Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid
The stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning (ML) and deep learning (DL) models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the nec...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/7319010 |
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| _version_ | 1850179726381940736 |
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| author | Ayushi Chahal Preeti Gulia Nasib Singh Gill Jyotir Moy Chatterjee |
| author_facet | Ayushi Chahal Preeti Gulia Nasib Singh Gill Jyotir Moy Chatterjee |
| author_sort | Ayushi Chahal |
| collection | DOAJ |
| description | The stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning (ML) and deep learning (DL) models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k-nearest neighbor, and optimized artificial neural network (ANN), have been applied on openly available smart energy grid datasets to predict their stability. The present article uses metrics such as accuracy, precision, recall, f1-score, and ROC curve to compare different predictive models. Data augmentation and feature scaling have been applied to the dataset to get better results. The augmented dataset provides better results as compared with the normal dataset. This study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. The ANN model provides 97.27% accuracy, 96.79% precision, 95.67% recall, and 96.22% F1 score. |
| format | Article |
| id | doaj-art-9bd2a64dbd92419f925ec8ebcfd94566 |
| institution | OA Journals |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9bd2a64dbd92419f925ec8ebcfd945662025-08-20T02:18:25ZengWileyComplexity1099-05262022-01-01202210.1155/2022/7319010Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart GridAyushi Chahal0Preeti Gulia1Nasib Singh Gill2Jyotir Moy Chatterjee3Department of Computer Science & ApplicationsDepartment of Computer Science & ApplicationsDepartment of Computer Science & ApplicationsDepartment of Information TechnologyThe stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning (ML) and deep learning (DL) models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k-nearest neighbor, and optimized artificial neural network (ANN), have been applied on openly available smart energy grid datasets to predict their stability. The present article uses metrics such as accuracy, precision, recall, f1-score, and ROC curve to compare different predictive models. Data augmentation and feature scaling have been applied to the dataset to get better results. The augmented dataset provides better results as compared with the normal dataset. This study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. The ANN model provides 97.27% accuracy, 96.79% precision, 95.67% recall, and 96.22% F1 score.http://dx.doi.org/10.1155/2022/7319010 |
| spellingShingle | Ayushi Chahal Preeti Gulia Nasib Singh Gill Jyotir Moy Chatterjee Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid Complexity |
| title | Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid |
| title_full | Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid |
| title_fullStr | Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid |
| title_full_unstemmed | Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid |
| title_short | Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid |
| title_sort | performance analysis of an optimized ann model to predict the stability of smart grid |
| url | http://dx.doi.org/10.1155/2022/7319010 |
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