Search Results (1,002)

The smart home contributions in energy management systems can help the microgrid operator overcome technical problems and ensure economically viable operation by flattening the load profile. The purpose of this paper is to propose a smart home energy management system (SHEMS) that enables smart homes to monitor, store, and manage energy efficiently. SHEMS relies heavily on energy storage systems (ESSs) and electric vehicles (EVs), which enable smart homes to be more flexible and enhance the reliability and efficiency of renewable energy sources. It is vital to study the optimal operation of batteries in SHEMS; hence, a multi-objective optimization approach for SHEMS and demand response programs is proposed to simultaneously reduce the daily bills, the peak-to-average ratio, and the number of battery discharging cycles of ESSs and EVs. An inverter-based air conditioner, photovoltaic system, ESS, and EV, shiftable and non-shiftable equipment are considered in the suggested smart home. In addition, the amount of energy purchased and sold throughout the day is taken into account in the suggested mathematical formulation based on the real-time market pricing. The suggested multi-objective problem is solved by an improved gray wolf optimizer, and various weather conditions, including rainy, sunny, and cloudy days, are also analyzed. Additionally, simulations indicate that the proposed method achieves optimal results, with three objectives shown on the Pareto front of the optimal solutions. Full article

At the international level, new measures, policies, and technologies are being developed to reduce greenhouse gas emissions and, more broadly, air pollutants. Road transportation is one of the main contributors to such emissions, as vehicles are extensively used in logistics operations, and many fleet owners of fossil-fueled trucks are adopting new technologies such as electric, hybrid, and hydrogen-based vehicles. This paper addresses the Hybrid Fleet Capacitated Vehicle Routing Problem with Time Windows (HF-CVRPTW), with the objectives of minimizing costs and mitigating environmental impacts. A mixed-integer linear programming model is developed, incorporating split deliveries, scheduled arrival times at stores, and a carbon cap-and-trade mechanism. The model is tested on a real case study provided by Decathlon, evaluating the performance of internal combustion engine (ICE), electric (EV), and hydrogen fuel cell (HV) vehicles. Results show that when considering economic and emission trading costs, the optimal fleet deployment priority is to use ICE vehicles first, followed by EVs and then HVs, but considering only total emissions, the result is the reverse. Further analysis explores the conditions under which alternative fuel, electricity, or hydrogen prices can achieve competitiveness, and a further analysis investigates the impact of different electricity generation and hydrogen production pathways on overall indirect emissions. Full article

The smart grid (SG) plays a seminal role in the modern energy landscape by integrating digital technologies, the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) to enable bidirectional energy flow, real-time monitoring, and enhanced operational efficiency. However, these advancements also introduce critical challenges related to data privacy, cybersecurity, and operational balance. This review critically evaluates SG systems, beginning with an analysis of data privacy vulnerabilities, including Man-in-the-Middle (MITM), Denial-of-Service (DoS), and replay attacks, as well as insider threats, exemplified by incidents such as the 2023 Hydro-Québec cyberattack and the 2024 blackout in Spain. The review further details the SG architecture and its key components, including smart meters (SMs), control centers (CCs), aggregators, smart appliances, and renewable energy sources (RESs), while emphasizing essential security requirements such as confidentiality, integrity, availability, secure storage, and scalability. Various privacy preservation techniques are discussed, including cryptographic tools like Homomorphic Encryption, Zero-Knowledge Proofs, and Secure Multiparty Computation, anonymization and aggregation methods such as differential privacy and k-Anonymity, as well as blockchain-based approaches and machine learning solutions. Additionally, the review examines pricing models and their resolution strategies, Demand–Supply Balance Programs (DSBPs) utilizing optimization, game-theoretic, and AI-based approaches, and energy storage systems (ESSs) encompassing lead–acid, lithium-ion, sodium-sulfur, and sodium-ion batteries, highlighting their respective advantages and limitations. By synthesizing these findings, the review identifies existing research gaps and provides guidance for future studies aimed at advancing secure, efficient, and sustainable smart grid implementations. Full article

Given the strong coupling between electricity flow and carbon flow, promoting the low-carbon transformation of the energy sector is a crucial measure to actively responding to climate challenges. As a pivotal hub linking the electricity market with the carbon market, promoting electricity–carbon coordinated scheduling of Virtual Power Plants (VPPs) is of great significance in expediting the energy transition process. Based on the introduction of carbon potential, this manuscript constructs a VPP electricity–carbon coordinated scheduling model that incorporates various typical elements, including renewable energy units and demand response. Furthermore, this paper utilizes Brain Storm Optimization (BSO) to improve the Snow Ablation Optimizer (SAO) algorithm and applies the improved algorithm to solve the model developed in this manuscript. Finally, an analysis was conducted using a small-scale VPP project in eastern China, and the results are the following: Firstly, the SAO improved by BSO demonstrates a significant enhancement in solution efficiency. In particular, for the cases presented in this manuscript, the algorithm’s convergence speed increased by 42.85%. Secondly, under the multi-market conditions and with real-time carbon potential, VPPs will possess greater flexibility in scheduling optimization and stronger incentives to fully explore their emission reduction potential through collaborative electricity–carbon scheduling, thereby improving both economic and environmental performance. However, constrained by factors such as the currently low carbon price level, the extent of improvement in VPPs’ performance under real-time carbon potential, compared to fixed carbon potential, remains relatively limited, with a 1.07% increase in economic benefits and a 2.63% reduction in carbon emissions. Thirdly, an increase in carbon prices can incentivize VPPs to continuously tap into their emission reduction potential, but beyond a certain threshold (120 CNY/t in this case study), the marginal contribution of further carbon price increases to emission reductions will progressively decline. Specifically, for every 20-yuan increase in the carbon price, the carbon emission reduction rate of VPPs drops below 1%. Full article

Efficient and cost-effective biomass logistics remain a significant challenge due to the dynamic and nonlinear nature of supply chains, as well as the scarcity of comprehensive data on this topic. As biomass plays an increasingly important role in sustainable energy systems, managing its complex supply chains efficiently is crucial. Traditional logistics methods often struggle with the dynamic, nonlinear, and data-scarce nature of biomass supply, especially when integrating local and international sources. To address these challenges, this study aims to develop an innovative modular artificial neural network (ANN)-based Biomass Delivery Management (BDM) model to optimize biomass procurement and supply for a fluidized bed combined heat and power (CHP) plant. The comprehensive model integrates technical, economic, and geographic parameters to enable supplier selection, optimize transport routes, and inform fuel blending strategies, representing a novel approach in biomass logistics. A case study based on operational data confirmed the model’s ability to identify cost-effective and quality-compliant biomass sources. Evaluated using empirical operational data from a Polish CHP plant, the ANN-based model demonstrated high predictive accuracy (MAE = 0.16, MSE = 0.02, R² = 0.99) within the studied scope. The model effectively handled incomplete datasets typical of biomass markets, aiding in supplier selection decisions and representing a proof-of-concept for optimizing Central European biomass logistics. The model was capable of generalizing supplier recommendations based on input variables, including biomass type, unit price, and annual demand. The proposed framework supports both strategic and real-time logistics decisions, providing a robust tool for enhancing supply chain transparency, cost efficiency, and resilience in the renewable energy sector. Future research will focus on extending the dataset and developing hybrid models to strengthen supply chain stability and adaptability under varying market and regulatory conditions. Full article

The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurrency market forecasting, leveraging distributed systems to enable low-latency prediction models. Our approach integrates machine learning algorithms across a distributed network: edge nodes perform real-time data preprocessing and feature extraction, while the cloud infrastructure handles deep learning model training and global pattern recognition. The proposed architecture uses a three-tier system comprising edge nodes for immediate data capture, fog layers for intermediate processing and local inference, and cloud servers for comprehensive model training on historical blockchain data. A federated learning mechanism allows edge nodes to contribute to a global prediction model while preserving data locality and reducing network latency. The experimental results show a 40% reduction in prediction latency compared to cloud-only solutions while maintaining comparable accuracy in forecasting Bitcoin and Ethereum price movements. The system processes over 10,000 transactions per second and delivers real-time insights with sub-second response times. Integration with blockchain ensures data integrity and provides transparent audit trails for all predictions. Full article

Iris recognition is currently considered the most promising biometric method and has been applied in many fields. Current commercial and research systems typically use software solutions running on a dedicated computer, whose power consumption, size and price are considerably high. This paper presents a hardware-based embedded solution for real-time iris segmentation. From an algorithmic point of view, the system consists of two steps. The first employs a YOLOX trained to detect two classes: eyes and iris/pupil. Both classes intersect in the last of the classes and this is used to emphasise the detection of the iris/pupil class. The second stage uses a lightweight U-Net network to segment the iris, which is applied only on the locations provided by the first stage. Designed to work in an Iris At A Distance (IAAD) scenario, the system includes quality parameters to discard low-contrast or low-sharpness detections. The whole system has been integrated on one MultiProcessor System-on-Chip (MPSoC) using AMD’s Deep learning Processing Unit (DPU). This approach is capable of processing the more than 45 frames per second provided by a 16 Mpx CMOS digital image sensor. Experiments to determine the accuracy of the proposed system in terms of iris segmentation are performed on several publicly available databases with satisfactory results. Full article

Facilities with high energy demands, such as swimming pools, face escalating costs in electricity and heating, exacerbated by economic instability and fluctuating energy prices. These facilities are often overdesigned to meet extreme peak demands, resulting in higher than necessary energy usage. Therefore, to reduce costs, diversification of heat sources and tailoring their efficiency to meet real-time needs is required. This study analyzes a swimming pool complex in Poland with a sports pool, a recreational pool, an outdoor pool, and a spa bath, comparing the initial design assumptions for the use of heat and electricity with actual consumption data. By incorporating a mix of energy sources, including cogeneration (combined heat and power), gas boilers, district heating, heat pumps, and photovoltaic panels, the system can flexibly adjust to market energy prices. An automated monitoring system continuously monitors energy use, identifies deviations, and helps pinpoint errors, allowing more precise and economical energy management. Detailed reports generated from meter readings enable comparisons with previous usage periods and guide future planning. A balance of energy production with consumption, adjustment of production to match demand, and configuration of equipment operation with defined parameters all contribute to an effective and cost-effective approach to facility energy management. Full article

Soaring housing prices worldwide are compromising housing affordability, potentially leading to significant economic, social, and health repercussions. Understanding the price discovery process within the real estate market is therefore crucial for policymakers. While the relationship between land and housing prices in urban residential markets has been widely examined in the literature, the results are often context-specific, leaving the question of whether the land market leads the housing market or vice versa open to debate. Saudi Arabia, with its rapidly growing real estate market, evolving demographics and urbanization trends, and transformative regulatory reforms, presents a compelling context for revisiting the land–housing nexus. This study examines the long-term relationship between land and housing markets and investigates the short-term price dynamics with the ultimate goal of understanding the price formation in the housing market. The study dataset comprises quarterly time-series price indices published by the General Authority for Statistics (GASTAT) in Saudi, representing the nation-wide price movements of residential lands and villas from 2014Q1 to 2025Q1. The study employs the Johansen cointegration method and the Granger causality testing. The results of cointegration analysis confirm a significant long-run equilibrium relationship between the two markets, while the error correction model reveals that both land and housing prices adjust to restore this equilibrium. Granger causality test results show a unidirectional relationship, where land prices predict future housing prices, consistent with the neoclassical rent theory. These findings reinforce the long-term, intrinsic link between land and housing markets observed in prior studies. The dynamics in the Saudi market are likely shaped by rapid urbanization that intensified speculation in the land market, and also the prevalence of self-building enabled by government-supported financing. This study underscores the importance of striking a delicate balance between supply and demand side policies in the real estate market while monitoring the impact of these policies on housing affordability. Full article

For effective logistics planning and pricing strategies, it is essential to predict road freight transportation costs accurately. Using a real-world dataset with 45,569 freight offers and 52 different variables, including financial, logistical, geographical, and temporal characteristics, this study presents a data-driven method for forecasting transport prices. To create a strong predictive model, the approach combines hyperparameter optimization, evolutionary feature selection, and extensive feature engineering. Because gradient boosting works well for modelling intricate, nonlinear relationships, it was used as the main algorithm. Temporal dependencies were maintained through a nested cross-validation framework with a time-series split, which improved the generalizability of the model. With a mean absolute percentage error (MAPE) of 6.27%, the model showed excellent predictive accuracy. Key predictive factors included total transport distance, load and delivery quantities, temperature constraints, and aggregated categorical features such as route and vehicle type. The results confirm that evolutionary algorithms are capable of efficiently optimizing model parameters, as well as feature subsets, greatly enhancing interpretability and performance. In the freight logistics industry, this method offers useful insights for operational and dynamic pricing decision-making. This model may be expanded in future research to include external data sources and investigate its suitability for use in various geographic locations and modes of transportation. Full article

Electric arc furnace technology is a key factor in the sustainable transformation of the iron and steel industry. This study compares two discrete-time multi-objective optimization models—integer and mixed-integer linear programming—that integrate unit commitment with economic and environmental dispatch. After evaluating both approaches, the integer linear programming model is used, due to its reasonable calculation time, to assess demand-side management potentials under real-world processes and day-ahead market conditions. The model is applied to various scenarios with differing energy price dynamics, CO₂ pricing, EAF utilization levels, and weighting of the objective functions. Results indicate cost savings of up to 6.95% and CO₂ emission reductions of up to 10.86%, though these are subject to a non-linear trade-off between economic and environmental goals. Due to process constraints and market structures, EAFs’ flexibility in energy carrier use (switch between electricity and natural gas) is limited to 3.07%. Additionally, lower furnace utilization does not necessarily increase flexibility, as downstream process requirements restrict scheduling options. The study underscores the importance of green electrification, with up to 36% CO₂ savings when using 100% renewable electricity. Overall, unlocking industrial flexibility requires technical solutions, supportive market incentives, and regulatory frameworks for effective industrial decarbonization. Full article

With the development of electricity spot markets, the integrated renewable-storage plant (IRSP) has emerged as a crucial entity in real-time energy markets due to its flexible regulation capability. However, traditional methods face computational inefficiency in high-dimensional bidding scenarios caused by expansive decision spaces, limiting online generation of multi-segment optimal quotation curves. This paper proposes a policy migration-based optimization framework for high-dimensional IRSP bidding: First, a real-time market clearing model with IRSP participation and an operational constraint-integrated bidding model are established. Second, we rigorously prove the monotonic mapping relationship between the cleared output and the real-time locational marginal price (LMP) under the market clearing condition and establish mathematical foundations for migrating the self-dispatch policy to the quotation curve based on value function concavity theory. Finally, a generalized inverse construction method is proposed to decompose the high-dimensional quotation curve optimization into optimal power response subproblems within price parameter space, substantially reducing decision space dimensionality. The case study validates the framework effectiveness through performance evaluation of policy migration for a wind-dual energy storage plant, demonstrating that the proposed method achieves 90% of the ideal revenue with a 5% prediction error and enables reinforcement learning algorithms to increase their performance from 65.1% to 84.2% of the optimal revenue. The research provides theoretical support for resolving the “dimensionality–efficiency–revenue” dilemma in high-dimensional bidding and expands policy possibilities for IRSP participation in real-time markets. Full article

Accurate and efficient cryptocurrency price prediction is vital for investors in the volatile crypto market. This study comprehensively evaluates nine models—including baseline, zero-shot, and deep learning architectures—on 21 major cryptocurrencies using daily and hourly data. Our multi-dimensional evaluation assesses models based on prediction accuracy (MAE, RMSE, MAPE), speed, statistical significance (Diebold–Mariano test), and economic value (Sharpe Ratio). Our research found that the optimally fine-tuned TimeGPT model (without variables) demonstrated superior performance across both Daily and Hourly datasets, with its statistical leadership confirmed by the Diebold–Mariano test. Fine-tuned Chronos excelled in daily predictions, while TFT was a close second to TimeGPT for hourly forecasts. Crucially, zero-shot models like TimeGPT and Chronos were tens of times faster than traditional deep learning models, offering high accuracy with superior computational efficiency. A key finding from our economic analysis is that a model’s effectiveness is highly dependent on market characteristics. For instance, TimeGPT with variables showed exceptional profitability in the volatile ETH market, whereas the zero-shot Chronos model was the top performer for the cyclical BTC market. This also highlights that variables have asset-specific effects with TimeGPT: improving predictions for ICP, LTC, OP, and DOT, but hindering UNI, ATOM, BCH, and ARB. Recognizing that prior research has overemphasized prediction accuracy, this study provides a more holistic and practical standard for model evaluation by integrating speed, statistical significance, and economic value. Our findings collectively underscore TimeGPT’s immense potential as a leading solution for cryptocurrency forecasting, offering a top-tier balance of accuracy and efficiency. This multi-dimensional approach provides critical, theoretical, and practical guidance for investment decisions and risk management, proving especially valuable in real-time trading scenarios. Full article

Air pollution is an escalating global challenge with profound implications for agricultural production and food security. This review explores the impacts of deteriorating air quality on global crop yields and food security, emphasizing both direct physiological effects on plants and broader environmental interactions. Key pollutants such as ground-level ozone (O₃), fine particulate matter (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs) reduce crop yield and quality. They have been shown to inhibit plant growth, potentially by affecting germination, morphology, photosynthesis, and enzyme activity. PAH contamination, for example, can negatively affect soil microbial communities essential for soil health, nutrient cycling and organic matter decomposition. They persist and accumulate in food products through the food chain, raising concerns about food safety. The review synthesizes evidence demonstrating how air pollution undermines the four pillars of food security: availability, access, utilization, and stability by reducing crop yields, elevating food prices, and compromising nutritional quality. The consequences are disproportionately severe in low- and middle-income countries, where regulatory and infrastructural limitations exacerbate vulnerability. This study examines mitigation strategies, including emission control technologies, green infrastructure, and precision agriculture, while stressing the importance of community-level interventions and real-time air quality monitoring through IoT and satellite systems. Integrated policy responses are urgently needed to bridge the gap between environmental regulation and agricultural sustainability. Notably, international cooperation and targeted investments in multidisciplinary research are essential to develop pollution-resilient crop systems and inform adaptive policy frameworks. This review identifies critical knowledge gaps regarding pollutant interactions under field conditions and calls for long-term, region-specific studies to assess cumulative impacts. Ultimately, addressing air pollution is not only vital for ecosystem health, but also for achieving global food security and sustainable development in a rapidly changing environment. Full article

As hybrid microgrids become increasingly widespread in real-world applications, the need for intelligent energy management strategies that ensure reliability, economic efficiency, and robustness to uncertainties is growing. This study presents a real-time capable model predictive control (MPC)-based energy management for a medium-sized hybrid microgrid at the Karabuk University Demir Çelik campus. The system comprises 100 kW photovoltaic (PV) panels, a 500 Ah battery energy storage system (BESS), a 440 kW diesel generator, and a 75 MVA utility connection. The proposed MPC approach is evaluated under ten realistic operating scenarios, incorporating dynamic pricing and fault conditions. Simulation results show up to 43% reduction in operational costs and 35% decrease in grid dependency, while keeping unserved critical loads below 3%. Compared to conventional rule-based methods, the proposed strategy offers improved scalability, adaptability, and resilience, highlighting its practical potential for deployment in smart energy systems. Full article