Search Results (459)

With the increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users. Full article

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

Natural disasters in the United States frequently wreak havoc on critical infrastructure, affecting energy, water, transportation, and communication systems. To address these disruptions, the use of mobile power solutions like PowerKiosk trailers is a partial solution during recovery periods. PowerKiosk is a trailer equipped with renewable energy sources such as solar panels and biogas generators, offering a promising strategy for emergency power restoration. With a daily power output of 12.1 kWh, PowerKiosk trailers can support small lift stations or a few homes, providing a temporary solution during emergencies. Their key strength lies in their mobility, allowing them to quickly reach disaster-affected areas and deliver power when and where it is most needed. This flexibility is particularly valuable in regions like Florida, where hurricanes are common, and power outages can cause widespread disruption. Although the PowerKiosk might not be suitable for long-term use because of its limited capacity, it can play a critical role in disaster recovery efforts. In a community-wide power outage, deploying the PowerKiosk to a lift station ensures essential services like wastewater management, benefiting everyone. By using this mobile power solution, community resilience can be enhanced in the face of natural disasters. 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

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

The development of electric mobility offers new perspectives in the energy sector and improves resource efficiency and sustainability. This paper proposes a new strategy for synchronizing the energy requirements of home, commercial, and vehicle mobility, with a focus on the batteries of electric cars. In particular, this paper describes the coordination between a battery management algorithm that optimally assigns its capacity so that at least a part is reserved for mobility and a vehicle-to-building (V2B) service algorithm that uses a share of EV battery energy to improve user participation in renewable energy exploitation at home and at work. The system offers the user the choice of always maintaining a minimum charge for mobility or providing more flexible use of energy for business needs while maintaining established vehicle autonomy. Suitable management at home and at work allows always charging the vehicle to the required level of charge with renewable power excess, highlighting how the cooperation of home and work charging may provide novel frameworks for a smarter and more sustainable integration of electric mobility, reducing energy consumption and providing more effective energy management. The effectiveness of the proposed solution is demonstrated in a realistic configuration with real data and an experimental setup. 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

The integration of Electric Vehicles (EVs) with solar power generation is important for decarbonizing the economy. While electrifying transportation reduces Greenhouse Gas (GHG) emissions, its success depends on ensuring that EVs are charged with clean energy, requiring significant increases in photovoltaic capacity and robust Demand-Side Management (DSM) solutions. EV charging patterns, such as home, workplace, and public charging, need adapted strategies to match solar generation. This study analyzes a system designed to meet a unitary hourly average energy demand (8760 MWh annually) using an optimization framework that balances PV capacity and battery storage to ensure reliable energy supply. Historical solar data from 22 years is used to analyze seasonal and interannual fluctuations. The results show that solar PV alone can cover around 30% of the demand without DSM, rising to nearly 50% with aggressive DSM measures, using PV capacities of 1.0–2.0 MW. The optimization reveals that incorporating battery storage can achieve near 100% coverage with PV power of 8.0–9.0 MW. Moreover, DSM reduces required storage from 18 to about 10 MWh. These findings highlight the importance of integrating optimization-based energy management strategies to enhance system efficiency and cost-effectiveness, offering a pathway toward a more sustainable and resilient EV charging infrastructure. Full article

With the development of smart grids and home energy management systems, accurate load identification has become an important part of improving energy efficiency and ensuring electrical safety. However, traditional load identification methods struggle with high computational overhead and long model update times, which hinder real-time performance. In this study, a load identification method based on the channel attention mechanism is proposed for the lightweight model update problem in the electrical load identification task. To overcome this challenge, we construct color V-I trajectory maps by extracting the voltage and current signals of electrical devices during steady-state operation, and combine the convolutional neural network and channel attention mechanism for feature extraction and classification. Experimental results show that the proposed method significantly improves the accuracy, precision, recall, and F1-score compared with traditional methods on the public dataset, and tests on real hardware platforms verify its efficiency and robustness. This suggests that the lightweight model update method based on the channel attention mechanism holds great promise for smart grid applications, particularly in real-time systems with limited computational resources. Full article

The surge in electric vehicles (EVs) in Denmark challenges the country’s residential low-voltage (LV) distribution system. In particular, it increases the demand for home EV charging significantly and possibly overloads the LV grid. This study analyzes the impact of EV charging integration on Denmark’s residential distribution networks. A residential grid comprising 67 households powered by a 630 kVA transformer is studied using DiGSILENT PowerFactory. With the assumption of simultaneous charging of all EVs, the transformer can be heavily loaded up to 147.2%. Thus, a voltage-droop based autonomous control approach is adopted, where the EV charging power is dynamically adjusted based on the point-of-connection voltage of each charger instead of the fixed rated power. This strategy eliminates overloading of the transformers and cables, ensuring they operate within a pre-set limit of 80%. Voltage drops are mitigated within the acceptable safety range of ±10% from normal voltage. These results highlight the effectiveness of the droop control strategy in managing EV charging power. Finally, it exemplifies the benefits of intelligent EV charging systems in Horizon 2020 EU Projects like SERENE and SUSTENANCE. The findings underscore the necessity to integrate smart control mechanisms, consider reinforcing grids, and promote active consumer participation to meet the rising demand for a low-carbon future. Full article

Under the “Healthy China” strategy, the demand for home fitness equipment is increasing, but existing solutions face challenges such as large size, limited functionality, and lack of personalization. This study proposes an innovative integrated design framework for multifunctional home fitness equipment, combining modular design, space optimization, and intelligent health monitoring. The design integrates an exercise bike, rowing machine, and spring tensioner into a single unit, reducing equipment footprint by 30% while enabling seamless transitions between exercise modes. Multimodal sensors collect real-time physiological data, processed via Kalman filtering and adaptive algorithms to generate personalized fitness recommendations. The system achieves 95% monitoring accuracy for key metrics (heart rate: 97–147 bpm, energy consumption: 216–550 kcal) and improves user satisfaction by 40% compared to conventional equipment. This research demonstrates a scalable and intelligent solution that bridges the gap between multifunctional integration and user-centric health management, offering significant advancements over previous designs. Full article

Photovoltaic (PV) energy production in Western countries increases yearly. Its production can be carried out in a highly distributed manner, not being necessary to use large concentrations of solar panels. As a result of this situation, electricity production through PV has spread to homes and open-field plans. Production varies substantially depending on the panels’ location and weather conditions. However, the integration of PV systems presents a challenge for both grid planning and operation. Furthermore, the predictability of rooftop-installed PV systems can play an essential role in home energy management systems (HEMS) for optimising local self-consumption and integrating small PV systems in the low-voltage grid. In this article, we show a novel methodology used to predict the electrical energy production of a 48 kWp PV system located at the Campus Feuchtwangen, part of Hochschule Ansbach. This methodology involves hybrid time series techniques that include state space models supported by artificial intelligence tools to produce predictions. The results show an accuracy of around 3% on nRMSE for the prediction, depending on the different system orientations. Full article