Search Results (729)

The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial step toward achieving energy efficiency and carbon neutrality. However, ensuring real-time optimization, interoperability, and sustainability across these distributed energy resources (DERs) remains a key challenge. This paper presents a comprehensive review of artificial intelligence (AI) applications for sustainable energy management and low-carbon technology integration in smart grids and smart homes. The review explores how AI-driven techniques include machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization (PSO), whale optimization algorithm (WOA), and cuckoo optimization algorithm (COA) enhance forecasting, adaptive scheduling, and real-time energy optimization. These techniques have shown significant potential in improving demand-side management, dynamic load balancing, and renewable energy utilization efficiency. Moreover, AI-based home energy management systems (HEMSs) enable predictive control and seamless coordination between grid operations and distributed generation. This review also discusses current barriers, including data heterogeneity, computational overhead, and the lack of standardized integration frameworks. Future directions highlight the need for lightweight, scalable, and explainable AI models that support decentralized decision-making in cyber-physical energy systems. Overall, this paper emphasizes the transformative role of AI in enabling sustainable, flexible, and intelligent power management across smart residential and grid-level systems, supporting global energy transition goals and contributing to the realization of carbon-neutral communities. Full article

Conventional sensing expends energy at three stages: powering dedicated sensors, transmitting measurements, and executing computationally intensive inference. Wireless sensing re-purposes WiFi channel state information (CSI) inherent in every packet, eliminating extra sensors and uplink traffic, though reliance on deep neural networks (DNNs) often trained and run on graphics processing units (GPUs) can negate these gains. This review highlights two core energy efficiency levers in CSI-based wireless sensing. First ambient CSI harvesting cuts power use by an order of magnitude compared to radar and active Internet of Things (IoT) sensors. Second, integrated sensing and communication (ISAC) embeds sensing functionality into existing WiFi links, thereby reducing device count, battery waste, and carbon impact. We review conventional handcrafted and accuracy-first methods to set the stage for surveying green learning strategies and lightweight learning techniques, including compact hybrid neural architectures, pruning, knowledge distillation, quantisation, and semi-supervised training that preserve accuracy while reducing model size and memory footprint. We also discuss hardware co-design from low-power microcontrollers to edge application-specific integrated circuits (ASICs) and WiFi firmware extensions that align computation with platform constraints. Finally, we identify open challenges in domain-robust compression, multi-antenna calibration, energy-proportionate model scaling, and standardised joules per inference metrics. Our aim is a practical battery-friendly wireless sensing stack ready for smart home and 6G era deployments. Full article

The exploitation of hydrokinetic resources represents a sustainable and efficient alternative for renewable energy generation. This study presents the design and real-world implementation of a compact hydrokinetic system capable of converting rainwater runoff into electricity within smart homes. Unlike conventional large-scale hydrokinetic technologies, this system was specifically engineered for intermittent, low-flow conditions typical of residential rainwater collection networks. The turbine was manufactured using 3D-printed biodegradable materials to promote environmental sustainability and facilitate rapid prototyping. Through CFD simulations and laboratory testing, the system’s hydraulic behaviour and energy conversion efficiency were validated across different flow scenarios. The complete system, consisting of four turbines rated at 120 W each, was integrated into a real smart home without structural modifications. From an academic perspective, this study contributes a quantitatively validated hybrid hydrokinetic–low-head framework for residential rainwater energy recovery, addressing intermittent and low-flow urban conditions insufficiently explored in existing literature. Field tests demonstrated that the hydrokinetic system provides complementary energy during rainfall events, generating up to 6000 Wh per day and enhancing household energy resilience, particularly during periods of low solar availability. The results confirm the technical feasibility, sustainability, and practical viability of decentralized hydrokinetic energy generation for residential applications. Full article

Green technologies are strongly present in the energy mixes of countries around the world. In addition to the need to reduce the extraction of non-renewable raw materials and the harmful environmental impact associated with energy production, the trend towards renewable energy development should also be linked to the need to minimize energy poverty stemming from high electricity prices and the need to increase the energy efficiency of existing solutions. These issues formed the basis for the study’s objective, which was to examine the regulatory framework for the development of Poland’s energy system, with particular emphasis on sustainable development. A particularly important aspect of the study was the exploration of the market for green technologies introduced into the energy system in Poland, with a primary focus on solutions dedicated to small, individual consumers (households). The cognitive value of the study and its original character is created by the cognitive aspect in terms of the interests and consumer preferences of households in this area, motivated by economic considerations related to the energy efficiency aspect of RES solutions. In this regard, there is a relatively limited number of current studies conducted for the reference country (Poland), justifying the choice of the research topic and theme. For the purposes of the study, a literature review, as well as legal standards and industry reports, was conducted. A practical study was conducted based on the results of surveys conducted by selected companies involved in the sale and installation of heating solutions. Detailed research was supported by statistical instruments using PQstat software version 1.8.4.164. Key findings confirm significant household interest in green electricity production technologies, which enable improved energy efficiency of home energy installations. Importantly, the potential for lower electricity bills, which can be attributed to low system maintenance costs and the ability to manage consumption, is a factor in choosing renewable energy solutions. Current interest in renewable energy solutions focuses on heat pumps, photovoltaics, and energy storage. Renewable energy users are interested in integrating renewable energy technology solutions into energy production and management to optimize energy consumption costs and increase household energy independence. Full article

A low-power IPv6 mesh standard, Thread, is gaining traction in smart-home, building-automation, and industrial IoT deployments. It extends mesh connectivity with the help of Router-Eligible End Devices (REEDs), which can be promoted to, or demoted from, the router status. Promotion and demotion hinge on two tunable parameters, the router upgrade and the router downgrade thresholds. Yet the OpenThread reference stack ships with fixed values (16/23) for these thresholds. This paper presents a systematic study of how these thresholds shape router-election dynamics across diverse traffic loads and network topologies. Leveraging an extended OpenThread Network Simulator, a sweep through both router upgrade and router downgrade thresholds with different gaps was performed. Results reveal that the default settings may over-provision routing capacity and may result in increased frame retransmissions, wasting airtime and reducing energy efficiency. Full article

This work presents the design, modeling, and experimental validation of an innovative, highly autonomous, and economically viable photovoltaic solar cooker, integrating a robust battery storage system. The system combines 1200 Wp photovoltaic panels, a control block with DC/DC power converters and digital control for intelligent energy management, and a thermally insulated heating plate equipped with two resistors. The objective of the system is to reduce dependence on conventional fuels while overcoming the limitations of existing solar cookers, particularly insufficient cooking temperatures, the need for continuous solar orientation, and significant thermal losses. The optimization of thermal insulation using a ceramic fiber and glass wool configuration significantly reduces heat losses and increases the thermal efficiency to 64%, nearly double that of the non-insulated case (34%). This improvement enables cooking temperatures of 100–122 °C, heating element surface temperatures of 185–464 °C, and fast cooking times ranging from 20 to 58 min, depending on the prepared dish. Thermal modeling takes into account sheet metal, strengths, and food. The experimental results show excellent agreement between simulation and measurements (deviation < 5%), and high converter efficiencies (84–97%). The integration of the batteries guarantees an autonomy of 6 to 12 days and a very low depth of discharge (1–3%), allowing continuous cooking even without direct solar radiation. Crucially, the techno-economic analysis confirmed the system’s strong market competitiveness. Despite an Initial Investment Cost (CAPEX) of USD 1141.2, the high performance and low operational expenditure lead to a highly favorable Return on Investment (ROI) of only 4.31 years. Compared to existing conventional and solar cookers, the developed system offers superior energy efficiency and optimized cooking times, and demonstrates rapid profitability. This makes it a sustainable, reliable, and energy-efficient home solution, representing a major technological leap for domestic cooking in rural areas. Full article

This paper addresses the critical challenge of multi-objective optimization in residential Home Energy Management Systems (HEMS) by proposing a novel framework based on an Improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. The study specifically targets the low convergence efficiency of Multi-Agent Deep Reinforcement Learning (MADRL) for coupled Battery Energy Storage System (BESS) and Air Source Heat Pump (ASHP) operation. The framework synergistically integrates an action constraint projection mechanism with an economic-performance-driven dynamic learning rate modulation strategy, thereby significantly enhancing learning stability. Simulation results demonstrate that the algorithm improves training convergence speed by 35–45% compared to standard MAPPO. Economically, it delivers a cumulative cost reduction of 15.77% against rule-based baselines, outperforming both Independent Proximal Policy Optimization (IPPO) and standard MAPPO benchmarks. Furthermore, the method maximizes renewable energy utilization, achieving nearly 100% photovoltaic self-consumption under favorable conditions while ensuring robustness in extreme scenarios. Temporal analysis reveals the agents’ capacity for anticipatory decision-making, effectively learning correlations among generation, pricing, and demand to achieve seamless seasonal adaptability. These findings validate the superior performance of the proposed centralized training architecture, providing a robust solution for complex residential energy management. Full article

This article reviews recent advances in microwave and radar techniques for non-contact measurement of human vital signs in dynamic conditions. The focus is on solutions that work when the subject is moving or performing everyday activities, rather than lying motionless in clinical settings. This review covers innovative biodegradable and flexible antenna designs for wearable devices operating in multiple frequency bands and supporting efficient 5G/IoT connectivity. Particular attention is paid to ultra-wideband (UWB) radar, Doppler sensors, and microwave reflectometry combined with advanced signal-processing and deep learning algorithms for robust estimation of respiration, heart rate, and other cardiopulmonary parameters in the presence of body motion. Applications in telemedicine, home monitoring, sports, and search and rescue are discussed, including localization of people trapped under rubble by detecting their vital sign signatures at a distance. This paper also highlights key challenges such as inter-subject anatomical variability, motion artifacts, hardware miniaturization, and energy efficiency, which still limit widespread deployment. Finally, related developments in microwave imaging and early detection of pathological tissue changes are briefly outlined, highlighting the shared components and processing methods. In general, microwave techniques show strong potential for unobtrusive, continuous, and environmentally sustainable monitoring of human physiological activity, supporting future healthcare and safety systems. Full article

Air pollution episodes caused by particulate matter (PM) persist in and around Kraków even after the city’s ban on solid fuels. We examine how household wealth and the ongoing replacement of old heat sources with modern, energy-efficient units affect these emissions. Years of hourly data from a network of low-cost sensors for neighboring municipalities are combined with the Poland building emissions register specifying the number and type of heating devices and municipal personal income tax records. Two distinct emission patterns emerge. Episodes of elevated concentrations near houses with old hand-loaded stoves follow pronounced behavioral cycles tied to residents return home hours and the nightly sleep cycle, whereas elsewhere the pattern is smoother—consistent with modern heating sources or with advection from dispersed upwind sources. Municipalities that recorded per capita income growth also showed declines in average PM concentrations, suggesting that rising incomes accelerate the transition to cleaner, more efficient heating. Our findings suggest that economic development is linked to the shift towards cleaner and more efficient energy, and that providing targeted support for low-income households should not be overlooked in completing the transition. Full article

Social housing plays a key role in the Colombian residential market, showing a growing commitment to sustainability: currently, a high percentage of EDGE-certified homes belong to this segment. However, in hot and humid areas such as Montería, most VIS homes have deficiencies in their thermal envelopes and poor roof insulation, which leads to a heavy reliance on air conditioning. This study addresses the lack of practical and replicable methodologies for improving energy efficiency in social housing located in hot–humid climates. The research aims to develop and apply a methodological framework that integrates architectural rehabilitation strategies with quantitative evaluation using the EDGE App tool. The proposed approach was implemented in Montería, Colombia, through a case study that combines diagnostic analysis of existing housing conditions, simulation of energy-saving measures, and assessment of environmental and economic performance. A real home in Montería was used as a reference, and more than 600 simulations were carried out considering different orientations and passive strategies. Through a Pareto analysis, the three most efficient measures were identified: natural ventilation, high-solar-reflectance roofing, and moderate reduction in the U-value. Together, these measures reduced energy consumption by up to 50%, with minimal increases in construction costs (≤1.2% of the commercial value). It was also found that excessive insulation can induce unwanted nighttime heating demands, highlighting the need for adjustments to the climatic context. The results confirm the technical and economic feasibility of mass rehabilitation of VIS in hot and humid climates using standard passive measure packages, consolidating the role of the EDGE App as a key tool for guiding sustainable design, investment, and environmental certification decisions. Full article

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments. Full article

The United Kingdom’s (UK) retrofit revolution is at a crossroads and the efficacy of retrofit interventions is not solely a function of insulation thickness. To truly slash emissions and lift households out of fuel poverty, we must solve the persistent problem of thermal bridging (TB), i.e., the hidden flaws that cause heat to escape, dampness to form, and well-intentioned retrofits to fail. This review moves beyond basic principles to spotlight the emerging tools and transformative strategies to make a difference. We explore the role of advanced modelling techniques, including finite element analysis (FEA), in pinpointing thermal and moisture-related risks, and how emerging materials like vacuum-insulated panels (VIPs) offer high-performance solutions in tight spaces. Crucially, we demonstrate how an integrated fabric-first approach, guided by standards like PAS 2035, is essential to manage moisture, ensure durability, and deliver the comfortable, low-energy homes the UK desperately needs. Therefore, achieving net-zero targets is critically dependent on the systematic upgrade of the building envelope, with the mitigation of TB representing a fundamental prerequisite. The EnerPHit approach applies a rigorous fabric-first methodology to eliminate TB and significantly reduce the building’s overall heat demand. This reduction enables the use of a compact heating system that can be efficiently powered by renewable energy sources, such as solar photovoltaic (PV). Moreover, this review employs a systematic literature synthesis to critically evaluate the integration of TB mitigation within the PAS 2035 framework, identifying key technical interdependencies and research gaps in whole-house retrofit methodology. This article provides a comprehensive review of established FEA modelling methodologies, rather than presenting results from original simulations. Full article

Home energy management systems have emerged as a crucial solution for enhancing energy efficiency, reducing carbon emissions, and facilitating the integration of renewable energy sources into homes. To fully realize their potential, these systems’ performance must be optimized, which involves addressing multiple objectives, such as minimizing costs and environmental impact. The Pareto frontier is a tool widely adopted in multi-objective optimization within home energy management systems’ operation, where a range of optimal solutions are produced. This study uses the Pareto curve to optimize the operational performance of home energy management systems, considering the state health of the battery to determine the best answer among the optimal solutions in the curve. The main reason for considering the state of health is the effects of the battery’s operation on the performance of energy systems, especially for long-term optimization outcomes. In this study, the performance of the battery is measured through a physical model named PyBaMM that is tuned based on swarm intelligence techniques, including the Whale Optimization Algorithm, Grey Wolf Optimization, Particle Swarm Optimization, and the Gravitational Search Algorithm. The proposed framework automatically identifies the optimal solution out of the ones in the Pareto curve by comparing the performance of the battery through the tuned physical model. The effectiveness of the proposed algorithm is demonstrated for a home, including four distinct energy carriers along with a 12 V 128 Ah LFP chemistry Li-ion battery module, where the overall cost and carbon emissions are the metrics for comparisons. Implementation results show that tuning the physical model based on the Whale Optimization Algorithm reaches the highest accuracy compared to the other methods. Moreover, considering the state of health of the battery as the selecting criterion will improve home energy management systems’ performance, particularly in long-term operation models, because it guarantees a longer battery lifespan. Full article

This paper presents the development and validation of an IoT-enabled temperature-sensing device for real-time monitoring of the thermal insulation properties of glass windows. The system integrates contact and non-contact temperature sensors into a compact PCB platform equipped with WiFi connectivity, enabling seamless integration into smart home and building management frameworks. By continuously assessing window insulation performance, the device addresses the challenge of energy loss in buildings, where glazing efficiency often degrades over time. The collected data can be transmitted to cloud-based services or local IoT infrastructures, allowing for advanced analytics, remote access, and adaptive control of heating, ventilation, and air-conditioning (HVAC) systems. Experimental results demonstrate the accuracy and reliability of the proposed system, confirming its potential to contribute to energy conservation and sustainable living practices. Beyond energy efficiency, the device provides a scalable approach to environmental monitoring within the broader future internet ecosystem, supporting the evolution of intelligent, connected, and human-centered living environments. Full article

Care homes are an energy-intensive component of the health and social care sector, with high demands on heating, lighting, laundry, catering and medical technologies. This constant energy use makes care homes a notable contributor to global greenhouse gas emissions. Decarbonising care homes presents an opportunity to reduce emissions, operational costs, and deliver health co-benefits by improving air quality and thermal comfort. This scoping review mapped the international evidence on decarbonisation in care homes to inform sustainable practice and policy development. Guided by Joanna Briggs Institute methodology, seven databases (CINAHL, EMBASE, IEEE, MEDLINE, PubMed, Scopus, and Web of Science) were searched. Eligible studies included care home facilities, residents or staff with data managed in Covidence and extracted using the “The Greenhouse Gas Protocol Corporate Standard Inventory Accounting”. A total of 22 studies met the inclusion criteria. The evidence was concentrated around Scope 2 emissions, through efforts to monitor and reduce electricity use, while Scope 1 (facility emissions) and Scope 3 (supply chain emissions) remain comparatively underexplored. Evidence was fragmented and revealed risk aversion and care quality concerns related to adopting low-carbon technologies, as well as a growing interest in digital technologies and sustainable food procurement. Care homes should be prioritised within net zero healthcare frameworks, with targeted research, policy guidance, and investment to support decarbonisation. Full article