Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology

In power systems, electrical losses can be categorized into two types, namely, Technical Losses (TLs) and Non-Technical Losses (NTLs). It has been identified that NTL is more hazardous when compared to TL, primarily due to the factors such as billing errors, faulty meters, electricity theft etc. This proves to be crucial in the power system and will result in heavy financial loss for the utility companies. To identify theft, both academia and industry, use a mechanism known as Electricity Theft Detection (ETD). However, ETD is not used efficiently because of handling high-dimensional data, overfitting issues and imbalanced data. Hence, in this paper, a means of addressing this issue using Random Under-Sampling Boosting (RUSBoost) technique and Long Short-Term Memory (LSTM) technique is proposed. Here, parameter optimization is performed using RUSBoost and abnormal electricity patterns are detected by LSTM technique. Electricity data are pre-processed in the proposed methodology, using interpolation and normalization methods. The data thus obtained are then sent to the LSTM module where feature extraction takes place. These features are then classified using RUSBoost algorithm. Based on the output simulated, it is identified that this methodology addresses several issues such as handling and overfitting of massive time series data and data imbalancing. Moreover, this technique also proves to be more efficient than several other methodologies such as Logistic Regression (LR), Convolutional Neural Network (CNN) and Support Vector Machine (SVM). A comparison is also drawn, taking into consideration the parameters such as Receiver operating characteristics, recall, precision and F1-score.

Download Full-text

LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection

Applied Sciences ◽

10.3390/app10124378 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4378 ◽

Cited By ~ 8

Author(s):

Muhammad Adil ◽

Nadeem Javaid ◽

Umar Qasim ◽

Ibrar Ullah ◽

Muhammad Shafiq ◽

...

Keyword(s):

Power Systems ◽

Time Series Data ◽

Short Term Memory ◽

Imbalanced Data ◽

Electricity Consumption ◽

Series Data ◽

Support Vector ◽

Operating Characteristics ◽

Electricity Theft ◽

Under Sampling

The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in the power system worldwide and incurs a huge revenue loss for utility companies. Electricity theft detection (ETD) is the mechanism used by industry and academia to detect electricity theft. However, due to imbalanced data, overfitting issues and the handling of high-dimensional data, the ETD cannot be applied efficiently. Therefore, this paper proposes a solution to address the above limitations. A long short-term memory (LSTM) technique is applied to detect abnormal patterns in electricity consumption data along with the bat-based random under-sampling boosting (RUSBoost) technique for parameter optimization. Our proposed system model uses the normalization and interpolation methods to pre-process the electricity data. Afterwards, the pre-processed data are fed into the LSTM module for feature extraction. Finally, the selected features are passed to the RUSBoost module for classification. The simulation results show that the proposed solution resolves the issues of data imbalancing, overfitting and the handling of massive time series data. Additionally, the proposed method outperforms the state-of-the-art techniques; i.e., support vector machine (SVM), convolutional neural network (CNN) and logistic regression (LR). Moreover, the F1-score, precision, recall and receiver operating characteristics (ROC) curve metrics are used for the comparative analysis.

Download Full-text

Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach

Energies ◽

10.3390/en12173310 ◽

2019 ◽

Vol 12 (17) ◽

pp. 3310 ◽

Cited By ~ 15

Author(s):

Md. Nazmul Hasan ◽

Rafia Nishat Toma ◽

Abdullah-Al Nahid ◽

M M Manjurul Islam ◽

Jong-Myon Kim

Keyword(s):

Smart Grid ◽

Power Consumption ◽

Smart Grids ◽

Time Series Data ◽

Short Term Memory ◽

Detection System ◽

Series Data ◽

Grid Data ◽

Minority Class ◽

Electricity Theft

Among an electricity provider’s non-technical losses, electricity theft has the most severe and dangerous effects. Fraudulent electricity consumption decreases the supply quality, increases generation load, causes legitimate consumers to pay excessive electricity bills, and affects the overall economy. The adaptation of smart grids can significantly reduce this loss through data analysis techniques. The smart grid infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning and deep learning techniques can accurately identify electricity theft users. In this paper, an electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture. CNN is a widely used technique that automates feature extraction and the classification process. Since the power consumption signature is time-series data, we were led to build a CNN-based LSTM (CNN-LSTM) model for smart grid data classification. In this work, a novel data pre-processing algorithm was also implemented to compute the missing instances in the dataset, based on the local values relative to the missing data point. Furthermore, in this dataset, the count of electricity theft users was relatively low, which could have made the model inefficient at identifying theft users. This class imbalance scenario was addressed through synthetic data generation. Finally, the results obtained indicate the proposed scheme can classify both the majority class (normal users) and the minority class (electricity theft users) with good accuracy.

Download Full-text

Resource-Efficient Pet Dog Sound Events Classification Using LSTM-FCN Based on Time-Series Data

Sensors ◽

10.3390/s18114019 ◽

2018 ◽

Vol 18 (11) ◽

pp. 4019 ◽

Cited By ~ 7

Author(s):

Yunbin Kim ◽

Jaewon Sa ◽

Yongwha Chung ◽

Daihee Park ◽

Sungju Lee

Keyword(s):

Time Series ◽

Classification Accuracy ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Support Vector ◽

Intensity Data ◽

Pet Dogs ◽

Improve Energy Efficiency ◽

Internet Of Things Technology

The use of IoT (Internet of Things) technology for the management of pet dogs left alone at home is increasing. This includes tasks such as automatic feeding, operation of play equipment, and location detection. Classification of the vocalizations of pet dogs using information from a sound sensor is an important method to analyze the behavior or emotions of dogs that are left alone. These sounds should be acquired by attaching the IoT sound sensor to the dog, and then classifying the sound events (e.g., barking, growling, howling, and whining). However, sound sensors tend to transmit large amounts of data and consume considerable amounts of power, which presents issues in the case of resource-constrained IoT sensor devices. In this paper, we propose a way to classify pet dog sound events and improve resource efficiency without significant degradation of accuracy. To achieve this, we only acquire the intensity data of sounds by using a relatively resource-efficient noise sensor. This presents issues as well, since it is difficult to achieve sufficient classification accuracy using only intensity data due to the loss of information from the sound events. To address this problem and avoid significant degradation of classification accuracy, we apply long short-term memory-fully convolutional network (LSTM-FCN), which is a deep learning method, to analyze time-series data, and exploit bicubic interpolation. Based on experimental results, the proposed method based on noise sensors (i.e., Shapelet and LSTM-FCN for time-series) was found to improve energy efficiency by 10 times without significant degradation of accuracy compared to typical methods based on sound sensors (i.e., mel-frequency cepstrum coefficient (MFCC), spectrogram, and mel-spectrum for feature extraction, and support vector machine (SVM) and k-nearest neighbor (K-NN) for classification).

Download Full-text

Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

Electronics ◽

10.3390/electronics10020151 ◽

2021 ◽

Vol 10 (2) ◽

pp. 151

Author(s):

Harold R. Chamorro ◽

Alvaro D. Orjuela-Cañón ◽

David Ganger ◽

Mattias Persson ◽

Francisco Gonzalez-Longatt ◽

...

Keyword(s):

Power Systems ◽

Time Series Data ◽

Short Term Memory ◽

Mean Squared Error ◽

Test System ◽

Data Driven ◽

Machine Learning Techniques ◽

Series Data ◽

Measurement Unit ◽

System Frequency

Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Auto-regressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time-series data in order to predict the frequency features such as Nadir. Simulated scenarios are based on the gradual inertia reduction by including non-synchronous generation into the Nordic 32 test system, whereas the PMU collected data is taken from different locations in the Nordic Power System (NPS). Several horizon-windows are experimented in order to observe an adequate margin of prediction. Scenarios considering noisy signals are also evaluated in order to provide a robustness index of predictability. Results show the proper performance of the method and the adequate level of prediction based on the Root Mean Squared Error (RMSE) index.

Download Full-text

Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods

Complexity ◽

10.1155/2020/6309272 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Zhao Yang ◽

Yifan Wang ◽

Jie Li ◽

Liming Liu ◽

Jiyang Ma ◽

...

Keyword(s):

Neural Network ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Gradient Boosting ◽

Support Vector ◽

Model Parameters ◽

Neural Network Models ◽

Flow Prediction ◽

Extreme Gradient Boosting

This study presents a combined Long Short-Term Memory and Extreme Gradient Boosting (LSTM-XGBoost) method for flight arrival flow prediction at the airport. Correlation analysis is conducted between the historic arrival flow and input features. The XGBoost method is applied to identify the relative importance of various variables. The historic time-series data of airport arrival flow and selected features are taken as input variables, and the subsequent flight arrival flow is the output variable. The model parameters are sequentially updated based on the recently collected data and the new predicting results. It is found that the prediction accuracy is greatly improved by incorporating the meteorological features. The data analysis results indicate that the developed method can characterize well the dynamics of the airport arrival flow, thereby providing satisfactory prediction results. The prediction performance is compared with benchmark methods including backpropagation neural network, LSTM neural network, support vector machine, gradient boosting regression tree, and XGBoost. The results show that the proposed LSTM-XGBoost model outperforms baseline and state-of-the-art neural network models.

Download Full-text

LSTM-Based Forecasting for Urban Construction Waste Generation

Sustainability ◽

10.3390/su12208555 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8555

Author(s):

Li Huang ◽

Ting Cai ◽

Ya Zhu ◽

Yuliang Zhu ◽

Wei Wang ◽

...

Keyword(s):

Time Series ◽

Time Series Data ◽

Short Term Memory ◽

Structure Design ◽

Time Series Forecasting ◽

Series Data ◽

Support Vector ◽

Waste Generation ◽

Short Term ◽

Construction Waste

Accurate forecasts of construction waste are important for recycling the waste and formulating relevant governmental policies. Deficiencies in reliable forecasting methods and historical data hinder the prediction of this waste in long- or short-term planning. To effectively forecast construction waste, a time-series forecasting method is proposed in this study, based on a three-layer long short-term memory (LSTM) network and univariate time-series data with limited sample points. This method involves network structure design and implementation algorithms for network training and the forecasting process. Numerical experiments were performed with statistical construction waste data for Shanghai and Hong Kong. Compared with other time-series forecasting models such as ridge regression (RR), support vector regression (SVR), and back-propagation neural networks (BPNN), this paper demonstrates that the proposed LSTM-based forecasting model is effective and accurate in predicting construction waste generation.

Download Full-text

A LSTM-RNN-Assisted Vector Tracking Loop for Signal Outage Bridging

International Journal of Aerospace Engineering ◽

10.1155/2020/2975489 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Di Liu ◽

Qingyuan Xia ◽

Changhui Jiang ◽

Chaochen Wang ◽

Yuming Bo

Keyword(s):

Neural Network ◽

Urban Areas ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Support Vector ◽

Tracking Loop ◽

Navigation Solution ◽

Positioning Errors ◽

Vector Tracking

Global Navigation Satellite System (GNSS) has been the most popular tool for providing positioning, navigation, and timing (PNT) information. Some methods have been developed for enhancing the GNSS performance in signal challenging environments (urban canyon, dense foliage, signal blockage, multipath, and none-line-of-sight signals). Vector Tracking Loop (VTL) was recognized as the most promising and prospective one among these technologies, since VTL realized mutual aiding between channels. However, momentary signal blockage from part of the tracking channels affected the VTL operation and the navigation solution estimation. Moreover, insufficient available satellites employed would lead to the navigation solution errors diverging quickly over time. Short-time or temporary signal blockage was common in urban areas. Aiming to improve the VTL performance during the signal outage, in this paper, the deep learning method was employed for assisting the VTL navigation solution estimation; more specifically, a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) was employed to aid the VTL navigation filter (navigation filter was usually a Kalman filter). LSTM-RNN obtained excellent performance in time-series data processing; therefore, in this paper, the LSTM-RNN was employed to predict the navigation filter innovative sequence values during the signal outage, and then, the predicted innovative values were employed to aid the navigation filter for navigation solution estimation. The LSTM-RNN was well trained while the signal was normal, and the past innovative sequence was employed as the input of the LSTM-RNN. A simulation was designed and conducted based on an open-source Matlab GNSS software receiver; a dynamic trajectory with several temporary signal outages was designed for testing the proposed method. Compared with the conventional VTL, the LSTM-RNN-assisted VTL could keep the horizontal positioning errors within 50 meters during a signal outage. Also, conventional Support Vector Machine (SVM) and radial basis function neural network (RBF-NN) were compared with the LSTM-RNN method; LSTM-RNN-assisted VTL could maintain the positioning errors less than 20 meters during the outages, which demonstrated LSTM-RNN was superior to the SVM and RBF-NN in these applications.

Download Full-text

Early Prediction of Hemodynamic Shock in the Intensive Care Units with Deep Learning on Thermal Videos: A Retrospective Longitudinal Study (Preprint)

10.2196/preprints.35932 ◽

2021 ◽

Author(s):

Vanshika Vats ◽

Aditya Nagori ◽

Pradeep Singh ◽

Raman Dutt ◽

Harsh Bandhey ◽

...

Keyword(s):

Time Series ◽

Intensive Care ◽

Deep Learning ◽

Intensive Care Units ◽

Thermal Imaging ◽

Time Series Data ◽

Short Term Memory ◽

Imaging Modality ◽

Series Data ◽

Operating Characteristics

BACKGROUND Shock is one of the major killers in Intensive Care Units and early interventions can potentially reverse it. In this study, we advance a non-contact thermal imaging modality for continuous monitoring and prediction of hemodynamic shock in advance. OBJECTIVE We aim to monitor and predict the advent of hemodynamic shock 6 hours in advance using an automated non-contact thermal imaging decision pipeline. METHODS Thermal Videos were captured in a Pediatric ICU-setting along with vitals time-series data. Deep-learning-based body-part segmentation models were trained to extract the Center-to-Peripheral temperature value difference from the videos. Extracted time-series data along with heart rate was finally analyzed using Long-Short Term Memory models to predict the shock status up to the next 6 hours. RESULTS 103,936 frames from 406 non-contact thermal videos were recorded longitudinally upon 22 patients. Our models were able to predict the shock well till 6 hours of lead time using thermal information and achieved the best area under the receiver operating characteristics curve of 0.81±0.06 and area under the precision-recall curve of 0.78±0.05 at 5 hours, providing sufficient time to stabilize the patient. CONCLUSIONS Our approach leverages thermal imaging as a non-invasive and non-contact modality to continuously monitor hemodynamic shock, and thus, provides a reliable shock prediction using an automated decision pipeline that can provide better care and save lives. CLINICALTRIAL None

Download Full-text

Early Prediction of Hemodynamic Shock in the Intensive Care Units with Deep Learning on Thermal Videos

10.21203/rs.3.rs-464338/v1 ◽

2021 ◽

Author(s):

Vanshika Vats ◽

Aditya Nagori ◽

Pradeep Singh ◽

Raman Dutt ◽

Harsh Bandhey ◽

...

Keyword(s):

Intensive Care ◽

Deep Learning ◽

Intensive Care Units ◽

Time Series Data ◽

Short Term Memory ◽

Imaging Modality ◽

Series Data ◽

Operating Characteristics ◽

Receiver Operating Characteristics Curve ◽

Precision Recall Curve

Abstract Shock is one of the major killers in Intensive Care Units and early interventions can potentially reverse it. In this study, we advance a non-contact thermal imaging modality to continuous monitoring of hemodynamic shock working on 103,936 frames from 406 videos recorded longitudinally upon 22 patients. Deep learning was used to preprocess and extract the Center-to-Peripheral Difference (CPD) in temperature values from the videos. This time-series data along with heart rate was finally analyzed using Long-Short Term Memory models to predict the shock status up to the next 6 hours. Our models achieved the best area under the receiver operating characteristics curve of 0.81 ± 0.06 and area under the precision-recall curve of 0.78 ± 0.05 at 5 hours, providing sufficient time to stabilize the patient. Our approach, thus, provides a reliable shock prediction using an automated decision pipeline, that can provide better care and save lives.

Download Full-text

Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach

Energies ◽

10.3390/en12132538 ◽

2019 ◽

Vol 12 (13) ◽

pp. 2538 ◽

Cited By ~ 18

Author(s):

Li ◽

Wang ◽

Zhang ◽

Xin ◽

Liu

Keyword(s):

Neural Network ◽

Solar Power ◽

Time Series Data ◽

Short Term Memory ◽

Rbf Neural Network ◽

Back Propagation ◽

Series Data ◽

Support Vector ◽

Short Term ◽

Forecasting Method

The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 minutes. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.

Download Full-text