A State-of-the-Art Review of Smart Energy Systems and Their Management in a Smart Grid Environment

Muqeet, Hafiz Abdul; Liaqat, Rehan; Jamil, Mohsin; Khan, Asharf Ali

doi:10.3390/en16010472

Cited by 28 publications

(10 citation statements)

References 154 publications

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“…where, given post = [5,6,7,8], the superscript post -4 denotes the index of the post-event values of the parameter 𝛥𝑝 𝑘 . Consequently, the matrix containing the variations of all the 𝑝 𝑘 parameters named Δ𝑝 𝑘 𝜖 ℛ 4 * 4, has been obtained in (16).…”

Section: Feature Extractionmentioning

confidence: 99%

“…NILM provides energy consumption for all the target appliances by using a single measurement of their aggregate demand usually obtained from the energy meter [1]. Once the load has been disaggregated through a proper NILM technique, the disaggregated data can be used for flexibility assessment [2], load demand forecasting [3], energy management system [4][5][6], etc. Typical NILM algorithms acquire the aggregate data and detect instants of load activation events.…”

Section: Introductionmentioning

confidence: 99%

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Regularized NCA based Prominent Feature Selection for Load Identification Using Boosted Tree Classifier

Muqeet,

Liaqat,

Hussain

et al. 2024

Preprint

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Accurate and efficient energy management in residential and commercial buildings can be achieved by providing real-time monitoring of appliances and providing detailed insight to consumers about their consumption behaviors. These insights identify energy wastage and support demand-side management. Non-intrusive load monitoring provides disaggregated energy information of target appliances by observing the feature variations in aggregate demand. Nowadays, most residential appliances are non-linear and draw non-sinusoidal currents, therefore employing fundamental active and reactive power as input features results in degraded performance of load identification algorithms. To deal with this drawback, this research proposes an improved load identification method which incorporates the fundamental and harmonic characteristics of current and voltage besides the active power and reactive power features. In this work, 21 features computed by IEEE standard 1459, harmonic energy distribution, wavelet transformation, spectral flatness measures, etc. are analyzed for appliance recognition. To select the prominent features, regularized neighborhood component analysis (NCA) is applied. The selected features are then applied to boosted tree classifier (BTC). The proposed load identification approach is validated using high-frequency start-up events of appliances from WHITED and COOLL datasets. Results obtained by the proposed technique surpass various recent techniques in classifying various appliance activations.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Regularized NCA based Prominent Feature Selection for Load Identification Using Boosted Tree Classifier

Muqeet,

Liaqat,

Hussain

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It allows utilities to detect and respond to power outages more quickly and efficiently, reducing downtime and improving customer satisfaction. It also helps optimize energy resource use, reduce carbon emissions, and increase the reliability and resilience of the grid [39].…”

Section: Smart Gridmentioning

confidence: 99%

GITM: A GINI Index-Based Trust Mechanism to Mitigate and Isolate Sybil Attack in RPL-Enabled Smart Grid Advanced Metering Infrastructures

et al. 2023

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The smart grid relies on Advanced Metering Infrastructure (AMI) to function. Because of the significant packet loss and slow transmission rate of the wireless connection between smart meters in AMI, these infrastructures are considered Low-power and Lossy Networks (LLNs). The routing protocol in an AMI network is crucial for ensuring the availability and timeliness of data transfer. IPv6 Routing Protocol for Low-power and lossy networks (RPL) is an excellent routing option for the AMI communication configuration. However, it is highly at risk against many external and internal attacks, and its effectiveness may be severely diminished by Sybil assault. Different trust-based techniques have been suggested to mitigate internal attacks. However, existing trust systems have high energy consumption issues, which cause a reduction in the performance of LLNs due to complex calculations at the node level. Therefore, this paper presents a novel fog-enabled GINI index-based trust mechanism (GITM) to mitigate Sybil attacks using the forwarding behavior of legitimate member nodes. Regarding identifying and isolating Sybil assaults, our approach outperforms the state-of-the-art methods. GITM detects and isolates a more significant number of malicious network nodes compared to other techniques within a similar time frame. By using the proposed GITM framework, the Sybil attack detection rate increases by 4.48%, energy consumption reduces by 21%, and isolation latency reduces by 26.30% (concerning time). Furthermore, the end-to-end delay is merely 0.30% more in our case, and the number of control messages decreases by 28%.INDEX TERMS Smart grid, GINI index, advanced metering infrastructure, LLN, RPL, Sybil attack, trust.

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“…The data transmitted in smart grids can include the information and data of users and subscribers. Establishing a smart grid lets the producers know the actual energy needs of the consumers [23,24].…”

Section: Relevant Workmentioning

confidence: 99%

Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

Mhmood,

Ergül,

Rahebi

2023

SIViP

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Cyber attacks against Smart Grids (SG) have harmful effects. The first function of a defensive system is to provide an intelligent system to detect intrusions. The nature of attacks against smart grids is very complex, so the intrusion detection system must be able to detect complex attacks. Lack of balancing and optimization of deep learning methods are the main challenges for many intrusion detection systems. This research presents an intelligent intrusion detection system for a smart grid based on Game Theory, Swarm Intelligence, and Deep Learning (DL). First, the proposed method balances the training samples with a conditional DL technique based on Game Theory and CGAN. Secondly, the Aquila Optimizer (AO) algorithm selects features. The third step involves mapping the selected features on the dataset and coding reduced-dimension samples into RGB color images, which are used to train the VGG19 neural network. In the fourth step, the AO algorithm optimally adjusts meta-parameters to reduce the error of the VGG19 neural network. Tests performed on the NSL-KDD dataset show that the proposed method's accuracy, sensitivity, and precision in detecting attacks are 99.82%, 99.69%, and 99.76%, respectively. The CGAN method balances the dataset and increases the accuracy, sensitivity, and precision of the proposed method compared to the GAN method in detecting attacks on the smart grid. Experiments show that the proposed method more accurately detects attacks than deep learning methods such as VGG19, CNN-GRU, CNN-GRU-FL, LSTM, and CNN.

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A State-of-the-Art Review of Smart Energy Systems and Their Management in a Smart Grid Environment

Cited by 28 publications

References 154 publications

Regularized NCA based Prominent Feature Selection for Load Identification Using Boosted Tree Classifier

Regularized NCA based Prominent Feature Selection for Load Identification Using Boosted Tree Classifier

GITM: A GINI Index-Based Trust Mechanism to Mitigate and Isolate Sybil Attack in RPL-Enabled Smart Grid Advanced Metering Infrastructures

Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

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