Search Results (226)

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

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 Additionally, outdated public datasets fail to capture the rapid evolution of modern appliances.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

Rising household electricity consumption, driven by technological advances and increased indoor activity, has led to higher energy costs and an increased reliance on non-renewable sources, exacerbating the carbon footprint. Home energy management systems (HEMS) are positioning themselves as an efficient alternative by integrating artificial intelligence to improve their accuracy. Predictive algorithms that provide accurate data on the future behavior of energy consumption and appliance usage time are required in these HEMS to achieve this goal. This study presents a predictive model based on recurrent neural networks with long short-term memory (LSTM), known to capture nonlinear relationships and long-term dependencies in time series data. The model predicts individual and total household energy consumption and appliance usage time. Training data were collected for 12 months from an HEMS installed in a typical Colombian house, using smart meters developed in this research. The model’s performance is evaluated using the mean squared error (MSE), reaching a value of 0.0168 kWh². The results confirm the effectiveness of HEMS and demonstrate that the integration of LSTM-based predictive models can significantly improve energy efficiency and optimize household energy consumption. Full article

Due to the increasing volumes of plastic waste generated from electric and electronic devices, research has focused on the investigation of recycling methods for their safe handling. Pyrolysis converts plastics from waste electric and electronic equipment (WEEE) into valuable products (pyrolysis oil). Nevertheless, the frequent presence of flame retardants, mainly brominated flame retardants (BFR), hinders pyrolysis’s wide application, since hazardous compounds may be produced, limiting the use of pyrolysis oils. Taking the aforementioned into account, this work focuses on the recycling, via pyrolysis, of various plastic samples gathered from WEEE, to explore the valuable products that are formed. Specifically, 14 plastic samples were collected, including parts of computer peripheral equipment, remote controls, telephones and other household appliances. Considering the difficulties when BFRs are present, the study went one step further, applying XRF analysis to identify their possible presence, and then Soxhlet extraction as an environmentally friendly method for the debromination of the samples. Based on the XRF results, it was found that 23% of the samples contained bromine. After each Soxhlet extraction, bromine was reduced, achieving a complete removal in the case of a remote control sample and when butanol was the solvent. Thermal pyrolysis led to the formation of valuable products, including the monomer styrene and other secondary useful compounds, such as alpha-methylstyrene. The FTIR results, in combination with the pyrolysis products, enabled the identification of the polymers present in the samples. Most of them were ABS or HIPS, while only three samples were PC. 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

Introduction: The use of electrical appliances using extremely low frequency (ELF) electromagnetic fields (EMF) has increased in the past few years. These ELF MF are reported to be linked to several adverse health effects. However, only a couple of studies have been conducted on the association between risk of tumours and use of electronic devices using low frequency (LF) EMF. Methods: We studied the use of common household electrical appliances and suspected risk of tumours in a multi-hospital-based case-control study. In total, 316 patients were included in the final analysis. Results: The study results showed a below unity risk for most of the devices. A slight increased risk of tumour was observed for computer screen use OR: 1.13 (95% CI: 0.43–3.02) and use of microwave oven OR: 1.21 (95% CI: 0.36–4.04). We also had chance to investigate ELF MFs exposure association with tumour. Where we observed elevated odd ratios in individuals living near electricity transformer stations, with a statistically significant risk OR: 2.16 (95% CI: 1.30–3.59). However, the risk was below unity (OR: 0.98) in individuals residing close to powerlines. Conclusion: The current study serves as a pilot study of primary data and will be helpful in future epidemiological research studies on the topic in the region. Full article

With the development of society, energy application and building thermal comfort in rural residences are receiving more and more attention. The rural residences in this survey mainly cover the rural areas of 21 prefectures in Guangdong province, of which 24.7% are in the Pearl River Delta, 18.9% in western Guangdong, 13.1% in eastern Guangdong, and 43.2% in northern Guangdong. Rural household energy consumption is mainly used for lighting equipment, household appliances, and cooking equipment, where lighting equipment and household appliances mainly consume electrical energy, and cooking equipment consumes different types of energy due to the diversity of types. First, there is a wide variety and variation in rural energy consumption, with electricity and liquefied petroleum gas as the main sources of cooking energy. Hot water is mainly obtained by heating with electricity and natural gas. Secondly, for rural residents, renewable energy is too expensive to build, is also affected by the environment and weather, and is often not convenient to use. Third, rural residents generally experience a warm, humid indoor environment with adequate airflow, but poor kitchen ventilation reduces air quality satisfaction. To enhance renewable energy adoption, technological advancements and cost reductions are necessary, along with increased government efforts in awareness campaigns, policy incentives, and demonstration projects. This study analyses the rural energy structure in Guangdong, proposes the direction of rural energy optimization, and analyses rural energy use and the feasibility of renewable energy promotion, considering the population and income of rural households. 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

In complex electromagnetic environments, cardiac pacemakers may be interfered with easily. Microwave ovens, as common household appliances, may display electromagnetic leakage, which may pose risks to pacemaker wearers. This work evaluates the electromagnetic exposure of pacemaker wearers under various conditions. One involves different distances from the microwave oven to the human body, and the other involves a distinct oven door gap. This work uses COMSOL Multiphysics to establish a human thoracic cavity model with a heart and unipolar pacemaker, as well as a model of a microwave oven with contact-type doors. The results show that the specific absorption rate (SAR_10g) and temperature increase in the thoracic cavity and heart tissue are inversely proportional to the distance from the microwave source. They are directly proportional to the oven door gap size. The induced electric field intensity, the temperature increase, and the induced voltage in the pacemaker show the same trend. When the human body is closest to the microwave oven with the largest door gap (D = 100 mm, d = 0.3 mm), the SAR_10g and temperature increase of the thoracic cavity and heart tissue reach their maximum values, which are significantly below the safety standards recommended by ICNIRP. Similarly, the maximum value of the temperature increase and the induced electric field intensity in the pacemaker are below the safety standard recommended by ISO 14708-3 (+2 °C) and IEC 60601-1-2 (28 V/m). The maximum induced voltage at the pacemaker electrode is 5.322 mV, which exceeds the sensing sensitivity setting recommended by ISO 14117 (2 mV) for unipolar pacemakers. These findings demonstrate that microwave ovens with contact-type doors electromagnetic radiation do not threaten human health under normal usage conditions. However, the maximum value of the induced voltage exceeds the sensing sensitivity of some unipolar pacemakers, which may affect the operation of the unipolar pacemaker. This phenomenon requires attention from clinicians and patients. We still recommend that pacemaker wearers keep a distance from microwave ovens when using them. 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

Standby power consumption in household electrical and electronic equipment remains a persistent source of energy waste worldwide. Despite regulatory measures and ongoing technological developments, a considerable amount of electricity is still consumed by devices in standby or “off-mode”, resulting in higher utility costs and carbon emissions. This review synthesizes the latest research to clarify the scale of standby energy consumption, discusses relevant policies and regulations, and explores intelligent technologies and behavioral strategies that minimize energy consumption. Starting from the theoretical analysis and modeling of equipment consumption in standby mode to the implementation of intelligent systems to reduce it, the paper highlights heuristic optimization methods, smart grid integration, and occupant-centered interventions, all of which demonstrate tangible energy savings. This research was carried out in close connection with current policies regarding energy consumption and sustainable development, respectively, with the implementation of new technologies. Thus, in accordance with the latest European directives, the intelligent systems used have reduced the energy consumption of some common household appliances by 26.68 kWh. Additionally, knowledge gaps, particularly regarding user behavior, data granularity, and the integration of advanced analytics that limit the efficacy of current solutions, are identified. Recommendations for future research, emphasizing the importance of harmonized policies, precise data measurement, and artificial-intelligence-driven approaches for further reducing standby loads, are finally presented. Full article

Smart cities embody a transformative approach to modernizing urban infrastructure and harness the power of deep learning (DL) and Vehicle-to-Home (V2H) technology to redefine home energy management. Neural network-based Q-learning algorithms optimize the scheduling of household appliances and the management of energy storage systems, including batteries, to maximize energy efficiency. Data preprocessing techniques, such as normalization, standardization, and missing value imputation, are applied to ensure that the data used for decision making are accurate and reliable. V2H technology allows for efficient energy exchange between electric vehicles (EVs) and homes, enabling EVs to act as both energy storage and supply sources, thus improving overall energy consumption and reducing reliance on the grid. Real-time data from photovoltaic (PV) systems are integrated, providing valuable inputs that further refine energy management decisions and align them with current solar energy availability. The system also incorporates battery storage (BS), which is critical in optimizing energy usage during peak demand periods and providing backup power during grid outages, enhancing energy reliability and sustainability. By utilizing data from a Tunisian weather database, smart cities significantly reduce electricity costs compared to traditional energy management methods, such as Dynamic Programming (DP), Rule-Based Systems, and Genetic Algorithms. The system’s performance is validated through robust AI models, performance metrics, and simulation scenarios, which test the system’s effectiveness under various energy demand patterns and changing weather conditions. These simulations demonstrate the system’s ability to adapt to different operational environments. Full article

Domestic electrical fires can occur for a variety of reasons, including faulty wiring and plugs, overloaded circuits, and malfunctioning electrical appliances. In this article, the circumstances of domestic electrical fire injuries between 2011 and 2022 in the county of Merseyside in Northwestern England were examined in order to inform fire prevention activities. Householder carelessness appeared to be less of a factor in electrical fire injury compared to other types of fire injury such as cooking or smoking fire injury. Faulty electricity supplies were the main cause of electrical fire injuries. Male residents were slightly more likely to sustain injury in an electrical fire in comparison to females (1.25 to 1). Those aged 75+ appeared to be more at risk of electrical fire injuries compared to other age groups. Full article

Non-intrusive load disaggregation is a technique that monitors the total electrical load of an entire building or household. It uses a single power metering device to measure the total load. Then, it employs algorithms to break it down into the individual usage of different electrical devices. To address issues in load disaggregation models such as long training times, feature interference caused by the activation of other loads, and accuracy deficiencies caused by behavioral interference from users’ electricity usage habits, this paper proposes a VMD–Nyströmformer–BiTCN network architecture. The variational mode decomposition (VMD) filters the raw power data, reducing errors caused by noise and enhancing the accuracy of decomposing the load. A deep learning network utilizes a modified attention model, Nyströmformer, to reduce feature entanglement and accuracy degradation caused by habitual behavior interference during load disaggregation, while ensuring precise accuracy and improving network operational speed. The training network uses a bidirectional temporal convolutional network (BiTCN) and incorporates a residual network to expand the receptive field, allowing it to receive longer load sequence data and acquire more effective load information, thereby improving the disaggregation effectiveness for target appliances. Full article

This paper presents a novel machine learning framework useful for optimizing energy consumption in households. Home appliances have a great potential to optimize electricity consumption by mitigating peaks in the grid load or peaks in renewable energy generation. However, such functionality of home appliances requires their users to change their behavior regarding energy consumption. One of the criteria that could encourage electricity users to change their behavior is the cost of energy. The introduction of dynamic energy prices can significantly increase energy costs for unsuspecting consumers. In order to be able to make the right decisions about the process of electricity use in households, an algorithm based on machine learning is proposed. The presented proposal for optimizing electricity consumption takes into account dynamic changes in energy prices, energy production from renewable energy sources, and home appliances that can participate in the energy optimization process. The proposed model uses data from smart meters and dynamic price information to generate personalized recommendations tailored to individual households. The algorithm, based on machine learning and historical household behavior data, calculates a metric to determine whether to send a notification (message) to the user. This notification may suggest increasing or decreasing energy consumption at a specific time, or may inform the user about potential cost fluctuations in the upcoming hours. This will allow energy users to use energy more consciously or to set priorities in home energy management systems (HEMS). This is a different approach than in previous publications, where the main goal of optimizing energy consumption was to optimize the operation of the power system while taking into account the profits of energy suppliers. The proposed algorithms can be implemented either in HEMS or smart energy meters. In this work, simulations of the application of machine learning with different characteristics were carried out in the MATLAB program. An analysis of machine learning algorithms for different input data and amounts of data and the characteristic features of models is presented. Full article