Search Results (256)

The increasing integration of renewable energy sources, smart appliances, and distributed energy technologies has significantly increased the complexity of residential energy systems, necessitating advanced Home Energy Management Systems (HEMS). Optimization techniques play a critical role in achieving key objectives, including energy cost reduction, load balancing, minimizing the peak-to-average ratio, and enhancing user comfort. This paper presents a comprehensive review and critical analysis of optimization techniques employed in HEMS, including mathematical methods, metaheuristic algorithms, artificial intelligence (AI)-based approaches, and rule-based strategies. These techniques are systematically classified and compared based on scalability, computational complexity, uncertainty handling, and real-time applicability. The analysis reveals that while conventional methods provide reliable solutions for structured problems, AI-based techniques offer superior adaptability and performance in dynamic and data-driven environments. Furthermore, key research gaps are identified, including limited multi-objective optimization, inadequate consideration of uncertainty and electric vehicle integration, and the lack of real-world implementation. Finally, future research directions are outlined, emphasizing hybrid optimization frameworks and intelligent, IoT-enabled energy management systems. Full article

Smart home mobile applications are a primary digital channel through which older adults manage home devices and access daily services. Existing evaluation approaches do not adequately capture the cognitive burden experienced by older users, because dimensional weights are typically assigned through expert judgment rather than derived from target-user data. This study proposes a framework integrating eye-tracking-derived cognitive load with WCAG 2.2 criteria. Evaluation dimensions are defined based on the MOLD-US aging barrier classification, and nine indicators are selected according to compliance level, quantifiability, and relevance to cognitive aging. Cognitive load data from 35 older adults (aged 60–75) were used to calibrate dimensional priorities. Accessibility-related tasks produced significantly higher cognitive load than visual and operational tasks (Cohen’s $d_{\mathrm{z}}$ = 0.855), and the ordering held across three Cognitive Load Index aggregation schemes. A hybrid scoring mechanism combining a multimodal large language model with rule-based scripts was implemented for scalable evaluation. Validation on six high-fidelity prototypes showed strong agreement with expert ratings (Spearman’s $\rho$ = 0.71–0.93) and on the same scoring task, the framework required about 1/14 of the time taken by an expert panel. By calibrating dimensional weights with eye-tracking data from older adults instead of expert judgment alone, the framework integrates WCAG compliance scoring with group-specific priorities, positioned as a design-stage screening tool prior to deployment testing. Full article

The increasing complexity of residential energy systems, driven by rising electricity demand, renewable energy integration, and dynamic pricing mechanisms, has intensified the need for intelligent load scheduling within Home Energy Management Systems (HEMSs). This paper presents a comprehensive literature review of optimization algorithms applied to residential load scheduling, based on an analysis of 78 peer-reviewed studies published between 2020 and 2025. The analysis reveals a clear shift from conventional deterministic optimization toward adaptive and data-driven approaches capable of operating in uncertain and dynamic environments. Metaheuristic methods are widely used for solving complex scheduling problems, while Machine Learning and Deep Learning (DL) techniques primarily support forecasting tasks related to energy demand and renewable generation. Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) approaches enable autonomous real-time decision-making, although challenges related to scalability, computational cost, and practical deployment remain unresolved. The review identifies hybrid architectures that combine forecasting, optimization, and control mechanisms as the most promising direction for future HEMS development. Finally, the paper highlights key research gaps, including limited real-world validation, insufficient consideration of physical infrastructure constraints, and the need for scalable distributed control frameworks for future smart grids and energy communities. Full article

Home Energy Management Systems (HEMS) are increasingly positioned at the center of residential flexibility, particularly as homes integrate photovoltaics, battery storage, electric vehicles, and responsive loads. This systematic review examines recent advances in optimization and multi-asset coordination for HEMS. Searches were conducted in Scopus, Web of Science, IEEE Xplore, and ScienceDirect for studies published between 2020 and 2025; after screening and eligibility assessment, 90 studies were included. The findings indicates that deterministic optimization remains well suited to structured scheduling problems, whereas metaheuristic, hybrid, and learning-based methods are better able to address nonlinearity, uncertainty, and real-time adaptation. Across the reviewed literature, multi-asset integration generally improves cost, peak demand, self-consumption, and, in some cases, user comfort and emissions. Yet the field remains dominated by simulation-based validation. Future progress of HEMS will depend on real-world validation, interoperable system design, explainable control, and stronger alignment with user behavior, communication constraints, and regulatory frameworks. Full article

Feral donkeys (Equus asinus) are well adapted to arid ecosystems and are found in large populations in the deserts of Australia and the Americas. We assessed resource selection and seasonal home range size of female donkeys in southern California between 2020 and 2022 based on telemetry data. We also examined whether dyads with greater encounter rates were more likely to test positive for asinine herpesvirus 5 (AHV-5) and/or Streptococcus equi zooepidemicus (SEZ). Donkey home ranges were non-significantly larger in the cool/wet season (November through March; mean 318.37 ± sd 417.65 km²) than in the hot/dry season (April through October; mean 159.35 ± 212.43 km²). Donkeys selected flatter areas closer to water year-round but selected greater herbaceous cover during the cool/wet season and lower heat loads during the hot/dry season. Individuals testing positive for SEZ selected lower elevations during the wet season and closer distances to water during the dry season; donkeys testing positive for AHV-5 selected areas farther from water during the wet season and steeper slopes during the dry season. The dyad encounter rate was unrelated to presence of either disease. Our results contribute to the understanding of donkey ecology, allowing feral populations to be better controlled by specific and focused management. Full article

This paper presents a case study of a Home Energy Management System (HEMS) integrating photovoltaic (PV) generation, battery energy storage (BES), thermal storage, and a heat pump in a single-family household operating under a dynamic electricity tariff. The analysis is based on real operational data and focuses on system performance under varying solar generation conditions. The results show that during sunny days, the battery storage absorbs the entire surplus PV generation until reaching full capacity, i.e., 10 kWh, effectively preventing curtailment and maximizing self-consumption. On days with limited solar production, the system actively utilizes the available storage capacity by shifting energy use in time and, when economically justified, temporarily charging the battery from the grid during low-price periods. This strategy reduces electricity purchases during peak-price hours and stabilizes household energy costs. For the analyzed case, daily PV generation self-consumption exceeded 70% on high-generation days, while the application of storage-based load shifting under dynamic tariffs reduced daily electricity costs by up to 30% compared to a fixed-rate tariff. The study confirms that the economic and operational performance of residential energy systems under dynamic pricing depends primarily on adaptive storage control rather than on PV capacity alone, highlighting the central role of battery energy storage in year-round energy optimization. Full article

In the power grid scheduling process, load forecasting serves as the foundation for ensuring stability and economic dispatch. It not only optimizes resource allocation but also strengthens the system’s productivity and stability, helps prevent potential risks, and ensures the reliability and safety of power supply. Therefore, a predictive soft actor–critic network (PreSAC-Net) algorithm is proposed, which aims to reduce grid operating costs and enhance system stability through an enhanced load forecasting model and an optimized scheduling strategy. First, the load forecasting is performed using a sequential feature fusion model with gated recurrent attention and diffusion (SeqFusion-GRAD), which integrates gated recurrent units (GRU), attention mechanisms, and generative diffusion models to strengthen time-series modeling and accurately predict household electricity loads. Second, a multidimensional data fusion technique incorporates meteorological and other relevant factors into household load data, improving the forecast accuracy and robustness. Furthermore, the scheduling optimization is conducted with the soft actor–critic (SAC) algorithm, which explores scheduling schemes to minimize cost under multiple constraints. The integrated approach not only balances the electricity supply and demand effectively but also supports the sustainable development of intelligent grids. Based on the experimental results, the proposed method significantly enhances power system operational efficiency and stability. Full article

The rapid growth of distributed solar energy, such as rooftop photovoltaics (PVs), has revolutionized conventional power systems into more distributed networks, enabling end-users to engage in and trade within the energy market. Maximizing the benefits of rooftop PV panels for residential end-users, including increased renewable energy use and reduced reliance on the utility grid, remains an essential challenge in conventional centralized markets. Moreover, reducing energy consumption may lead to increased peak demand, decreased self-consumption, reduced system flexibility, and reduced grid stability. Therefore, this study presents a transactive energy market framework that integrates home energy management systems (HEMSs) with multi-objective optimization and an aggregator-based, distributed peer-to-peer (P2P) trading strategy to increase rooftop PV utilization and reduce grid dependency within an intra-residential community. The HEMS is structured to integrate rooftop PV production, battery energy storage systems, and smart appliances to offer flexibility through demand response programs in balancing supply and demand by scheduling appliances during periods of rooftop PV production and lower grid prices. Multi-objective (i.e., minimizing energy consumption cost and peak load) optimization problems are solved using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) by achieving a Pareto-optimal solution. To validate the reliability and optimality of the NSGA-II results, the same problem formulation is solved using a mixed-integer linear programming approach. Moreover, a Strategic Double Auction with Dynamic Pricing (SDA-DP) strategy is proposed to support P2P trading among consumers and prosumers and thereafter compared with a rule-based zero-intelligence strategy with market-matching rules to analyze the trading performance of the proposed SDA-DP. The results of this comparative analysis (for 10 households, year-long simulation with 15 min time resolution) demonstrate that compared to the baseline case, integrating NSGA-II optimization with SDA-DP trading significantly enhances rooftop PV utilization by 35.11%, reduces grid dependency by 34.04%, and reduces electricity consumption costs by 30.53%, with savings of €1.93 to €6.67 for a single day after participating in the proposed P2P market. Full article

This paper introduces SmartHouseOperator, a multi-agent intelligent control framework for adaptive and energy-efficient smart-home management. Modern smart homes integrate heterogeneous devices and sensors, yet most existing solutions rely on static rules or manual coordination, limiting their ability to adapt to dynamic environmental conditions and evolving user preferences. SmartHouseOperator addresses these limitations through an agentic architecture that coordinates device-specific agents for air conditioning, lighting, refrigeration, and shutters under a central orchestrator. The system combines contextual inputs (e.g., weather, occupancy, power load), persistent knowledge, reinforcement-learning-based preference modeling, and LLM-powered reasoning to enable coordinated and personalized control decisions. Experimental results show that the framework achieves consistent reasoning performance across multiple agent orchestration engines and reduces air-conditioning power consumption by up to 16% under critical load conditions. These findings demonstrate the potential of multi-agent, learning-enabled control systems to deliver intelligent, energy-aware, and user-centric smart-home operation. Full article

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

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

This paper presents a Genetic Algorithm-based Home Energy Management System designed to exploit the energy flexibility of user-dependent loads by identifying and recommending optimal operating schedules that minimize electricity costs. To determine the most advantageous 15 min activation slot for the following day for each load, the algorithm uses as input the forecasted consumption profile of non-optimizable loads and photovoltaic generation, both obtained through an LSTM-based model, along with the contracted power, applicable tariffs, and the load profiles of the selected appliances. Unlike previous approaches, the proposed framework allows users to select which loads to optimize and define specific operational constraints. Additionally, a user-friendly interface was developed to facilitate seamless interaction between the user and the system. To validate the proposed framework, a case study was conducted on a residential household with four occupants located in Portugal, considering user-dependent flexible loads such as a washing machine, tumble dryer, and dishwasher. The results demonstrated that the developed system operated effectively, reducing electricity costs by approximately 9% compared to a scenario without the proposed solution. Full article

The proliferation of the Social Internet of Things (SIoT) necessitates robust and scalable trust management systems to ensure secure and reliable interactions among heterogeneous devices. However, existing trust management models often lack scalability for large SIoT environments. To address this, a lightweight trust evaluation model for SIoT, referred to as Micro-Moment (MMTE), is presented here. MMTE evaluates trust based on concise, context specific, repetitive, and high-frequency interactions, termed micro-moments among SIoT devices. The MMTE model is evaluated using the Lysis dataset, which is extracted from a real SIoT environment, and demonstrates superior resource efficiency compared to existing SIoT trust models with significantly lower CPU time, memory, and disk usage. MMTE’s linear complexity and simple design make it more resource efficient and scalable than other lightweight trust models, especially when processing large-scale data in heterogeneous SIoT networks. Moreover, MMTE accurately distinguishes 99.35% of malicious nodes in a simulated smart home environment. Furthermore, a numerical comparison clearly demonstrates that MMTE outperforms existing and recently published trust models in terms of classifying malicious and benign nodes. To enhance scalability, the concept of trust spheres is introduced, and devices with similar trust scores are grouped to streamline processing and storage demands. Sphere Anchors manage the trust spheres and efficiently distribute computational tasks and optimize storage through an adaptive storage strategy. The trust spheres also efficiently manage increasing network sizes, maintaining linear processing times as the traffic load increases, and also outperform existing models in terms of average propagation times. MMTE and trust spheres together provide a robust, scalable, and lightweight solution for trust management in SIoT networks. Full article

A very important issue in urban agglomerations is the proper management of green waste while reducing its negative impact on the environment. One potential solution is the utilization of green biomass—originating from the maintenance of parks, squares, and home gardens—for the production of compost and compost-based pellets as organic fertilizers. The aim of this study was to produce compost-based pellets intended for fertilization purposes from compost derived from green waste and conifer sawdust, and to analyze their mechanical and chemical properties. Ten variants of pellets with different compost-to-sawdust ratios were evaluated. Compost-based pellets exhibited the highest initial mechanical strength; however, their resistance to external loads decreased over time, whereas the best long-term stability was observed in pellets containing 50% sawdust. The seasoning process influenced the stabilization or improvement of the mechanical properties of certain mixtures. Chemical analyses showed that compost-based pellets contained the highest concentrations of nutrients (N, P, K), while increasing the proportion of sawdust reduced their fertilizing value. No exceedances of permissible heavy metal limits were detected. The results confirm the suitability of compost-based pellets made from green biomass as a sustainable alternative to mineral fertilizers, supporting the principles of the circular economy. Full article

Germany’s energy transition to a higher share of renewable energy sources (RESs) is characterized by decentralization, with citizens, cooperatives, SMEs, and municipalities playing a central role. As of early 2025, private individuals own a significant share of renewable energy installations, particularly PV panels, which corresponds to approximately half of the total installed PV power. This trend is driven by physical, technological, and societal factors. Technological advances in battery storage and sector coupling are expected to further decentralize the energy system. Thereby, the electrification of mobility, particularly through electric vehicles (EVs), offers significant storage potential and grid-balancing capabilities via bidirectional charging, although it also introduces challenges, especially for distribution grids during peak loads. Within this work we present a detailed digital twin of the entire distribution grid of the rural German municipality of Wüstenrot. Using grid operator data and transformer measurements, we evaluate strategic expansion scenarios for electromobility, PV and heat pumps based on existing infrastructure and predicted growth in both public and private sectors. A core focus is the intelligent integration of EV charging infrastructure to avoid local overloads and to optimise grid utilisation. Thereby municipally planned and privately driven expansion scenarios are compared, and grid bottlenecks are identified, proposing solutions through charge load management and targeted infrastructure upgrades. This study of Wüstenrot’s low-voltage grid reveals substantial capacity reserves for future integration of heat pumps, electric vehicles (EVs), and photovoltaic systems, supporting the shift to a sustainable energy system. While full-scale expansion would require significant infrastructure investment, mainly due to widespread EV adoption, simple measures like temporary charge load reduction could cut grid stress by up to 51%. Additionally, it is shown that bidirectional charging offers further relief and potential income for EV owners. Full article