A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

Electricity theft (ET) causes major revenue loss in power utilities. It reduces the quality of supply, raises production cost, causes legal consumers to pay the higher cost, and impacts the economy as a whole. In this article, we use the State Grid Corporation of China (SGCC) dataset, which contains electricity consumption data of 1035 days for two classes: normal and fraudulent. In this work, ET detection model is proposed that consists of four steps: interpolation, data balancing, feature extraction, and classification. First, missing values of the dataset are recovered using the interpolation method. Second, resampling technique is implemented. ET consumers are 9% in the SGCC dataset that make the model inefficient to correctly classify both classes (normal and theft). A hybrid resampling technique is proposed, named synthetic minority oversampling technique with near miss. Third, residual network extracts the latent features from the SGCC dataset. Fourth, three tree based classifiers, such as decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost) are applied to train the encoded feature vectors for classification. Besides, search for good hyperparameters is a challenging task, which is usually done manually and takes a considerable amount of time. To resolve this problem, Bayesian optimizer is used to simplify the tuning process of DT, RF, and AdaBoost. Finally, the results indicate that RF outperforms DT and AdaBoost.

Section: Related Workmentioning

confidence: 99%

Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

Arif

Javaid

Aldegheishem

et al. 2021

“…Further, many other studies focus on machine learning approaches for abnormality detection in different application areas such as energy consumption 32 . Some of these approaches devised to detect energy consumption anomalies based on extracting micro‐moment features use a rule‐based model 33 and deep neural networks 34 …”

Section: Related Workmentioning

confidence: 99%

An efficient approach for detecting anomalous events in real‐time weather datasets

Arora

Rani

Saxena

2021

Event detection in real‐time is applied in diverse domains such as detection of fraudulent activities in commercial transactions, detection of faulty systems in industries, and so forth. Businesses and organizations benefit from the actionable information obtained through various techniques available for anomalous event detection. Real‐time event detection is nowadays handled through streaming data frameworks. Traditional approaches effectively handle event detection in real‐time but with more false positives, thus, resulting in false alarms. In this article, an efficient approach comprising two components, an offline model and an online event detection pipeline, is proposed to achieve minimum mean absolute error (MAE). An offline module is developed to investigate a variety of deep learning models that prove suitable for event detection in real‐time. The experiments conducted with PubNub sensors datasets demonstrate that the long short‐term memory unit of recurrent neural networks is the best suitable model for anomalous event detection. The online pipeline module is built using streaming data frameworks to predict the abnormal peaks. It is revealed through the experimental results that the proposed approach efficiently detects anomalous events in real‐time and also eliminates false positives.

“…A new solution to detect energy consumption anomalies is proposed by extracting micro‐moment features using a rule‐based model to analyze and detect abnormal consumption of power. An anomaly visualization technique known as the scatter representation is portrayed the micro‐moment classes for better understanding of the behavior 23 . Comprehensive taxonomy is used to classify existing algorithms based on different modules and parameters adopted.…”

Section: Related Workmentioning

confidence: 99%

Real time identification of anomalous events in coastal regions using deep learning techniques

Varalakshmi

Murugaiyan²,

Ravichandran

et al. 2021

Summary The present existing weather prediction systems deployed by weather forecasting agencies predict the anomalous events using numerical simulation models developed based on geophysics and oceanographic theories. The major disadvantage with existing models is that are explicit simulation models. So, we try to build a real time prediction system that will automatically predict the intensity of any marine natural disaster events and alert the authorities to take necessary actions using a satellite and observational real time data set including moored met ocean data. We propose a novel hybrid model which is a combination of Convolutional Neural Network (CNN) and Long Short‐Term Memory (LSTM). The paper analyzes various other models such as Gated Recurrent Unit (GRU), Bidirectional LSTM (Bi‐LSTM) Recurrent Neural Network (RNN), and Extreme Learning Machine. In addition the other standard CNN architectures are also analyzed. Since the residual CNN network is the most effective architecture, the paper proposes a modification of the residual CNN network by combining the residual CNN network with LSTM/GRU RNN network along with a proposed new activation function which was experimentally proved to be more effective than the deep learning community standard activation function ReLU. The proposed new activation function overcomes the dead neuron propagation problem in ReLU activation function and hence the model's accuracy is increased by more than 1%. The proposed CNN + LSTM/GRU model is benchmarked with UCR time series benchmark dataset and it is experimentally seen that the model performs better than all existing algorithms.