is the most appropriate in terms of the following criteria: accuracy of control action generation, computation speed, and calculation of energy consumption. Full article

Food safety management has evolved with the Hazard Analysis and Critical Control Point (HACCP) system serving as a global benchmark. However, conventional HACCP does not explicitly address environmental sustainability, leading to challenges such as excessive water use, chemical discharge, and energy inefficiency. Green HACCP extends traditional HACCP by integrating Environmental Respect Practices (ERPs) to fill this critical gap between food safety and sustainability. This study is presented as a conceptual paper based on a structured literature review combined with illustrative industry applications. It analyzes the principles, implementation challenges, and economic viability of Green HACCP, contrasting it with conventional systems. Evidence from recent reports and industry examples shows measurable benefits: water consumption reductions of 20–25%, energy savings of 10–15%, and improved compliance readiness through digital monitoring technologies. It explores how digital technologies—IoT for real-time monitoring, AI for predictive optimization, and blockchain for traceability—enhance efficiency and sustainability. By aligning HACCP with sustainability goals and the United Nations Sustainable Development Goals (SDGs), this paper provides academic contributions including a clarified conceptual framework, quantified advantages, and policy recommendations to support the integration of Green HACCP into global food safety systems. Industry applications from dairy, seafood, and bakery sectors illustrate practical benefits, including waste reduction and improved compliance. This study concludes with policy recommendations to integrate Green HACCP into global food safety frameworks, supporting broader sustainability goals. Overall, Green HACCP demonstrates a cost-effective, scalable, and environmentally responsible model for future food production. Full article

This study presents and evaluates three sampling strategies to characterize electricity demand in regions of Colombia with limited metering infrastructure. These areas lack Advanced Metering Infrastructure (AMI), relying instead on traditional monthly consumption records. The objective of the research is to obtain user samples that are representative of the original population and logistically efficient, in order to support energy planning and decision-making. The analysis draws on five years of historical data from 2020 to 2024. It includes monthly energy consumption, geographic coordinates, customer classification, and population type, covering over 500,000 users across four subregions of operation determined by the region grid operator: North, South, Center, and East. The proposed methodologies are based on Shannon entropy, consumption-based probabilistic sampling, and Kullback–Leibler divergence minimization. Each method is assessed for its ability to capture demand variability, ensure representativeness, and optimize field deployment. Representativeness is evaluated by comparing the differences in class proportions between the sample and the original population, complemented by the Pearson correlation coefficient between their distributions. Results indicate that entropy-based sampling excels in logistical simplicity and preserves categorical diversity, while KL divergence offers the best statistical fit to population characteristics. The findings demonstrate how combining information theory and statistical optimization enables flexible, scalable sampling solutions for demand characterization in under-instrumented electricity grids. Full article

Modern energy grids, with their regional diversity and complex consumption patterns, require accurate short-term forecasting for operational efficiency and reliability. This study introduces a Stacking Ensemble Forecasting (SEF) framework for multi-region household energy demand, utilizing an optimized stacking ensemble model tuned via Bayesian Optimization to achieve superior predictive accuracy. The framework significantly improved accuracy across Diyarbakır, Istanbul, and Odemis, with a final model demonstrating up to 16.47% RMSE reduction compared to the best baseline models. The final model’s real-world performance was validated through a Simulated Digital Twin (SDT) environment, where scenario-based testing demonstrated its robustness against behavioral changes, data quality issues, and device failures. The proposed SEF-SDT framework offers a generalizable solution for managing diverse regions and consumption profiles, contributing to efficient and sustainable energy management. Full article

Due to increasing demand for the exploration of marine resources, underwater optical wireless sensor networks (UOWSNs) have emerged as a promising solution by offering higher bandwidth and lower latency compared to traditional underwater acoustic wireless sensor networks (UAWSNs), with their existing routing protocols facing challenges in energy consumption and packet forwarding. To address these challenges, this paper proposes an energy-partitioned routing protocol based on an advancement function (EPAR) for UOWSNs. By dynamically classifying the nodes into high-energy and low-energy ones, the proposed EPAR algorithm employs an adaptive weighting strategy to prioritize the high-energy nodes in relay selection, thereby balancing network load and extending overall lifetime. In addition, a tunable advancement function is adopted by the proposed EPAR algorithm by comprehensively considering the Euclidean distance and steering angle toward the sink node. By adjusting a tunable parameter α, the function guides forwarding decisions to ensure energy-efficient and directionally optimal routing. Additionally, by employing a hop-by-hop neighbor discovery mechanism, the proposed algorithm enables each node to dynamically update its local neighbor set, thereby improving relay selection and mitigating the impact of void regions on the packet delivery ratio (PDR). Simulation results demonstrate that EPAR can obtain up to about a 10% improvement in PDR and up to about a 30% reduction in energy depletion, with a prolonged network lifetime when compared to the typical algorithms adopted in the simulations. Full article