Optimizing Smart Power Grid Stability Based on the Prediction of a Deep Learning Model Article Swipe
Shihab Hamad Khaleefah
,
Salama A. Mostafa
,
Saraswathy Shamini Gunasekaran
,
Umar Farooq Khattak
,
Mohammed Ahmed Jubair
,
Rita Afyenni
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.62527/joiv.8.2.2758
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.62527/joiv.8.2.2758
A smart grid is an electricity transmission system that uses digital technology to control getting and dispatching electricity from all generation sources to satisfy end users' fluctuating electricity demands. It achieves this through deploying technologies such as technology and smart grids, which are pivotal in increasing the power supply's efficiency, reliability, and sustainability to the public. Decentralized Smart Grid Control (DSGC) is a system where the control and decision-making functions are distributed to different grid points instead of in one central place. This paradigm is critical for the fault resistance and efficiency of the grid because it enables the local regions to carry on by themselves, manage electric power flows, respond to changes, and integrate many kinds of energy sources successfully. The grid frequency is monitored via the DSGC to ensure dynamic grid stability estimation. All parties, from users to energy producers, may take advantage of the price of power tied to grid frequency. The DSGC, a vital component of this research, gathered information about clients' consumption and used several assumptions to predict the behavior of the consumers. It establishes a method to assess against current supply circumstances and the resultant recommended pricing information. This research proposes a long short-term memory (LSTM) model to analyze data gathered regarding smart grid characteristics and predict grid stability. The results show a strong capacity for the LSTM model, achieving an accuracy of 96.73% with a loss of just 7.44%. The model also achieves a precision of 96.70%, recall of 98.18%, and F1-score of 97.43%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.62527/joiv.8.2.2758
- OA Status
- diamond
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403164346
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403164346Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.62527/joiv.8.2.2758Digital Object Identifier
- Title
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Optimizing Smart Power Grid Stability Based on the Prediction of a Deep Learning ModelWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-29Full publication date if available
- Authors
-
Shihab Hamad Khaleefah, Salama A. Mostafa, Saraswathy Shamini Gunasekaran, Umar Farooq Khattak, Mohammed Ahmed Jubair, Rita AfyenniList of authors in order
- Landing page
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https://doi.org/10.62527/joiv.8.2.2758Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://doi.org/10.62527/joiv.8.2.2758Direct OA link when available
- Concepts
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Stability (learning theory), Deep learning, Smart grid, Power grid, Computer science, Artificial intelligence, Power (physics), Machine learning, Engineering, Electrical engineering, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.fault | 88 |
| abstract_inverted_index.kinds | 116 |
| abstract_inverted_index.local | 99 |
| abstract_inverted_index.model | 202, 237 |
| abstract_inverted_index.power | 47, 108, 149 |
| abstract_inverted_index.price | 147 |
| abstract_inverted_index.smart | 1, 39, 208 |
| abstract_inverted_index.users | 138 |
| abstract_inverted_index.vital | 157 |
| abstract_inverted_index.where | 64 |
| abstract_inverted_index.which | 41 |
| abstract_inverted_index.(DSGC) | 60 |
| abstract_inverted_index.(LSTM) | 201 |
| abstract_inverted_index.7.44%. | 235 |
| abstract_inverted_index.96.73% | 229 |
| abstract_inverted_index.assess | 183 |
| abstract_inverted_index.energy | 118, 140 |
| abstract_inverted_index.ensure | 130 |
| abstract_inverted_index.flows, | 109 |
| abstract_inverted_index.grids, | 40 |
| abstract_inverted_index.manage | 106 |
| abstract_inverted_index.memory | 200 |
| abstract_inverted_index.method | 181 |
| abstract_inverted_index.model, | 224 |
| abstract_inverted_index.place. | 81 |
| abstract_inverted_index.points | 75 |
| abstract_inverted_index.recall | 244 |
| abstract_inverted_index.strong | 219 |
| abstract_inverted_index.supply | 186 |
| abstract_inverted_index.system | 7, 63 |
| abstract_inverted_index.users' | 25 |
| abstract_inverted_index.96.70%, | 243 |
| abstract_inverted_index.97.43%. | 250 |
| abstract_inverted_index.98.18%, | 246 |
| abstract_inverted_index.Control | 59 |
| abstract_inverted_index.against | 184 |
| abstract_inverted_index.analyze | 204 |
| abstract_inverted_index.because | 95 |
| abstract_inverted_index.central | 80 |
| abstract_inverted_index.control | 13, 66 |
| abstract_inverted_index.current | 185 |
| abstract_inverted_index.digital | 10 |
| abstract_inverted_index.dynamic | 131 |
| abstract_inverted_index.enables | 97 |
| abstract_inverted_index.getting | 14 |
| abstract_inverted_index.instead | 76 |
| abstract_inverted_index.pivotal | 43 |
| abstract_inverted_index.predict | 172, 212 |
| abstract_inverted_index.pricing | 192 |
| abstract_inverted_index.public. | 55 |
| abstract_inverted_index.regions | 100 |
| abstract_inverted_index.respond | 110 |
| abstract_inverted_index.results | 216 |
| abstract_inverted_index.satisfy | 23 |
| abstract_inverted_index.several | 169 |
| abstract_inverted_index.sources | 21, 119 |
| abstract_inverted_index.through | 32 |
| abstract_inverted_index.F1-score | 248 |
| abstract_inverted_index.accuracy | 227 |
| abstract_inverted_index.achieves | 30, 239 |
| abstract_inverted_index.behavior | 174 |
| abstract_inverted_index.capacity | 220 |
| abstract_inverted_index.changes, | 112 |
| abstract_inverted_index.clients' | 165 |
| abstract_inverted_index.critical | 85 |
| abstract_inverted_index.demands. | 28 |
| abstract_inverted_index.electric | 107 |
| abstract_inverted_index.gathered | 162, 206 |
| abstract_inverted_index.paradigm | 83 |
| abstract_inverted_index.parties, | 136 |
| abstract_inverted_index.proposes | 196 |
| abstract_inverted_index.research | 195 |
| abstract_inverted_index.supply's | 48 |
| abstract_inverted_index.achieving | 225 |
| abstract_inverted_index.advantage | 144 |
| abstract_inverted_index.component | 158 |
| abstract_inverted_index.deploying | 33 |
| abstract_inverted_index.different | 73 |
| abstract_inverted_index.frequency | 123 |
| abstract_inverted_index.functions | 69 |
| abstract_inverted_index.integrate | 114 |
| abstract_inverted_index.monitored | 125 |
| abstract_inverted_index.precision | 241 |
| abstract_inverted_index.regarding | 207 |
| abstract_inverted_index.research, | 161 |
| abstract_inverted_index.resultant | 190 |
| abstract_inverted_index.stability | 133 |
| abstract_inverted_index.consumers. | 177 |
| abstract_inverted_index.efficiency | 91 |
| abstract_inverted_index.frequency. | 153 |
| abstract_inverted_index.generation | 20 |
| abstract_inverted_index.increasing | 45 |
| abstract_inverted_index.producers, | 141 |
| abstract_inverted_index.resistance | 89 |
| abstract_inverted_index.short-term | 199 |
| abstract_inverted_index.stability. | 214 |
| abstract_inverted_index.technology | 11, 37 |
| abstract_inverted_index.assumptions | 170 |
| abstract_inverted_index.consumption | 166 |
| abstract_inverted_index.dispatching | 16 |
| abstract_inverted_index.distributed | 71 |
| abstract_inverted_index.efficiency, | 49 |
| abstract_inverted_index.electricity | 5, 17, 27 |
| abstract_inverted_index.establishes | 179 |
| abstract_inverted_index.estimation. | 134 |
| abstract_inverted_index.fluctuating | 26 |
| abstract_inverted_index.information | 163 |
| abstract_inverted_index.recommended | 191 |
| abstract_inverted_index.themselves, | 105 |
| abstract_inverted_index.information. | 193 |
| abstract_inverted_index.reliability, | 50 |
| abstract_inverted_index.technologies | 34 |
| abstract_inverted_index.transmission | 6 |
| abstract_inverted_index.Decentralized | 56 |
| abstract_inverted_index.circumstances | 187 |
| abstract_inverted_index.successfully. | 120 |
| abstract_inverted_index.sustainability | 52 |
| abstract_inverted_index.characteristics | 210 |
| abstract_inverted_index.decision-making | 68 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.79621626 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |