Academic Journal
Power distribution and forecasting using a probabilistic and systematic data processing model for renewable resources.
| Title: | Power distribution and forecasting using a probabilistic and systematic data processing model for renewable resources. |
|---|---|
| Authors: | Alnuman, Hammad, Abbas, Ghulam, Yousef, Amr |
| Source: | Scientific Reports; 7/28/2025, Vol. 15 Issue 1, p1-21, 21p |
| Subject Terms: | Electric power distribution, Forecasting, Energy consumption, Reinforcement learning, Smart power grids, Stochastic models, Renewable natural resources, Electronic data processing |
| Abstract: | The inherent unpredictability and fluctuation of renewable energy systems make it very difficult to precisely estimate power output and manage distribution, which is a major obstacle to their widespread use. Current forecasting techniques often fall short, struggling to effectively handle unexpected spikes or changes in demand, which can lead to inefficiencies and even system instability. To better anticipate short-term demand, optimize the balance between generation and distribution states, and dynamically detect and differentiate inappropriate surges in power distribution, this article proposes the Probabilistic Systematic Processing Method (PSPM), which utilizes reward-based state model learning. To anticipate demand and intervene proactively, the approach utilizes real-time and historical characteristics, including consumption, peak generation, and disconnection occurrences. To provide a robust and trustworthy assessment, we validate our results using the Smart Grid Data set from the ARRA projects dataset. Comparing PSPM to current methods, empirical data show that it improves forecast success rate by 20%, increases distribution efficiency by 25%, and reduces analytical latency by 35%. These enhancements showcase PSPM's innovative approach to improving the resilience and operational efficiency of renewable energy systems. Since adaptive energy distribution is not a frequently investigated topic in the existing literature, this study stands out by combining probabilistic analysis with reinforcement learning. Renewable energy systems may be made more intelligent and resilient with the help of the suggested method, which is both practical and scalable. Sustainable power infrastructure automation, energy policy planning, and smart grid management are among its many potential applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Scientific Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1038/s41598-025-12888-6 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edb DbLabel: Complementary Index An: 186952562 RelevancyScore: 1007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1007.33459472656 |
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| Items | – Name: Title Label: Title Group: Ti Data: Power distribution and forecasting using a probabilistic and systematic data processing model for renewable resources. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alnuman%2C+Hammad%22">Alnuman, Hammad</searchLink><br /><searchLink fieldCode="AR" term="%22Abbas%2C+Ghulam%22">Abbas, Ghulam</searchLink><br /><searchLink fieldCode="AR" term="%22Yousef%2C+Amr%22">Yousef, Amr</searchLink> – Name: TitleSource Label: Source Group: Src Data: Scientific Reports; 7/28/2025, Vol. 15 Issue 1, p1-21, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Electric+power+distribution%22">Electric power distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+power+grids%22">Smart power grids</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+models%22">Stochastic models</searchLink><br /><searchLink fieldCode="DE" term="%22Renewable+natural+resources%22">Renewable natural resources</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The inherent unpredictability and fluctuation of renewable energy systems make it very difficult to precisely estimate power output and manage distribution, which is a major obstacle to their widespread use. Current forecasting techniques often fall short, struggling to effectively handle unexpected spikes or changes in demand, which can lead to inefficiencies and even system instability. To better anticipate short-term demand, optimize the balance between generation and distribution states, and dynamically detect and differentiate inappropriate surges in power distribution, this article proposes the Probabilistic Systematic Processing Method (PSPM), which utilizes reward-based state model learning. To anticipate demand and intervene proactively, the approach utilizes real-time and historical characteristics, including consumption, peak generation, and disconnection occurrences. To provide a robust and trustworthy assessment, we validate our results using the Smart Grid Data set from the ARRA projects dataset. Comparing PSPM to current methods, empirical data show that it improves forecast success rate by 20%, increases distribution efficiency by 25%, and reduces analytical latency by 35%. These enhancements showcase PSPM's innovative approach to improving the resilience and operational efficiency of renewable energy systems. Since adaptive energy distribution is not a frequently investigated topic in the existing literature, this study stands out by combining probabilistic analysis with reinforcement learning. Renewable energy systems may be made more intelligent and resilient with the help of the suggested method, which is both practical and scalable. Sustainable power infrastructure automation, energy policy planning, and smart grid management are among its many potential applications. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Scientific Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/s41598-025-12888-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1 Subjects: – SubjectFull: Electric power distribution Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Smart power grids Type: general – SubjectFull: Stochastic models Type: general – SubjectFull: Renewable natural resources Type: general – SubjectFull: Electronic data processing Type: general Titles: – TitleFull: Power distribution and forecasting using a probabilistic and systematic data processing model for renewable resources. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alnuman, Hammad – PersonEntity: Name: NameFull: Abbas, Ghulam – PersonEntity: Name: NameFull: Yousef, Amr IsPartOfRelationships: – BibEntity: Dates: – D: 28 M: 07 Text: 7/28/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20452322 Numbering: – Type: volume Value: 15 – Type: issue Value: 1 Titles: – TitleFull: Scientific Reports Type: main |
| ResultId | 1 |