Electricity Usage Research Articles

Investigation on the overall performance of hydrogel-based thermochromic windows with various structures

Thermochromic (TC) windows have attracted extensive attention due to their potential for improving building performance through the variation of optical properties in response to temperature. However, past studies on TC windows at the building scale primarily focused on optimizing and evaluating the properties of TC materials, with little attention paid to the varying window structures frequently encountered in engineering applications. In this study, the building performance of hydrogel-based TC windows was investigated from the structural perspective. A comprehensive simulation framework was developed, integrating overall performance simulations of energy consumption and indoor comfort. Using a self-fabricated hydrogel TC glass and an office building as the case study, the impact of the optical properties of the substrate glass on single, double, vacuum, and ventilation TC windows was analyzed. The results indicated that the optimal extinction coefficients for the studied windows range from 55 to 105 m−1 for visible irradiation and from 155 to 255 m−1 for near-infrared irradiation. Subsequently, the overall performance improvements of these types of TC windows relative to corresponding static versions were calculated. It was found that the impacts of TC glass on these four windows varied significantly. Electricity usage was reduced by 33 ∼ 53 %, and the thermal comfort and visual comfort were enhanced by –22 ∼ 20 % and 72 ∼ 100 %, respectively. This study reveals in detail how the TC window structure affects various aspects of building performance, aiming to inspire and guide the consideration of window structure in the practical applications of TC glass.

Life cycle assessment of additively manufactured elastocaloric Ni-Ti heat pump/refrigeration components and the effect of design geometry on the environmental impact categories

Elastocaloric Ni-Ti alloys offer solid-state heat pump and cooling applications. The environmental impact of elastocaloric Ni-Ti parts manufactured via powder bed fusion was assessed using life cycle assessment with the ReCiPe method. These solid-state heat pumps can mitigate the environmental impacts of current air conditioning/refrigeration methods that use harmful chemicals like CFCs, HCFCs, and HFCs. Measurements of electricity, Ar gas, and compressed air consumption were considered within the gate-to-gate boundary, covering powder transportation, printing, and use-phase. Nickel was identified as the most damaging input, reducible through geometry optimization. Optimized geometries (given the same number of printing layers) require more energy during powder bed fusion however, the environmental benefit from material saving outweighs the damage from increased electrical usage by far. The powder bed fusion machine's standby mode consumes more energy and Ar gas than the printing process, presenting an opportunity for mitigation. Additively manufactured Ni-Ti elastocaloric structures were used as heat generators/sinks in flowing water during compression cycles, achieving 11 °C and 10 °C water temperature change per cycle for the solid geometry and the optimized geometry respectively. A 10 x 10 x 55 mm Ni-Ti structure, providing >2 °C temperature span under compressive cycles, served as the functional unit.

Household, Sociodemographic, Building and Land Cover Factors Affecting Residential Summer Electricity Consumption: A Systematic Statistical Study in Phoenix, AZ

Understanding determinants of residential electricity consumption is crucial for urban sustainability efforts for planners and policy makers to develop targeted strategies to lower energy use, reduce greenhouse gas emissions, and to increase community resilience. This study presents a systematic approach to build an interpretable multivariate linear model, addressing challenges like outlier detection, multicollinearity, non-normality, and heteroscedasticity. Using 2019 summer residential electricity data for 426 census tracts in Phoenix and 30 variables, the approach involves (1) addressing multicollinearity and regression outliers through Variance Inflation Factor and studentized residual analysis, (2) comparing an automatic variable selection method with Ridge, Lasso, and Elastic Net regression, (3) evaluating the final model, and (4) interpreting variable effects. Critical findings reveal multicollinearity in land cover and racial variables, while 21 census tracts on the urban periphery exhibit outliers with unique features. Variable selection demonstrates the significance of household and building information in influencing residential electricity consumption. Household variables alone account for 84% of electricity usage variation. Incorporating building information and land cover variables reduces errors by 35% and 26% respectively, emphasizing the significance of including household characteristics as predictors or control variables when modeling electricity consumption. A final model with 93% explanatory power enables precise predictions.

A Data-Driven Analysis of Electric Vehicle Adoption Barriers in the Philippines: Combining SEM and ANNs

As the world progresses into the peak of the Fourth Industrial Revolution, the adoption of smart and sustainable technologies, including electric vehicles (EVs), has gained significant momentum. However, the widespread acceptance of EVs is hindered by several unresolved barriers. This study investigates the factors influencing the adoption of electric vehicles in the Philippines, focusing on key barriers through an integrated approach using machine learning and structural equation modeling (SEM). Specifically, artificial neural networks (ANNs) and SEM are employed to analyze data from online surveys and the existing literature, identifying the critical obstacles that impact consumer acceptance. The findings reveal that the availability of charging stations, range anxiety, and vehicle costs are the primary deterrents to EV adoption. By incorporating a sustainability perspective, this study underscores the crucial role of electric vehicles in reducing environmental impacts and achieving carbon reduction targets. The hybrid methodology presented offers new insights to guide policymakers in promoting electric vehicle usage, thereby contributing to the global sustainable development goals.

Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series

With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building’s behavior in order to lower its usage. This requires on-site data collection by qualified professionals and specialized equipment, which represents high costs. However, multiple studies have demonstrated that it is possible to find electricity-saving strategies from the study of electricity usage, recorded in an hourly period or less, captured by smart meters. In this context, the present study applies a methodology to determine useful information on the operation and characteristics of public buildings on the Ecuadorian coast based on the data gathered over a period of five consecutive months from smart meters. The methodology consists of four steps: (1) data cleaning and filling, (2) time-series decomposition, (3) the generation of consumption profile and (4) the identification of the temperature influence. According to the results, the pre-cooling of spaces accounts for 5% of all electricity used in the commercial buildings, while prolonged shutdown uses 10%. Approximately USD 1100 per month would be spent on the main building and USD 78 on the agency as a result.

How grid reinforcement costs differ by the income of electric vehicle users

The simultaneous charging of many electric vehicles in future mobility scenarios may lead to peaks and overloads threatening grid stability. The necessary infrastructure investments vary by the number and model type of vehicles driven and the residents’ charging preferences. These attributes significantly depend on socio-economic factors such as income. Using power flow simulations based on real-life driving profiles, we predict massive cost asymmetries with an investment demand up to 33-fold in higher-income compared to lower-income neighborhoods. Many grid operators may redistribute these costs through an across-the-board electricity price increase for all households. In times of rising electricity prices, these unwanted inequitable costing allocations could lead to severe challenges and energy poverty. Policymakers should consider countermeasures like dynamic electricity pricing schemes, income-based electric vehicle subsidies, or improved charging network access to ensure energy equity in future mobility scenarios. Our analysis of the impact of socio-economic factors on electric vehicle grid infrastructure and their quantification contributes to the energy equity discussion.

Indicadores de rendimiento energético y ambiental en instalaciones petroleras de producción en Ecuador

The study evaluates the energy and environmental performance of Ecuador's upstream oil facilities between 2015 and 2020, focusing on major extraction blocks. By analyzing data on oil, gas, and water production, fossil fuel consumption, and electricity usage, key performance indicators were developed to assess efficiency and environmental impact. The findings revealed disparities in energy efficiency, with Block 43-ITT being the most efficient at 7.82 kWh per barrel, while Block 57-LB was the least efficient at 31.41 kWh per barrel. The study emphasizes that maturing oil fields require more energy and emit more greenhouse gases, underlining the necessity for sustainable energy practices. EP Petroecuador’s initiatives, such as substituting fuel and integrating renewable electricity, led to a reduction of approximately 540 kTons of CO2 emissions by replacing 163.32 million gallons of diesel with low-carbon fuels and renewable electricity. These findings serve as a baseline for optimizing energy use and reducing emissions, offering valuable insights for policymakers and industry leaders to adopt data-driven strategies that enhance energy efficiency and minimize environmental impact in Ecuador's oil sector.

Environmental Footprints of Yogurt Production: A Case Study of a Small-Scale Manufacturing Process in Sri Lanka

This study investigated the life cycle environmental impacts of yogurt production from cradle to gate at the Pulathisi milk factory in Polonnaruwa, Sri Lanka. The functional unit considered was one kilogram of yogurt produced in the factory using existing technology. The defined system boundary encompassed raw milk production and transportation, machinery fabrication, standardization of milk, and standard unit operations of yogurt production. Material and energy usage data were collected through a series of observations, literature and Ecoinvent 3.7.1 databases. The ReCiPe 2008 method was employed for impact analysis, considering eighteen impact categories with SimaPro software. The inventory analysis included material consumption for machinery fabrication, energy consumption, and emissions for each unit operation in yogurt production. Results were characterized using midpoint indicators, and normalized impacts were calculated. The characterized outcomes of the yogurt production process contributed 2.48 kgCO2eq to climate change, 0.0297 kgSO2eq to acidification, 0.0002 kgPeq to eutrophication, and 6.9199 kg 1,4-DBeq to human toxicity. The study identified hotspots, emphasizing the significant contributions of cow milk production and electricity usage to environmental impacts. Potential mitigations, such as improved waste management in dairy farming and switching to renewable energy sources, are proposed to address these impacts. The normalized impacts provided insights for decision-makers and stakeholders to compare environmental performances and guide sustainable practices in yogurt production. The total impact was recorded as 0.014-person equivalent per year per one kilogram of yogurt.

INFORMATION SYSTEM OF STREET LIGHTING CONTROL IN A SMART CITY

Context. In the context of the rapid development of technologies and the implementation of the concept of smart cities, smart lighting becomes a key element of a sustainable and efficient urban environment. The research covers the analysis of aspects of the use of sensors, intelligent lighting control systems with the help of modern information technologies, in particular such as the Internet of Things. The use of such technologies makes it possible to automate the regulation of lighting intensity depending on external conditions, the movement of people or the time of a day. This contributes to the efficient use of electricity and the reduction of emissions into the atmosphere. Objective. The purpose of the paper is to analyze the procedures for creating an information system as a tool for monitoring and evaluating the level of illumination in a smart city with the aim of improving energy efficiency, safety, comfort and effective lighting management. The implementation of a smart lighting system for Lviv will help improve energy efficiency and community safety. Method. A content analysis of scientific publications was carried out, in which the results of research on the creation of street lighting monitoring systems in real urban environments were presented. The collection and analysis of data on street lighting in the city, such as energy consumption, illumination level, lamp operation schedules, and others, was carried out. Machine learning methods were used to analyze data and predict lighting needs. Using the UML methodology, the conceptual model of the street lighting monitoring information system was developed based on the identified needs and requirements. Results. The role of data processing technologies in creating effective lighting management strategies for optimal use of resources and meeting the needs of citizens is highlighted. The study draws attention to the challenges and opportunities of implementing smart lighting in cities, maximizing the positive impact of smart lighting on modern urban environments. The peculiarities of the development and use of an information system for controlling street lighting in a smart city are analyzed. The potential advantages and limitations of using the developed system are determined. Conclusions. The project on the creation of an information system designed to provide an energy-efficient lighting system in a smart city will contribute to increasing security, particularly, ensuring the safety of the community through integration with security systems, reducing energy consumption, through minimizing the electricity usage in periods when the need for lighting is not necessary. It has been determined that to implement an information system for remote monitoring and lighting control in a smart city, it is advisable to consider the possibility of using a complex lighting control system. Calculations were made on the example of Lviv for the city’s lighting needs. The use of motion sensors to determine the need to turn on lighting was analyzed. A conceptual model of the information system was developed using the object-oriented methodology of the UML notation. The main functionality of the information system is defined.

Electricity Bill Savings from Reduced Household Energy Consumption in Apartment Complexes

Apartments account for 64.6% of all housing units in the Republic of Korea, and most of them receive electricity under a contract, which includes a progressive rate plan. Recently, due to the electrification of energy used in homes and the growing adoption of electric vehicles, electricity consumption in apartment complexes has been gradually increasing. Given the characteristics of the progressive rate system, an increase in electricity usage results in a significant higher rise in electricity bills. Thus, an effective alternative is required to reduce electricity bills for each household. In this paper, the savings in electricity bills achieved by reducing household electricity usage are analyzed from both apartment complex and individual household perspectives, using metering data from 13,332 households. When households are sorted by the amount of savings in descending order, the resulting values are found to follow a negative exponential curve. This indicates that the benefits from reducing electricity usage in households with higher saving are significantly larger compared to other ones. We analyzed bill savings when electricity usage reductions were selectively applied to the top 10%, 20%, and 30% of households with the largest savings. From the results, it is found that the largest savings in electricity bills for households are achieved when usage reductions are applied to the top 10% of households. It is expected that this amount of savings would encourage these households to reduce their electricity consumption. Additionally, it is found that the savings for apartment complexes and the total savings for selected households are not the same, resulting in changes in the bills for households that do not reduce their usage. From the results, it was observed that when the usage reduction of selected households is small or the proportion of households reducing usage is low, the common area charges for non-reducing households tend to increase, leading to higher electricity bills. On the contrary, when the usage reduction of selected households is large or the proportion of households reducing usage is high, the common area charges for non-reducing households tend to decrease, resulting in lower electricity bills.

A clustering method for charging behaviors of electric vehicle users based on an improved k-means algorithm

Abstract The uncertainty of Electric vehicle users’ (EVUs) charging behaviors affect the power grid safety. Therefore, mining the charging behavior characteristics of EVUs can help the power grid to make optimal operation plans previously to smooth the load curve and improve stability. In this regard, a clustering strategy for EVUs is proposed, using an improved K-means algorithm based on the minimum variance theory(MVT). Additionally, the Monte Carlo algorithm is utilized to obtain the simulation results of EVUs. Finally, a numerical simulation based on actual data in China is established to validate the effectiveness and reliability of the proposed cluster algorithm. The results indicate that the proposed cluster algorithm can accurately extract the characteristics of EVUs’ off-on grid behaviors and obtain the accurate daily charging demand representing the main charging rules among EVUs. The proposed algorithm results in a reduction of 69.2% of solution time compared to the traditional K-means algorithm.

Precision Prediction of Household Electricity Consumption Through Data- Driven Model

An effective strategy for managing energy and sustainability is the accurate forecasting of household electricity consumption. A new challenge arises in consumption patterns for traditional models, which face difficulties in variability and data variety. This study aims to bridge the gap by proposing a novel technique called the Mountain Gazelle optimizer-driven Malleable Random Forest technique (MG-MRF), for improving electricity consumption prediction. This has enabled MG-MRF to model different consumption patterns as well as manage variability in the data. The study collected extensive datasets from different households, and those datasets had to undergo preprocessing to ensure integrity. Evaluation results of the approach further underscore the potential of MG-MRF to give accurate and dependable predictions, consequently allowing informed decision-making for the consumption of energy. The proposed method outperformed the traditional models with a prediction accuracy of 98.2%, precision of 94%, recall of 90%, and an f1-score of 92%. This study emphasizes the importance of adaptive modeling techniques in understanding and predicting household electricity usage, enabling the development of more effective energy management strategies. The experimental results advocate and contribute to sustainable energy practices by raising consumer awareness regarding their electrical consumption.

Scrupulous analysis of the carbon footprint of sustainable electricity produced from raw bagasse, torrefied bagasse, and filter cake pellets in Thailand

This study evaluates the carbon footprint of a 25 MW biomass power plant in a sugar factory, focusing on its 8 MW grid supply. Six key activities were assessed: fuel preparation, combustion, power generation, particulate removal, and water production (soft and cooling). The research compares greenhouse gas (GHG) emission from three scenarios: 100 % raw bagasse (RB), RB mixed with torrefied bagasse (TB), and RB with filter cake pellets (FP) at ratios of 95:5, 97:3, and 99:1. Data collected throughout 2023 revealed that the plant consumed 119,769.89 tons of RB (51 % moisture, 7740.40 kJ/kg heating value), generating 31,552,711.00 kWh annually. Emissions were calculated in kg CO2eq/kWh using the 2006 IPCC Guideline (The Intergovernmental Panel on Climate Change). Results showed that 100 % RB emitted (0.184+0.006, −0.005) kg CO2eq/kWh. Incorporating TB reduced emissions to 5 % = (0.178±0.005), 3 % = (0.178+0.006, −0.005) and 1 % = (0.178+0.006, −0.005) kg CO2eq/kWh due to decreased diesel oil, electricity, and steam usage in fuel preparation. Remarkably, using FP further decreased emissions, with 5 % FP resulting in negative total GHG emissions of (-0.225+0.015, −0.017) kg CO2eq/kWh, primarily due to the FP preparation process (-15,096,234.40±56,716.07 kg CO2eq/year). The study concludes that combining RB with TB or FP significantly reduces emissions and increases electricity production 5 % torrefied bagasse decreased 0.30 %, 3 % torrefied bagasse decreased 1.34 % and 1 % torrefied bagasse decreased 2.41 %, 5 % pelletized filter cake decreased 229.95 %, 3 % pelletized filter cake decreased 139.77 % and 1 % pelletized filter cake decreased 48.77 % by compare raw bagasse 100 %, promoting sustainable energy generation. These findings encourage the use of biomass for sustainable and environmentally advantageous power production.

Two-level integrated verification evaluation method based on comprehensive weighted assessment in multiple scenarios

The complexity and diversity brought by the distributed architecture of the new generation electric information collection system have deepened the difficulty of constructing evaluation verification index systems and quality evaluation models. Moreover, the presence of differentiated components has made fair and scientific verification challenging. Therefore, leveraging graph neural networks and siamese networks, a integrated construction quality evaluation system based on comprehensive weighted assessment in multiple scenarios was developed. Firstly, a graph neural network was constructed based on terminal data of the branches electricity usage information collection system and the link topology structure. Subsequently, this network was deployed on the headquarters side to directly acquire terminal data and generate mirror network input from the branches data, enabling real-time acquisition of various system operational indicators. Finally, the similarity between the headquarters and branches data was calculated using siamese networks to compute accuracy compensation weights for checking and evaluating various indicators, thereby obtaining comprehensive weighted quality evaluation indicators of the branches new generation electric information collection system. We use three types of services including electricity data collection, load forecasting, and task scheduling as experimental scenarios. The results showed that the multidimensional comprehensive weighted quality assessment combined with accuracy compensation obtained from the siamese network resulted in business construction quality assessment values of 97.92%, 95.95%, and 99.96% in branch. This value is approximately equal to the quality evaluation value of manual work, so the method can effectively verify the construction quality of new systems in the branch.

Heatwaves, Hospitals, and Health System Resilience in England: A Qualitative Assessment (Summer 2019)

Abstract Background Frequent heatwaves challenge healthcare delivery in England, affecting patient safety and staff welfare. This study addresses the observational gap in how healthcare settings adapt to extreme weather. Methods We utilised a qualitative approach, conducting semi-structured interviews with fourteen NHS staff across England to explore experiences during the 2019 summer heatwave. Participants included clinical and non-clinical personnel, providing broad insights into operational challenges and adaptive strategies. Results The 2019 heatwave significantly disrupted service delivery, with variable staff awareness of the Heatwave Plan for England affecting responses. Key issues included managing infection control versus electric fan usage, impacting patient and staff comfort and safety. These conditions also led to an increase in hospital admissions. Conclusions The findings highlight the need for improved workforce training and strategic planning to enhance resilience to heat-related risks. Further research is essential to develop a comprehensive evidence base on the impacts and costs of heatwaves and to assess the effectiveness of adaptation strategies. This will support national health adaptation planning and emergency response. Key messages • Enhanced planning and training are crucial for health system resilience to heatwaves. • More research is needed to understand heatwave impacts and effective responses.

Implementation Of IoT-Based Sense Plug Design On The Prototype Of The FTTH Network Of The UPI Campus Transmission Laboratory In Purwakarta

The increasing demand for energy presents challenges in efficient energy management. This study aims to develop and implement an Internet of Things (IoT)-based energy monitoring system called SENSE Plug, integrated with a Fiber To The Home (FTTH) network at the Universitas Pendidikan Indonesia campus laboratory. The system is designed to monitor real-time electricity consumption, detect voltage anomalies, and manage the electrical usage of connected electronic devices. The research uses an experimental approach by designing and testing the system in the laboratory. Data was collected using the PZEM-004T sensor connected to the NodeMCU ESP8266 microcontroller, with the measurements displayed on an OLED screen and sent to the IoT Cloud for remote monitoring. The system was tested on various electronic devices to measure voltage, current, active power, and power factor. The results indicate that the SENSE Plug system achieved high accuracy, with an average error of 0.44% for voltage and 0.41% for current. The system can also detect voltage anomalies and automatically disconnect the power supply to prevent damage to devices. In conclusion, SENSE Plug contributes to improving energy efficiency and offers a solution applicable to smart labs and other building environments

Optimizing Compressed Air Operations for Electrical Energy Savings: A Case Study in Pharmaceutical Packaging Manufacturing

This study in pharmaceutical packaging manufacturing focuses on improving compressed air efficiency through targeted strategies at both the source and user levels by establishing a baseline to analyze energy consumption patterns. Key measures, including minimizing air leaks, adjusting pressure, and optimizing compressor performance, aim to achieve a 20-50% increase in efficiency, thereby supporting environmental sustainability. The User Point and Source Point approaches are expected to lower Specific Power Consumption (SPC), with data collected from December 2020 to May 2022 providing insights into potential energy savings. Establishing this baseline, based on machine runtime and productivity, offers a solid foundation for evaluation. Results show a 23% reduction in compressor electricity usage and a 7-8% decrease in compressed air consumption. A structured improvement process and strong collaboration between engineering and management are essential for enhancing productivity and achieving sustainable energy efficiency in the industrial sector.

Sustainable Horizons: Exploring Attitudes and Beliefs on Electric Aircraft Usage in General Aviation

This research was conducted to better understand the perceptions surrounding electrically powered aircraft in the general aviation (GA) sector. The study employed survey methodologies to collect responses from ( N = 780) participants, with an emphasis on data from collegiate aviation students as well as a broad subset of the GA community. The study focused on factors such as generational differences, gender, the impact of flight training, and prior exposure to electric or hybrid cars, and how these factors influence the aviation industry’s sustainable transformation. The results indicate a significant and positive correlation between attitudes toward electric aircraft and prior exposure to or ownership of electric or hybrid cars. Generational differences, gender-based variations, and the impact of flight training on willingness to pay for electric aircraft utilization also had significant relationships with attitudes toward electric aircraft. The findings provide valuable insights for industry stakeholders and policymakers in promoting sustainable aviation practices, and complements prior research into the feasibility of electric flight.

Natural resource rent, green finance, and CO2 emissions from the industrial sector

This study investigates the effect of natural resource rents and green finance on industrial sustainability in 20 OECD countries from 2010 to 2020, using the CCEMG-ARDL approach. Findings reveal that a 1% increase in green finance reduces CO2 emissions by 0.43% in the short term and 0.27% in the long term, highlighting its role in supporting environmentally friendly investments. In contrast, a 1% rise in fossil fuel rents increases CO2 emissions by 0.29% short-term and 0.05% long-term, showing the pollutive impact of fossil fuel profits. Additionally, a 1% increase in electricity usage raises CO2 emissions by 0.41% short-term and 0.16% long-term, stressing the need for cleaner energy. Industry-specific patents show limited alignment with sustainability goals. Policy measures should target resource rent management, boost green finance, and encourage sustainable practices to promote industrial greening.

Typical Scenario Load Identification Based on Feature Fusion and Transfer Learning

Background: The electricity demand is continuously increasing. However, various institutions, enterprises, and individuals exhibit many irregularities in their electricity usage, leading to significant wastage of electricity. To achieve effective energy management, researchers are attempting to analyze and regulate users' electricity demands by monitoring their load usage through Non- Intrusive Load Monitoring (NILM) technology. The accuracy of load identification in this technology will greatly impact the results of load monitoring. Although there are currently many articles and patents related to NILM, they utilize a large amount of computational resources and require high sampling rates from devices, yet the results are still unsatisfactory. Therefore, it is necessary to improve the accuracy of load identification in data with relatively low sampling frequencies. Objective: To improve the accuracy of load identification with low sampling frequency data, this paper proposes a typical scenario load identification method based on feature fusion and transfer learning. Methods: This method adopts the fusion of current and power factor angles to provide abundant identification information for NILM, effectively reducing the situation of single-feature overlap of different loads. By inputting the fused feature data into GoogLeNet and utilizing transfer learning for training, not only is the accuracy improved, but also the training time and the requirement for the sampling rate of training data are greatly reduced. In addition, selecting typical scenario loads can monitor loads in a targeted manner, reduce the waste of computing resources caused by irrelevant loads, and more effectively guide electricity usage strategies. Results: The proposed load identification method was tested on the low sampling frequency dataset used in this paper. It achieved an overall load identification accuracy of 94.61% across three scenarios, improving accuracy by 3% to 7% compared to other models. Conclusion: The simulation results indicate that this method achieves high load identification accuracy at low sampling frequencies. It also exhibits good generalization ability. This method not only reduces the performance requirements for monitoring equipment but also enhances monitoring efficiency.