Search Results (227)

Wheelchair-mounted assistive robotic manipulators can provide reach and grasp functions for power wheelchair users. This in-lab study evaluated a vision-guided shared control (VGS) system with twelve users completing two multi-step kitchen tasks: a drinking task and a popcorn making task. Using a mixed methods approach participants compared VGS and manual joystick control, providing performance metrics, qualitative insights, and lessons learned. Data collection included demographic questionnaires, the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and exit interviews. No significant SUS differences were found between control modes, but NASA-TLX scores revealed VGS control significantly reduced workload during the drinking task and the popcorn task. VGS control reduced operation time and improved task success but was not universally preferred. Six participants preferred VGS, five preferred manual, and one had no preference. In addition, participants expressed interest in robotic arms for daily tasks and described two main operation challenges: distinguishing wrist orientation from rotation modes and managing depth perception. They also shared perspectives on how a personal robotic arm could complement caregiver support in their home. Full article

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

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

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

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

This study aimed to evaluate the therapeutic adherence to antiretroviral therapy (ART) and the cost of care for people living with HIV (PLwHIV) in the Condesa Specialized Clinics (CSCs). A cross-sectional observational study was conducted using the Adherence Follow-Up Questionnaire developed by The AIDS Clinical Trials Group (ACTG) to measure adherence in 261 PLwHIV. An economic Markov model was developed to simulate clinical outcomes, health costs, and quality-adjusted life years (QALYs) over a 5-year horizon from the CSC perspective. The mean adherence index was 89.97, and 59% of the surveyed PLwHIV were non-adherent, but more than 95% of the population had an undetectable viral load, suggesting that ART remains effective in achieving clinical goals, even under suboptimal adherence conditions. More than half of the surveyed PLwHIV (60.54%) stated that they had stopped taking their ART at some point, and the three most frequent causes were forgetting (49.37%), being away from home (45.57%), and having a change in their daily routine (25.95%). The economic model showed a cumulative cost per PLwHIV of USD 8432 and 3.80 QALYs (USD 2218/QALYs), which is below the threshold of willingness to pay in Mexico (USD 13,790/QALY). These findings provide valuable information to guide public health decisions and resource allocation in HIV management in Mexico. Full article

The use of load-monitoring systems in residential homes is fundamental in the context of smart homes and smart grids. Specifically, these systems will allow, for example, the provision of efficient energy management and/or load forecasting for residential homes. To achieve this goal, these systems can be based on the concept of a smart meter. However, a smart meter provides aggregate power consumption, which makes it extremely complex to identify individual home appliances, even using advanced algorithms. In line with this, this paper proposes to analyze the impact of power definitions on the disaggregation of home appliance loads. Moreover, it will also consider the distortion of the voltage grid, which is usually not addressed in the resolution of this problem. This effect will be verified through an approach that is based on a genetic algorithm. The approach will be tested through the use of several scenarios, in which an aggregation of home appliances is used. 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

This research aimed to ascertain the ZIP (constant impedance, constant current, and constant power) coefficients and Conservation of Voltage Reduction factor (CVR_f) for residential appliances as well as for the residential network feeders in Malaysia through measurement and simulation analysis. The required power data were obtained through varying the supply voltage from 250 V to 215 V with a 5 V reduction. The appliances’ components were identified using the ZIP coefficients solved with the Sequential Least Squares Programming optimizer in Python (Spyder 5.5.4). The CVR_f for residential appliances was determined using the well-established voltage and power correlation analysis. The study’s findings demonstrate a strong association between the appliance load composition determined by the ZIP load model and CVR_f. This paper’s primary contribution is a comprehensive analysis conducted using the ZIP and CVR techniques to ascertain each appliance’s load composition. Based on the findings of this study, a classification is developed and extended to include a range of findings from other published studies in which the conclusion is consistent. Moreover, the CVR_f value for one residence corresponds to a residential substation CVR_f which is further validated via bottom-up load model analysis. The main contribution of this paper is to categorize residential appliances based on constant impedance, constant current, and constant power through the ZIP load model and the CVR_f. Additionally, this CVR analysis is the pioneer study in Malaysia; thus, it is crucial to develop a systematic approach for identifying and classifying household devices according to their electrical characteristics. Load categorization provides the fundamental understanding about an appliance to determine its behavior towards a change in voltage, thus establishing cost savings and energy management in a home. Full article

Non-Intrusive Load Monitoring (NILM) includes a set of methods orientated to disaggregating the power consumption of a household per appliance. It is commonly based on a single metering point, typically a smart meter at the entry of the electrical grid of the building, where signals of interest, such as voltage or current, can be measured and analyzed in order to disaggregate and identify which appliance is turned on/off at any time. Although this information is key for further applications linked to energy efficiency and management, it may also be applied to social and health contexts. Since the activation of the appliances in a household is related to certain daily activities carried out by the corresponding tenants, NILM techniques are also interesting in the design of remote monitoring systems that can enhance the development of novel feasible healthcare models. Therefore, these techniques may foster the independent living of elderly and/or cognitively impaired people in their own homes, while relatives and caregivers may have access to additional information about a person’s routines. In this context, this work describes an intelligent solution based on deep neural networks, which is able to identify the daily activities carried out in a household, starting from the disaggregated consumption per appliance provided by a commercial smart meter. With the daily activities identified, the usage patterns of the appliances and the corresponding behaviour can be monitored in the long term after a training period. In this way, every new day may be assessed statistically, thus providing a score about how similar this day is to the routines learned during the training interval. The proposal has been experimentally validated by means of two commercially available smart monitors installed in real houses where tenants followed their daily routines, as well as by using the well-known database UK-DALE. 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

Increasingly, renewable energy resources, energy storage systems (ESSs), and demand response programs (DRPs) are being discussed due to environmental concerns and smart grid developments. An innovative home appliance scheduling scheme is presented in this paper, which incorporates a local energy grid with wind turbines (WTs), photovoltaic (PV), and ESS, which is connected to an upstream grid, to schedule household appliances while considering various constraints and DRP. Firstly, the household appliances are specified as non-shiftable and shiftable (interruptible, and uninterruptible) loads, respectively. Secondly, an enhanced mathematical formulation is presented for smart home energy management which considers the real-time price of upstream grids, the price of WT, and PV, and also the sold energy from the smart home to the microgrid. Three objective functions are considered in the proposed energy management: electricity bill, peak-to-average ratio (PAR), and pollution emissions. To solve the optimization problem, a novel modification-based grey wolf optimizer (GWO) is proposed. When the wolves hunt prey, other wild animals try to steal the prey or some part of the prey, hence they should protect the prey; therefore, this modification mimics the battle between the grey wolves and other wild animals for the hunted prey. This modification improves the performance of the GWO in finding the best solution. Simulations are examined and compared under different conditions to explore the effectiveness and efficiency of the suggested scheme for simultaneously optimizing all three objective functions. Also, both GWO and improved GWO (IGWO) are compared under different scenarios, which shows that IGWO improvement has better performance and is more robust. It has been seen in the results that the suggested framework can significantly diminish the energy costs, PAR, and emissions simultaneously. Full article

Estuarine lakes possess significant ecological value. Their complex hydrological environments give rise to diverse habitats, providing a home to numerous life forms. However, with the intensification of the impacts of global climate change, the estuarine lake ecosystem is facing severe challenges. Through trend analysis and differential analysis, this paper elaborates on the changes in nitrogen and phosphorus nutrients in the bottom water of Lake Nakaumi from 2013 to 2017. It analyzes the differences between the El Niño period and the non-El Niño period, speculates on the possible connections between the changes in nutrient load and the El Niño phenomenon, and uses the traditional water quality assessment method of the WQI to more intuitively demonstrate the fluctuations in nutrient load. Based on the analysis of the case of Lake Nakaumi, possible environmental management suggestions are put forward. Additionally, the paper compares and discusses the differences in the changes of lakes at adjacent latitudes during similar periods. As for the bottom water of Lake Nakaumi, there may be the following connections between the changes in its nutrient load and the El Niño phenomenon: (1) DPO₄-P was most sensitive to the peak intensity of the El Niño phenomenon. (2) Compared with NO₃-N, the changes in NO₂-N and DPO₄-P were more sensitive to the start of the El Niño cycle. (3) The El Niño phenomenon had differential impacts on various forms of nitrogen and phosphorus in Lake Nakaumi. The focus of this paper is to explore the connection between the El Niño phenomenon and the changes in the nutrient load of the bottom water of estuarine lakes and to find a method beneficial to environmental management. However, due to the limitations of the currently available data, there are still many deficiencies that need to be further addressed. It is hoped that this paper can attract the attention of relevant researchers to this issue. Full article

Integrating renewable energy sources (RESs) such as solar and wind generation systems introduces challenges in ensuring a safe and stable power supply to the power system due to their inherent output variability. Addressing this issue requires the development of advanced technologies and methodologies to mitigate power variability while enabling the integration of high levels of renewable energy into the existing power system. One practical approach to managing the variability of RESs is incorporating an energy storage system (ESS), which enhances the reliability and stability of the power supply from RESs. This study focuses on optimized energy management and storage capacity sizing while ensuring safe operation amid output variability to maximize the benefits of combining RESs and two ESSs (i.e., primary and secondary) for a smart home energy management system. To achieve this, a linear programming (LP) model is employed to investigate the relationship between RESs, ESSs, and energy loads to determine the storage capacity under safety conditions. Here, safety refers to preserving the capacity limitations of each ESS in the power system against fluctuations. The optimization problem is mathematically formulated, and a feasible solution is found using the LP Solver in MATLAB. To validate the proposed optimal sizing of ESS and energy balancing against fluctuations, power generation, and consumption data from apartment facility, iHouse smart apartment facilities are employed during all seasons, i.e., spring, summer, winter, and autumn. Additionally, several case studies are analyzed, representing a distinct physical arrangement of connectivity between power devices, from the most densely connected to the least connected. The results indicate that the strategic power distribution significantly reduces the total ESS size, including the primary and secondary storage systems, for each season. The optimal secondary ESS size decreased to 25.7 % for the spring season, 17.29% for the summer season, 6.79 % for the winter season, and 7.01 % for the autumn season from the least connectivity from power devices to dense connectivity. The findings highlight the seasonal variations of generation and consumption and their impact on ESS sizing while preserving the limitations and ensuring the safety of the power system. Hence, it is a novel methodology for seasonal storage sizing and strategic energy management, guaranteeing stable and resilient power system operation. Full article