Loss Estimation Method Research Articles

Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning

PurposeClinical validation of “BrainLossNet”, a deep learning-based method for fast and robust estimation of brain volume loss (BVL) from longitudinal T1-weighted MRI, for the detection of accelerated BVL in multiple sclerosis (MS) and for the discrimination between MS patients with versus without disability progression. Materials and MethodsA longitudinal normative database of healthy controls (n=563), two mono-scanner MS cohorts (n=414, 156) and a mixed-scanner cohort acquired for various indications (n=216) were included retrospectively. Mean observation period from the baseline (BL) to the last follow-up (FU) MRI scan was 2-3 years. Expanded Disability Status Scale (EDSS) at BL and FU was available in 149 MS patients. Annual BVL was computed using BrainLossNet and Siena and then adjusted for age. Repeated-measures ANOVA and Cohen’s effect size were used to compare BrainLossNet and Siena regarding the detection of accelerated BVL and the differentiation between MS patients with versus without EDSS progression. ResultsCohen’s effect size for the differentiation of patients from healthy controls based on the age-adjusted annual BVL was larger with BrainLossNet than with Siena (MS cohort 1: 0.927 versus 0.495, MS cohort 2: 0.671 versus 0.456, mixed-scanner cohort: 0.918 versus 0.730, all p<0.001). Cohen’s effect size for the discrimination between MS patients with (n=51) versus without (n=98) EDSS progression was larger with BrainLossNet (0.503 versus 0.400, p=0.048). ConclusionBrainLossNet can provide added value in clinical routine and MS therapy trials regarding the detection of accelerated BVL in MS and the differentiation between MS patients with versus without disability progression.

Calculation of electrical energy losses in distribution networks for power supply using oil and vacuum circuit breakers

A method for approximate estimation of load losses in distribution networks from 6 to 10 kV, as well as for lower voltage networks, has been proposed. The main advantage of this method is that the root mean square current is calculated only once for a series of calculations. This approach significantly reduces the time required for calculations and improves their accuracy, as it eliminates the need for multiple determinations of the root mean square current for each individual calculation. The proposed approach contributes to a better understanding of the influence of various factors on load losses, which allows for more effective planning and management of distribution networks. This is particularly important in the context of increasing loads on electrical networks and higher demands for their reliability and efficiency. In the calculation of electrical energy losses in distribution networks from 6 to 10 kV, methods that use generalized coefficients of electrical circuits and operating modes in the form of regression equations are widely applied. The most significant and independent factors, such as the input of active energy, total line length, number of line sections, total number of transformers, and their total installed capacity, must be taken into account and are included in the calculation equation. Additionally, the components used and their regular maintenance play an important role in electrical energy losses in electrical networks. The main direction of research is the optimization of distribution network parameters, taking into account the latest technologies and materials, as well as the implementation of automated monitoring and control systems that will allow timely detection and elimination of problems associated with electrical energy losses. The proposed method for estimating load losses in distribution networks is a promising direction for the development of the energy sector, which will contribute to improving the efficiency and reliability of electrical networks, as well as reducing the costs of their operation and maintenance.

Power prediction using high-resolution SCADA data with a farm-wide deep neural network approach

Accurate loss estimation methods with a high level of temporal granularity are necessary to enable the implementation of efficient and adaptable control strategies for wind farms. Predictive models for the power of wind turbines within a wind farm are investigated using high-resolution SCADA data and deep learning methodologies. Traditional physical models offer detailed insights but are computationally expensive. Statistical models face limitations in handling wind energy variability. In this study, deep learning models are explored to capture spatial and temporal dynamics, with four models developed: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and a hybrid CNN-LSTM model. SCADA data from an offshore wind farm is preprocessed, anomalies removed, and annotated based on operational regions. The models are trained, validated, and tested, demonstrating improved accuracy over baseline methods. The hybrid model, incorporating spatial and temporal information, yields the highest predictive performance, showcasing the significance of these dimensions in wind power prediction.

AC Loss Estimation Method for REBCO Superconducting Tapes in AC+DC Fields for Field Windings in Fully Superconducting Synchronous Machine for Electric Aircraft

Abstract The estimation of AC loss in REBa2Cu3O y (REBCO; RE = rare earth, such as Y, Eu, and Gd) superconducting tapes within superconducting synchronous motors and generators for electric aircraft is imperative during the design stage phase to optimize efficiency and cooling considerations. Our previous studies clarified the AC loss characteristics, focusing on estimating the AC loss in armature windings. While the magnetic field applied to the armature windings is exclusively as an AC field, the field windings are subject to a composite field comprising both AC and DC components. Notably, the amplitude of the AC component is markedly smaller than the magnitude of DC component. This study experimentally investigated to consider the AC loss properties in field windings. The experimental results were as follows: When subjected to operational conditions of field windings within synchronous motor and generator, the AC loss increased with the DC field strength. This property is attributed to a reduction in the critical current induced by the external field. Hence, we modified the method for estimating AC loss. Subsequently, we validated the effectiveness of the modified method.

An Online Energy-Saving Control Allocation Strategy Based on Self-Updating Loss Estimation for Multi-Motor Drive Systems

In this paper, an online energy-saving control allocation strategy based on self-updating loss estimation for multi-motor drive systems is proposed, where the impact of variations in motor parameters and distribution coefficients is considered. Firstly, a drive system model for multi-motor drive systems incorporating iron loss in permanent magnet synchronous motor (PMSM) is established. Then, a self-updating PMSM loss estimation method based on dynamic torque–current mapping is proposed. The torque–current mapping is initially identified based on the conv-fusion curve, and iteratively updated by optimal estimation. Subsequently, an online control allocation method based on line search is proposed, which mitigates the adverse effects caused by variations in distribution coefficients and reduces the total motor loss. Finally, the effectiveness of the proposed strategy is verified on the hardware-in-the-loop (HIL)-based platform. The results demonstrate that the strategy effectively enhances energy efficiency while maintaining the original control performance of the system.

Theoretical calculation and simulation analysis of hydraulic joint pressure loss based on fluid dynamics

To study the pressure loss in the runner during the rotating transmission of the hydraulic joint, the local pressure loss of the hydraulic joint is simplified, and the theoretical estimation method of the pressure loss of the hydraulic joint is proposed. Based on the finite element software Comsol, the numerical analysis of the hydraulic joint is carried out to study the effect of the regularity of different fluid inlet and outlet angles and the width of the annular runner on the pressure loss of the runner. The results show that the relative numerical simulation result error of the theoretical calculation of fluid pressure loss is within 20%, and the theoretical calculation method proposed in this paper can guide the rapid evaluation of slip ring pressure loss. The pressure loss of the slip ring increases at first and then decreases gradually with the increase of the fluid inlet and outlet angle. When the angle of the fluid inlet and outlet is 30, the pressure loss of the hydraulic joint is the largest. The pressure loss of the hydraulic joint gradually decreases with the increase of the width coefficient of the runner. When the width coefficient increases to a certain value, the pressure loss reduction effect of the runner is weakened, and the optimal runner width is 0.8-1.2 times the design width of the equal section.

Undulator linear taper control at the European X-Ray Free-Electron Laser facility

Undulator tapering controls the resonance properties of the free-electron laser (FEL) amplification process. Wakefield energy losses in an undulator’s vacuum chamber are one of the factors that determine the undulator’s linear taper. While another contribution to energy losses, namely the losses due to spontaneous radiation, can be calculated analytically, estimating wakefield energy losses requires detailed knowledge of the chamber geometry and the electron beam current profile. We introduce a method for the automatic estimation of wakefield energy losses, which leverages noninvasive THz diagnostics, a current profile reconstruction algorithm enhanced with machine learning, and a recently developed analytical wakefield function for the European XFEL’s undulator beamline. The correctness of this method was validated by directly measuring wakefield-induced electron beam energy losses in the undulator section. This, in turn, enables the prediction of the optimal linear taper in the undulator. Published by the American Physical Society 2024

High Spatiotemporal Estimation of Reservoir Evaporation Water Loss by Integrating Remote-Sensing Data and the Generalized Complementary Relationship

Accurately estimating the reservoir evaporation loss is crucial for water resources management. The existing research on reservoir evaporation loss estimates primarily focuses on large spatiotemporal scales and neglects the rapid dynamic changes to reservoirs’ surface area. For reservoirs essential for frequent flood control and regular water supply, high spatiotemporal evaporation data are crucial. By integrating remote sensing and the evaporation model, this study proposes a new method for the high spatiotemporal estimation of the evaporation losses from reservoirs. The proposed method is applied to the largest artificial freshwater lake in Asia, i.e., Danjiangkou (DJK) Reservoir. The daily reservoir water surface area is extracted at a spatial resolution of 30 m during the period 2014–2018 based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). The daily evaporation rate is estimated at a spatial resolution of 100 m using the generalized complementary relationship (GCR). The results show that the water surface area of the DJK Reservoir exhibits rapid and frequent fluctuations from 2015 to 2018, with a multi-year average area of 731.9 km2 and a maximum and minimum difference of 304 km2. Significant seasonal variations are observed in both the evaporation rate and volume, with a multi-year average evaporation rate of 806 mm and evaporation volume of 595 million m3. The estimated results align well with three other independent estimates, indicating that the GCR is capable of water surface evaporation estimation. Further analysis suggests that the data resolution has a great influence on the evaporative water loss from the reservoir. The estimated mean annual evaporation volume based on the 1000 m resolution water surface area data is 14% lower than that estimated using the 30 m resolution water surface area data. This study not only provides a new method for the high spatiotemporal estimation of reservoir evaporation by integrating remote-sensing data and the GCR method but also highlights that reservoir evaporation water loss should be quantified using the volume rather than the rate and that the estimated loss is noticeably affected by the estimation spatial resolution.

Photovoltaic inverter-based quantification of snow conditions and power loss

Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.

A core loss estimation method based on improved waveform coefficient Steinmetz equation for asymmetric triangular flux density waveform

Accurate estimation of high frequency magnetic core losses under non-sinusoidal excitations is critical to the design of solid-state transformers. This paper proposes a waveform transformation principle, termed the equal derivative transformation (EDT), for piecewise linear flux density waveforms. Under the same maximum flux density, an asymmetric triangular flux density wave can be transformed into a symmetric one with a different frequency in terms of the calculation of core losses, hence the introduction of the improved waveform coefficient Steinmetz equation (iWcSE). The magnetic characteristics of three different toroidal samples (nanocrystalline alloy FT-3KL, amorphous alloy 1K101, ferrite N87) under square excitation at different duty ratios and certain frequencies are measured. Core losses predicted by the model are in good agreement with the measurement.

Enhanced marine predators algorithm optimized support vector machine for IGBT switching power loss estimation

As the use of renewable energy generation continues to grow, improving power conversion efficiency has become an urgent task. This study aims to propose a high-precision power module loss estimation method, with a focus on predicting the switching losses of insulated gate bipolar transistor (IGBT) modules, which is crucial for the reliability assessment of IGBT modules. The main objective of this study is to establish a high-precision predictive model for IGBT module switching losses to enhance the reliability and efficiency of these devices. Firstly, a dynamic characteristic test platform was established to acquire relevant data for in-depth analysis of IGBT behavior. Secondly, given the excellent performance of support vector machine (SVM) in handling both strong and small-sized datasets, SVM was chosen as the foundational model for high-precision switching loss estimation. Subsequently, an enhanced marine predatory algorithm (EMPA), known for its superior convergence precision and speed, was introduced to optimize the random parameters of SVM. Finally, a method based on the optimized EMPA-SVM was constructed for predicting IGBT switching power losses. The proposed approach was validated using dynamic characteristic test data. And it indicated that the predictive model achieved the value of R 2 exceeding 99.8% for switching losses. Additionally, the mean absolute error and root mean square error metrics of EMPA-SVM model outperformed other models. Therefore, the research results unambiguously demonstrate the significant benefits of accurately predicting IGBT switching losses in enhancing device performance.

Real-time and accurate estimation of surgical hemoglobin loss using deep learning-based medical sponges image analysis

Real-time and accurate estimation of surgical hemoglobin (Hb) loss is essential for fluid resuscitation management and evaluation of surgical techniques. In this study, we aimed to explore a novel surgical Hb loss estimation method using deep learning-based medical sponges image analysis. Whole blood samples of pre-measured Hb concentration were collected, and normal saline was added to simulate varying levels of Hb concentration. These blood samples were distributed across blank medical sponges to generate blood-soaked sponges. Eight hundred fifty-one blood-soaked sponges representing a wide range of blood dilutions were randomly divided 7:3 into a training group (n = 595) and a testing group (n = 256). A deep learning model based on the YOLOv5 network was used as the target region extraction and detection, and the three models (Feature extraction technology, ResNet-50, and SE-ResNet50) were trained to predict surgical Hb loss. Mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient (R2) value, and the Bland–Altman analysis were calculated to evaluate the predictive performance in the testing group. The deep learning model based on SE-ResNet50 could predict surgical Hb loss with the best performance (R2 = 0.99, MAE = 11.09 mg, MAPE = 8.6%) compared with other predictive models, and Bland–Altman analysis also showed a bias of 1.343 mg with narrow limits of agreement (− 29.81 to 32.5 mg) between predictive and actual Hb loss. The interactive interface was also designed to display the real-time prediction of surgical Hb loss more intuitively. Thus, it is feasible for real-time estimation of surgical Hb loss using deep learning-based medical sponges image analysis, which was helpful for clinical decisions and technical evaluation.

Limitations of current loss estimation methods and recommendations to improve the assessment of post-disaster impacts of extreme events in Poland

Abstract This article aims to identify the limitations of the method used in Poland to estimate losses caused by natural extreme phenomena [e.g., floods, flooding, heavy rainfall, strong winds, spring frosts and negative effects of wintering] and to propose recommendations that will improve our ability to assess the impact of extreme events in the country. Information about issues related to the adopted method for estimating the effects of natural extreme phenomena in the country was gathered through a survey conducted with participants in the damage and loss estimation process. This article focuses on examining the method of estimating losses applied after the occurrence of a specific event [ex-post]. The method of estimating losses and the method of collecting data on losses in Poland does not guarantee comprehensive information on the effects of natural extreme events. Limited access to reliable data increases the uncertainty of research related to assessing the impact of extreme events on the environment, society and economy. The conducted research allowed us to develop recommendations, which were divided into two groups: the near- and long-term. They include the implementation of a system for collecting data on the effects of disasters, the development of methods to assess the social and long-term impact of extreme events and the development of methods involving satellite remote sensing and airborne laser scanning.

Parameter Optimization of Magnetic Components for Phase-Shifted Full-Bridge Converters Using a Digital Twin

Due to their significant performance, Phase-Shifted Full-Bridge Converters (PSFBCs) have gained widespread adoption in medium- and high-power applications. The performance of a PSFBC is greatly influenced by its magnetic components, namely the transformer and resonance inductor. To address these challenges, this paper proposes a parameter optimization of magnetic components for PSFBCs, specifically the transformer turns ratio and resonance inductor value, to enhance conversion efficiency and minimize operational loss. A digital twin of PSFBCs enabling a more accurate loss estimation is proposed to achieve this objective. The proposed loss estimation method precisely calculates the effective and circulation intervals and the corresponding current points of the primary-side transformer current, resulting in improved accuracy. By leveraging the digital twin, the effects of transformer turns ratio and resonant inductor value on the conversion efficiency of a PSFBC can be efficiently simulated. This facilitates the parameter optimization of magnetic components, thereby minimizing operational losses across different application scenarios. This paper also designs and implements a PSFBC prototype with a rated input voltage of 380 V, output voltage of 24 V, and output current of 20 A. The experimental results show the influences of transformer turns ratio and resonant inductor value on the PSFBC and validate the proposed digital twin. The proposed parameter optimization of magnetic components is further evaluated across two application scenarios with varying utilization rates. The simulation results indicate a reduction of approximately 14% in operational loss per hour after applying the parameter optimization of magnetic components for the PSFBC used as a battery charger. The results demonstrate the effectiveness and practicality of the proposed digital twin in designing PSFBCs.

Estimating the economic loss caused by earthquake in Mainland China

In order to estimate the post-earthquake losses in Mainland China, this paper develops a set of economic loss estimation models for Mainland China based on fixed asset data and the damaging information compiled in the Mainland China composite damaging earthquake catalog (MCCDE-CAT). Considering the differences in seismicity and economic development levels across China, these estimation models are developed separately for different sub-regions. Before performing regression analysis, the Human development index (HDI) is used to normalize the economic loss ratio from earthquake occurrence to the present. The robustness and effectiveness of the loss estimation models are verified by separating the loss information in MCCDE-CAT into sample and test data. According to the loss estimation models developed and the normal distribution characteristics of the residuals between modeled and recorded loss data, the probability of different ranges of predicted earthquake economic loss can be determined. Finally, the 2021 Ms 6.4 Yangbi earthquake is taken as an example to demonstrate the application of our developed loss estimation model. The good consistency between the economic loss estimated based on post-earthquake investigations and the predicted economic loss range by our model further indicates the applicability of the model in rapid post-earthquake loss estimation. In the future, the economic loss estimation method will provide scientific guidance in post-earthquake government reconstruction decision-making and international humanitarian assistance. This method is also applicable to disaster loss estimation in any country and region in the world.

Life‐cycle benefits estimation for hybrid seismic‐resistant self‐centering braced frames

AbstractThe hybrid self‐centering braced frames (HSBFs) with shape memory alloy‐based braces (SMABs) and viscous dampers (VDs) are an emerging structural system developed for improving the seismic resilience of traditional braced frames (e.g., buckling restrained braced frames [BRBFs]) and self‐centering braced frames (SCBFs) by reducing residual inter‐story drifts (RIDs) and floor acceleration responses simultaneously. Nevertheless, the excellent performance of HSBFs is captured by introducing high initial construction costs. This paper aims to highlight the benefits of HSBFs compared with BRBFs and SCBFs by investigating the earthquake‐induced life‐cycle costs. To this end, 6‐story BRBF, SCBF, and HSBF are designed separately to achieve the same maximum inter‐story drifts under design basis earthquakes (DBE) for a fair comparison. The advanced numerical models of the considered systems were developed, and nonlinear dynamic analyses were conducted to obtain the structural seismic responses under various intensities. Monte Carlo simulation (MCS) was adopted for considering the modeling uncertainty. The life‐cycle costs were subsequently estimated based on the revised HAZUS loss estimation method. The influence of RIDs, initial construction costs of SMABs and VDs, and values of contents inside buildings were comprehensively investigated. It has been found that HSBF can achieve better performance in reducing seismic loss than BRBF and SCBF due to its excellent capability of controlling structural and nonstructural damage. SCBF may show higher seismic loss than BRBFs, given high RID limits for demolition or high building contents’ value, highlighting the importance of controlling floor acceleration responses of SCBFs. The life‐cycle benefits of BRBFs, SCBFs, and HSBFs are highly influenced by the RIDs, initial construction costs of SMABs and VDs, building contents’ values, and building's lifetime. HSBFs show better life‐cycle benefits than SCBFs when the building's lifetime is not lower than 50 years. Compared to BRBF, the life‐cycle cost effectiveness of SCBF can be only achieved when the initial cost of SMABs is lower than 1.21 times that of BRBs for the building with 50 years’ lifetime; the life‐cycle cost effectiveness of HSBF6 can be only achieved when the initial cost of SMABs or VDs is lower than 1.84 times that of BRBs for the building with 50 years’ lifetime. It has been concluded that reducing the initial costs of SMABs and VDs is critical and essential for increasing the life‐cycle economic benefits of SCBFs and HSBFs and promoting their real‐world applications.

A data-driven method for heat loss estimation from district heating service pipes using heat meter- and GIS data

District heating (DH) pipes are often older than 20 years and of unknown condition, imposing a strong demand for detailed knowledge about their current state to help cost-effective ongoing renovation and reduce water and energy losses. Here, we develop a scalable method that exploits hourly resolved remotely read heat meter data, pipe coordinates, and soil temperature measurements to continuously estimate the heat loss of service pipes. The method does not require the measured or simulated flows in the full system, making it possible to omit the usual step of establishing a complete thermohydraulic network model which is currently hindering implementation on larger scale. By exploiting open-source soil temperature data, the estimated heat loss is normalized to output the transmission coefficient, U, in W/(m∙K), allowing for direct intercomparison between pipes of different length and assessment of timely evolution across seasons. Estimated heat losses are validated by complementary techniques, using simulation of data sets and highly time-resolved data from a test area with additional measurement equipment installed. Finally, a full-scale application is demonstrated over a year on 1089 service pipes, including detection of a spontaneously emerged leakage, revealed through the model sensitivity towards the consequential changes in heat loss coefficient, U.

Techno-Economic Modelling of Solar Photovoltaic (PV) Power Transformers in South Africa

The increasing demand for electricity in South Africa has led to a rise in the deployment of solar photovoltaic systems. The integration of these systems requires the installation of power transformers to convert the generated DC power into AC power that can be fed into the grid. However, the high cost of these transformers can hinder the widespread adoption of solar PV systems. Consequently, the primary objective of this work is to introduce a novel service-lifetime loss estimation method that acknowledges the unique requirements of transformers facilitating solar photovoltaic applications. In reality, these transformers are required to facilitate a sporadic energy source, which advocates for an appropriate method to assess the cost of their service-lifetime losses and total ownership cost. Thus far, the energy elements of classical coal-power generation are inadequate, and in conformity with operational and financial considerations of solar PVs, a novel method incorporating the outcomes of a transformer facilitating a solar PV plant is proposed. The proposed approach has been benchmarked with existing methods and the distinction between proposed and existing methods is highlighted. The study further proposes various regression models for estimating the purchase price for transformers specifically intended to exclusively serve in solar PV applications, which are not available in the current South African standards. The percentage errors between the conventional method and the proposed solar PV method are relatively high, ranging from 34.66% to 44.03%. This indicates that the two methods produce significantly different results, and the proposed solar PV method may not be an accurate replacement for the conventional method.

Risk assessment and leakage prediction system of the water distribution system of Changzhou, China

ABSTRACT In order to improve the application efficiency of intelligent monitoring equipment, leakage early warning system and water loss estimation methods under flow monitoring in different water supply areas are explored. Pressure monitoring provides risk alerts regarding unusual activity in the water distribution system in an effort to lessen or prevent catastrophic pipe bursting incidents. The location of leak points can be more accurately identified with the help of hydrophones or leak noise loggers. Furthermore, on the basis of the continuous improvement of hydraulic monitoring, a technical way to predict water quality changes through hydraulic monitoring is being developed.

A combined unsupervised learning approach for electricity theft detection and loss estimation

AbstractElectricity theft is a great trouble for power companies. As the means of tampering with smart meters continue to increase, the electricity theft behaviours become more diversified and covert, which are difficult to be identified using the existing electricity theft detection method. In addition, the existing methods usually cannot estimate the economic losses caused by electricity theft. To address these issues, a combined unsupervised learning approach for electricity theft detection and loss estimation is proposed in this study. First, three anomaly measurement indexes including the mean index, fluctuation index, and trend index are proposed to capture different anomalies respectively. Then, based on historical electricity consumption data, we develop two unsupervised learning techniques including the sample‐to‐subsamples decomposition algorithm and clustering algorithm to obtain the typical ranges of index values, and the load samples whose index values are not in the typical ranges will be considered fraudulent. Furthermore, three anomaly measurement indexes are combined to judge whether the load sample is fraudulent, and the user whose most load samples are judged fraudulent will be considered as an electricity thief. Finally, an economic loss estimation method is proposed, which quantifies the losses of electricity theft. Numerical experiments are carried out based on the Irish smart meter dataset, and the results demonstrate the effectiveness and the superior performance of the proposed method compared with a series of electricity theft detection methods.