Intelligent energy cyber physical systems (iECPS) for reliable smart grid against energy theft and false data injection

Jain, Harshit; Kumar, Mukul; Joshi, Amit M.

doi:10.1007/s00202-021-01380-9

Cited by 25 publications

(7 citation statements)

References 41 publications

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“…The proposed BiLSTM-LogitBoost stacking ensemble model, proposed for ETD in SGs, is evaluated and discussed in this section. Some recent benchmarks, such as SVM [19], [71], logistic regression (LR) [37], decision tree (DT) [37], LSTM [21], [71], adaptive boosting (AdaBoost) [37], BiLSTM [64], LogitBoost [65], and LSTM-AdaBoost [72] are also implemented for ETD and their results are compared with the proposed model. LogitBoost with n_estimators = 25 is employed as a benchmark technique to our proposed model.…”

Section: Discussion Of the Simulation Resultsmentioning

confidence: 99%

“…To successfully accomplish this job, different performance evaluation parameters are available, as given in study [73]. On the other hand, it is not practical to employ all of the performance metrics mentioned in the study; therefore, in this paper, we employed few metrics from them that are most relevant and widely used in the recent literature of ETD [21], [71], [74]. Hence, the proposed model's performance evaluation is done via different performance metrics.…”

Section: B Performance Measuresmentioning

confidence: 99%

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RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

et al. 2022

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Obtaining outstanding electricity theft detection (ETD) performance in the realm of advanced metering infrastructure (AMI) and smart grids (SGs) is quite difficult due to various issues. The issues include limited availability of theft data as compared to benign data, neglecting dimensionality reduction, usage of the standalone (single) electricity theft detectors, etc. These issues lead the classification techniques to low accuracy, minimum precision, low F1 score, and overfitting problems. For these reasons, it is extremely crucial to design such a novel strategy that is capable to tackle these issues and yield outstanding ETD performance. In this article, electricity theft happening in SGs is detected using a novel ETD approach. The proposed approach comprises recursive feature elimination (RFE), k nearest neighbor oversampling (KNNOR), bidirectional long short term memory (BiLSTM), and logit boosting (LogitBoost) techniques. Furthermore, three BiLSTM networks and a LogitBoost model are combined to make a BiLSTM-LogitBoost stacking ensemble model. Data preprocessing and feature selection followed by data balancing and electricity theft classification are the four major stages of the model proposed for ETD. It is obvious from the simulations performed using state grid corporation of China (SGCC)'s electricity consumption (EC) data that our proposed model achieves 96.32% precision, 94.33% F1 score, and 89.45% accuracy, which are higher than all the benchmarks employed in this study.

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Section: Discussion Of the Simulation Resultsmentioning

confidence: 99%

Section: B Performance Measuresmentioning

confidence: 99%

RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

et al. 2022

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“…The best‐suited algorithm is identified and integrated for forecasting and theft detection. A financial transaction is vulnerable to cyber threats in IoT platforms [18]. Hence, cyber‐resilient energy and financial transactions via digital watermarking are developed.…”

Section: System Overviewmentioning

confidence: 99%

A comparative analysis of deep neural network models in IoT‐based smart systems for energy prediction and theft detection

Sebastian,

Deepa,

Neelima

et al. 2023

IET Renewable Power Gen

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Traditional analog and digital meters are being substantially replaced with technological advances and the Internet of Things (IoT) introduction. The smart meter is highly preferred for accessing real‐time consumption, tariff calculation, and remote system control. These smart meters also prevent the majority from bypassing theft. Despite its intelligence, it cannot be 100% secure. An error in the readings can be caused by hacking or damage to meter components making the utility companies suffer significant losses. Based on past and future energy consumption data predictions on the consumer side, various methods are used in the proposed work to identify theft or anomalies in smart meter readings. Forecast‐based detection proved to be the most effective and accurate method. The primary and secondary decision models, which employ a variety of statical analyses to identify system anomalies, serve as the foundation for the energy consumption that follows the forecasting. Past 24‐h data is needed for forecasting, which is passed through different statical calculations such as RMSE, simple moving average, and Absolute Percentage Error to conclude detecting the normal values. Long short‐term memory gives high accuracy of 97% for forecasting and detecting abnormalities.

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“…The outcomes attained are assessed against other widespread DL methodologies namely traditional LSTM, RNN, and Gated Recurrent Units (GRU). Jain et al [14] present a new ML-based multi model predictive technique called intelligent energy CPS (iECPS) for smarter energy theft verification and detection. Moreover, the scheme has strength because verification from the user is taken into account as concluding validation for deciding further course of action.…”

Section: Related Workmentioning

confidence: 99%

Industrial Cyber Blockchain Physical System for Microgrid in Data Based Predictive Analysis for Automatic Control Analysis

Pasani¹,

Rohaeti²

2022

Int. j. commun. netw. inf. secur.

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As an efficient distributed renewable energy utilization model, a microgrid is predictable to realize the higher incorporation of the industrial cyber-physical system (CPS) that has gained significant interest in the academia and industry fields. Electric grid is now facing exceptional variations in generation and load as rising number of distributed energy resources (DERs), typically interfaced via power electronics converter, have been positioned, which possess multifaceted technical problems. In the context of electric grid, Blockchain (BC) was primarily developed for peer-to-peer energy trading through cryptocurrency. This paper presents a deep learning based predictive model for automated control analysis (DLBPM-ACS) in BC assisted industrial CPS environment. The presented DLBPM-ACS technique aims to forecast the short-term energy requirement for reducing the delivery cost of electrical energy for consumers. In addition, the presented DLBPM-ACS technique employs BC for effective energy utilization monitoring and trading control. Moreover, the presented DLBPM-ACS technique employs deep belief network (DBN) model for energy prediction process. Furthermore, the artificial ecosystem optimizer (AEO) algorithm is applied for optimal tuning of the hyperparameters related to the DBN approach. A wide range of simulations was conducted and the outcomes demonstrate the better outcomes of the DLBPM-ACS technique.

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Intelligent energy cyber physical systems (iECPS) for reliable smart grid against energy theft and false data injection

Cited by 25 publications

References 41 publications

RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

A comparative analysis of deep neural network models in IoT‐based smart systems for energy prediction and theft detection

Industrial Cyber Blockchain Physical System for Microgrid in Data Based Predictive Analysis for Automatic Control Analysis

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