Search Results (227)

Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article

The growing global demand for energy has resulted in a demand for innovative strategies for residential energy management. This study explores a novel framework—MELISSA (Modern Energy LLM-IoE Smart Solution for Automation)—that integrates Internet of Things (IoT) sensor networks with Large Language Models (LLMs) to optimize household energy consumption through intelligent automation and personalized interactions. The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. A 12-month randomized controlled trial was conducted with 100 households, which were stratified across four socioeconomic quintiles in metropolitan areas. The experimental design included the continuous collection of IoT data. Baseline energy consumption was measured and compared with post-intervention usage to assess system impact. Statistical analyses included k-means clustering, multiple linear regression, and paired t-tests. The system achieved its intended goal, with a statistically significant reduction of 5.66% in energy consumption (95% CI: 5.21–6.11%, $p<0.001$) relative to baseline, alongside high user satisfaction (mean = 7.81, SD = 1.24). Clustering analysis ($k=4$, silhouette = 0.68) revealed four distinct energy-consumption profiles. Multiple regression analysis ($R^2=0.68$, $p<0.001$) identified household size, ambient temperature, and frequency of user engagement as the principal determinants of consumption. This research advances the theoretical understanding of human–AI interaction in energy management and provides robust empirical evidence of the effectiveness of LLM-mediated behavioral interventions. The findings underscore the potential of conversational AI applications in smart homes and have practical implications for optimization of residential energy use. Full article

The growing mismanagement of energy resources is a pressing issue that poses significant risks to both individuals and the environment. As energy consumption continues to rise, the ramifications become increasingly severe, necessitating urgent action. In response, the rapid expansion of Internet of Things (IoT) devices offers a promising and innovative solution due to their adaptability, low power consumption, and transformative potential in energy management. This study describes a novel, integrative strategy that integrates IoT and Artificial Neural Networks (ANNs) in a smart monitoring mobile application intended to optimize energy usage and promote sustainability in residential settings. While both IoT and ANN technologies have been investigated separately in previous research, the uniqueness of this work is the actual integration of both technologies into a real-time, user-adaptive framework. The application allows for continuous energy monitoring via modern IoT devices and wireless sensor networks, while ANN-based prediction models evaluate consumption data to dynamically optimize energy use and reduce environmental effect. The system’s key features include simulated consumption scenarios and adaptive user profiles, which account for differences in household behaviors and occupancy patterns, allowing for tailored recommendations and energy control techniques. The architecture allows for remote device control, real-time feedback, and scenario-based simulations, making the system suitable for a wide range of home contexts. The suggested system’s feasibility and effectiveness are proved through detailed simulations, highlighting its potential to increase energy efficiency and encourage sustainable habits. This study contributes to the rapidly evolving field of intelligent energy management by providing a scalable, integrated, and user-centric solution that bridges the gap between theoretical models and actual implementation. Full article

With the increasing prominence of the energy crisis and environmental problems, microgrid technology has received widespread attention as an important technical means to improve the stability and reliability of new energy access. Focusing on the latest development of microgrid operation control technology, this paper combs and summarizes the related research at home and abroad, including the key technologies of microgrid optimization operation, power prediction and virtual synchronous active support control technology, and points out their advantages and limitations. First, this review describes the concept and structure of microgrids, including components such as distributed power sources, energy storage devices, energy conversion devices and loads. Then, the microgrid optimization operation technologies are analyzed in detail, including energy management optimization algorithms for efficient use of energy and cost reduction. Focusing on microgrid power forecasting techniques, including wind energy and PV power forecasting and load forecasting, the contributions and impacts of different power forecasting methods are summarized. Furthermore, the inverter control strategies and the stability mechanism of the virtual synchronous generator (VSG) active support control technology are investigated. Finally, synthesizing domestic and international microgrid development experience, this review summarizes the current state-of-the-art technologies, analyzes the advantages and limitations of these key technologies (including optimization scheduling, power prediction and VSG-based active support control) and highlights the necessity of their continuous improvement to provide a solid foundation for promoting the widespread application and sustainable development of microgrid technology. Full article

Simulation-based development is a structured approach that uses formal models to design and test system behavior before building the actual system. The Internet of Things (IoT) connects physical devices equipped with sensors and software to collect and exchange data. Cyber-Physical Systems (CPSs) integrate computing directly into physical processes to enable real-time control. This paper reviews the Discrete-Event System Specification (DEVS) formalism and explores how it can serve as a unified framework for designing, simulating, and implementing systems that combine IoT and CPS—referred to as the Internet of Cyber-Things (IoCT). Through case studies that include home automation, solar energy monitoring, conflict management, and swarm robotics, the paper reviews how DEVS enables construction of modular, scalable, and reusable models. The role of the System Entity Structure (SES) is also discussed, highlighting its contribution in organizing models and generating alternative system configurations. With this background as basis, the paper evaluates whether DEVS provides the necessary modeling power and continuity across stages to support the development of complex IoCT systems. The paper concludes that DEVS offers a robust and flexible foundation for developing IoCT systems, supporting both expressiveness and seamless transition from design to real-world deployment. Full article

The Internet of Things (IoT) has enabled the development of various applications for energy, exploiting unprecedented data collection, multi-stage data processing, enhanced awareness, and control of the physical environment. In this context, the availability of tools for efficient development is paramount. This paper explores and validates the use of a generic, flexible, open-source measurement-oriented data collection framework for the energy field, namely Measurify, in the Internet of Things (IoT) context. Based on a literature analysis, we have spotted three domains (namely, vehicular batteries, low voltage (LV) test feeder, and home energy-management system) and defined for each one of them an application (namely: range prediction, power flow analysis, and appliance scheduling), to verify the impact given by the use of the proposed IoT framework. We modeled each one of them with Measurify, mapping the energy field items into the abstract resources provided by the framework. From our experience in the three applications, we highlight the generality of Measurify, with straightforward modeling capabilities and rapid deployment time. We thus argue for the importance for practitioners of using powerful big data management development tools to improve efficiency and effectiveness in the life-cycle of IoT applications, also in the energy domain. Full article

This paper analyzes the process of energy transition from traditional solid fuel heating to an air-to-air (A2A) heat pump-based heating system. Special emphasis was placed on the implementation of new technologies for improved management of energy systems, aiming to elevate both comfort levels and energy efficiency. This paper explores the use of the open-source software Home Assistant as an integration platform for home automation, designed to manage smart home devices while preserving local control, user privacy, and increasing cybersecurity. The proposed hardware platform includes a Raspberry Pi with appropriate IoT modules, providing a flexible and economically viable solution for household needs. Full article

Non-Intrusive Load Monitoring (NILM) technology, enabled by high-precision electrical data acquisition sensors at household entry points, facilitates real-time monitoring of electricity consumption, enhancing user interaction with smart home systems and reducing electrical safety risks. However, the growing diversity of household appliances and limitations in NILM accuracy and robustness necessitate innovative solutions. Additionally, outdated public datasets fail to capture the rapid evolution of modern appliances. To address these challenges, we constructed a high-sampling-rate voltage–current dataset, measuring 15 common household appliances across diverse scenarios in a controlled laboratory environment tailored to regional grid standards (220 V/50 Hz). We propose an AI-driven NILM method that integrates power-mapped, color-coded voltage–current (V–I) trajectories with frequency-domain features to significantly improve load recognition accuracy and robustness. By leveraging deep learning frameworks, this approach enriches temporal feature representation through chromatic mapping of instantaneous power and incorporates frequency-domain spectrograms to capture dynamic load behaviors. A novel channel-wise attention mechanism optimizes multi-dimensional feature fusion, dynamically prioritizing critical information while suppressing noise. Comparative experiments on the custom dataset demonstrate superior performance, particularly in distinguishing appliances with similar load profiles, underscoring the method’s potential for advancing smart home energy management, user-centric energy feedback, and social informatics applications in complex electrical environments. Full article

Home energy optimization management improves energy utilization efficiency and reduces electricity costs through intelligent load control, strategic utilization of time-of-use pricing, and optimized integration of energy storage and distributed energy systems. Simultaneously, it enhances energy autonomy, lowers carbon emissions, and promotes sustainable low-carbon lifestyles. By coordinating demand response programs with flexible load scheduling strategies, this approach effectively reduces peak loads and improves grid stability, thereby advancing smart grid development. This paper investigates the optimized scheduling problem in smart home energy management systems, focusing on achieving integrated optimization of multiple factors, including load balancing, cost control, carbon emission reduction, user comfort, and demand response. Considering the diverse load characteristics of residential energy systems, we propose a novel optimization framework incorporating dynamic pricing mechanisms and intelligent scheduling algorithms, which is rigorously validated through simulation experiments. Results demonstrate that the proposed scheduling strategy successfully balances economic efficiency, load management, and environmental sustainability while maintaining acceptable user comfort levels—providing a comprehensive solution for intelligent home energy management systems. Full article

The adoption of the Internet of Things (IoT) in smart household energy systems offers new opportunities for efficiency and automation, while also posing substantial security challenges. These systems utilize diverse standards and protocols to autonomously access, collect, and share energy-related data over distributed networks. However, this interconnectivity increases their vulnerability to cyber threats, making the system vulnerable to cyber threats. The literature reveals numerous cases of cyberattacks on IoT-based energy infrastructures, primarily involving unauthorized access, data breaches, and device exploitation. Therefore, designing a robust ecosystem with secure and efficient access control (AC), while safeguarding user functionality and privacy, is essential. This paper proposes a dynamic attribute-based access control (ABAC) model that leverages a hybrid blockchain architecture to enhance security and trust in smart household energy systems. The proposed architecture integrates Hyperledger Fabric for managing user, resource, and device attributes using smart contracts, while Hyperledger Besu enforces decentralized access policies. Additionally, a trust recalibration mechanism dynamically adjusts access permissions based on behavioral analysis, mitigating unauthorized access risks and improving energy system adaptability. Experimental results demonstrate the model’s effectiveness in securing IoT smart home energy, while ensuring seamless device onboarding and efficient access control. Full article

Heat pumps (HPs) are considered as a key technology in the future energy system. Besides technical and ecological aspects, user acceptance and user friendliness are also essential. The aim of the study was therefore to research which aspects are decisive for the purchase decision, which different types of HP owners can be distinguished, how their specific user behavior can be characterized in terms of control and operation, and what their respective requirements and wishes are for the functions and operation of their HPs. A mixed-methods approach in an exploratory sequential design was used. Based on nine qualitative interviews and a survey with 510 respondents, both conducted in Austria, it is observed that the most relevant arguments for the purchase decision of HPs are high environmental friendliness and efficiency, as well as resource independence. Respecting certain usage and requirement patterns, four user types could be identified and defined—the minimalist, the functionalist, the tech-savvy tinkerer, and the anxious user. In the future, intelligent control and regulation approaches and the integration of HPs into a holistic energy and building management system (smart home) will become more important. Based on the results, tailor-made system solutions can be developed, user friendliness optimized, and new services developed. Full article

The increasing adoption of intermittent renewable energy sources and electric vehicles in households necessitates effective energy management systems (EMS) in the residential sector. This study quantifies the economic benefits of using a state-of-the-art EMS for optimally controlling a grid-connected smart home, which includes PV panels, a battery, and an EV charging station with either monodirectional or bidirectional charging modes. The EMS uses a two-layer approach: the first layer handles strategic decisions with day-ahead forecasts and solving a mixed-integer linear program (MILP) model; the second layer manages the real-time control decisions based on a heuristic strategy. Tested on 396 real-world case studies (based on measured data) with varying user types and energy systems (different PV plant sizes, with or without BESS, and different EV charging modes), different EV models, and weekly commutes, the results demonstrate the EMS’s cost-effectiveness compared to current non-predictive heuristic strategies. Annual cost savings exceed 20% in all cases and reach up to 900 €/year for configurations with large (6 kW) PV plants. Additionally, while installing a battery is not economically advantageous, bidirectional EV chargers yield 10–15% additional savings compared to monodirectional chargers, increasing with more weekly remote working days. Full article

Background: There is no cure for mitochondrial diseases which manifest in numerous ways including fatigue, muscle weakness, and exercise intolerance. Medical treatment varies and focuses on managing symptoms. Photobiomodulation (PBM) can decrease mitochondrial damage thereby increasing energy production and decreasing cell death. This pilot study will apply PBM to people with mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) to examine the safety of application, and if changes occur in symptoms and signs after cross-over application/withdrawal of a sham or active PBM treatment including a two-week period of washout. Methods: This study is an exploratory, prospective series N-of-1 (single patient) studies. The protocol is guided by the CONSORT extension for reporting N-of-1 trials (CENT 2015), chosen due to the rarity of mitochondrial diseases, the fluctuating symptomology, and heterogeneity of the clinical presentation. The primary outcome is patient-reported fatigue assessed using the Checklist of Individual Strength and with concomitant evaluation of safety. Secondary measures are of depression, anxiety and stress, sleepiness, physical activity, blood lactate and creatine kinase, physical measures of sit-to-stand, and heel raise capability. Mitochondrial function will be evaluated using hydrogen magnetic resonance spectroscopy for lactate. PBM will be a participant-administered, home-based therapy using a multiple diode flexible array (BeniLight iLED-Pro Multi-Wave Multi-Pulse belt; 465 nm, 660 nm, 850 nm; average irradiance 5.23 mW/cm²; total joules: 770.1 J/treatment, all sites; 5 KHz; 20% duty ratio) over the anterior thigh muscles, posterior calf muscles and abdomen for 10 min to each site, three times/week. The safety of the intervention will be assessed. Descriptive statistics, causal analyses of time series data and dynamic modelling will be applied as relevant to the variables collected. Hydrogen magnetic resonance spectra will be acquired and averaged to obtain the content of the targeted hydrogen levels. Discussion: The study will provide guidance on whether and how to progress to a larger, randomised cohort study with sham control. Full article

Improving user-level energy efficiency is critical for reducing the load on the power grid and addressing the challenges created by tight power balance when operating domestic air conditioning equipment under time-of-use (ToU) pricing. This paper presents a data-driven control method for HVAC (heating, ventilation, and air conditioning) systems that is based on model predictive control (MPC) and takes ToU electricity pricing into account. To describe building thermal dynamics, a multi-layer neural network is constructed using time-delayed embedding, with the rectified linear unit (ReLU) serving as the activation function for hidden layers. Using this piecewise affine approximation, an optimization model is developed within a receding horizon control framework, integrating the data-driven model and transforming it into a mixed-integer linear programming issue for efficient problem solving. Furthermore, this research suggests a hybrid optimization model for integrating air conditioning systems and battery energy storage systems. By employing a rolling time-domain control method, the proposed model minimizes the frequency of switching between charging and discharging states of the battery energy storage system, improving system reliability and efficiency. An Internet of Things (IoT)-based home energy management system is developed and validated in a real laboratory environment, complemented by a distributed integration solution for the energy management monitoring platform and other essential components. The simulation results and field measurements demonstrate the system’s effectiveness, revealing discernible pre-cooling and pre-charging behaviors prior to peak electricity pricing periods. This cooperative economic operation reduces electricity expenses by 13% compared to standalone operation. Full article

More recently, researchers and the industrial community have started researching DC appliances and DC microgrids as a means of increasing the end-to-end efficiency of systems. Given the fluctuating nature of renewable resources, energy storage becomes mandatory in powering households with minimal AC grid supply, and rechargeable battery packs with maximum power point tracking controllers with inverters are used. However, this approach is not the most efficient due to losses in the power converters used in the energy supply path, while short life and environmental concerns of battery storage also come into play. With the rapid development of commercial super-capacitors, with longer life, higher power density and wider operational temperature range, this device family can be at the center of a new development era, for power converters for DC homes and DC appliances. The new family of converters and protection systems known as supercapacitor-assisted techniques is a unique new approach to minimize or eliminate batteries while improving the ETEE. These new SCA techniques are based on a new theoretical concept now published as supercapacitor-assisted loss management theory. In this paper, we will demonstrate how we extend SCALoM theory to develop SCA converters for whiteware, with the example of a DC-converted commercial double-door refrigerator with implementation details. Full article