ANN and regression based quantification framework for climate change impact assessment on a weak transmission grid of a developing country across Horizon 2050 plus.
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| Title: | ANN and regression based quantification framework for climate change impact assessment on a weak transmission grid of a developing country across Horizon 2050 plus. |
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| Authors: | Malik, M. Mahad1 (AUTHOR) malikmahad42@yahoo.com, Asim, Hamza Waheed1 (AUTHOR) hamxa.waheed@gmail.com, Kazmi, Syed Ali Abbas1 (AUTHOR) saakazmi@uspcase.nust.edu.pk, Mujahid, Kamran1 (AUTHOR) kamran.mujahid@ymail.com, Ansari, Muhammad Waleed2 (AUTHOR) waleed.ansari2k17@gmail.com |
| Source: | Environment, Development & Sustainability. Jan2026, Vol. 28 Issue 1, p261-280. 20p. |
| Subject Terms: | *Climate change, *Electric power distribution grids, *Artificial neural networks, *Energy consumption, *Computer simulation of heat transfer, *Renewable energy sources, *Temperature effect |
| Geographic Terms: | Pakistan |
| Abstract: | Climate change and rising temperatures represent a grave threat to modern civilization, with many of the key infrastructures across the world under risk. The energy sector is highly susceptible to anthropogenic warming, and many of its subsectors would be impacted by it. We use climate projections from climate models and use them to estimate the impact on demand, transmission, and generation in the country. An ANN-based approach utilizing historical demand patterns is used to estimate the impact on consumer demand, and it showed that most regions in the country would experience a drastic increase in demand. A thermal model of transmission lines showed that some transmission lines might lose 23.34% of their capacity. Data reveals that renewable energy sources boost energy efficiency. Hence, future national policies should include more of them. Thermal and PV power in the country have been compared to their resilience to rising temperatures. Without the implementation of more efficient technologies, demand-side management programs, or the upgrading of transmission infrastructure, CC impacts may overwhelm Pakistan's already weak grid system. Our applied models show the Highest forecasting efficiency to be 92.42%, 92.02% and 91.98% for PESCO, LESCO and IESCO. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 191807199 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: ANN and regression based quantification framework for climate change impact assessment on a weak transmission grid of a developing country across Horizon 2050 plus. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Malik%2C+M%2E+Mahad%22">Malik, M. Mahad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> malikmahad42@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Asim%2C+Hamza+Waheed%22">Asim, Hamza Waheed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hamxa.waheed@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kazmi%2C+Syed+Ali+Abbas%22">Kazmi, Syed Ali Abbas</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> saakazmi@uspcase.nust.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Mujahid%2C+Kamran%22">Mujahid, Kamran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kamran.mujahid@ymail.com</i><br /><searchLink fieldCode="AR" term="%22Ansari%2C+Muhammad+Waleed%22">Ansari, Muhammad Waleed</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> waleed.ansari2k17@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Jan2026, Vol. 28 Issue 1, p261-280. 20p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+distribution+grids%22">Electric power distribution grids</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+simulation+of+heat+transfer%22">Computer simulation of heat transfer</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br />*<searchLink fieldCode="DE" term="%22Temperature+effect%22">Temperature effect</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Pakistan%22">Pakistan</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Climate change and rising temperatures represent a grave threat to modern civilization, with many of the key infrastructures across the world under risk. The energy sector is highly susceptible to anthropogenic warming, and many of its subsectors would be impacted by it. We use climate projections from climate models and use them to estimate the impact on demand, transmission, and generation in the country. An ANN-based approach utilizing historical demand patterns is used to estimate the impact on consumer demand, and it showed that most regions in the country would experience a drastic increase in demand. A thermal model of transmission lines showed that some transmission lines might lose 23.34% of their capacity. Data reveals that renewable energy sources boost energy efficiency. Hence, future national policies should include more of them. Thermal and PV power in the country have been compared to their resilience to rising temperatures. Without the implementation of more efficient technologies, demand-side management programs, or the upgrading of transmission infrastructure, CC impacts may overwhelm Pakistan's already weak grid system. Our applied models show the Highest forecasting efficiency to be 92.42%, 92.02% and 91.98% for PESCO, LESCO and IESCO. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=191807199 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10668-024-04977-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 261 Subjects: – SubjectFull: Climate change Type: general – SubjectFull: Electric power distribution grids Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Computer simulation of heat transfer Type: general – SubjectFull: Renewable energy sources Type: general – SubjectFull: Temperature effect Type: general – SubjectFull: Pakistan Type: general Titles: – TitleFull: ANN and regression based quantification framework for climate change impact assessment on a weak transmission grid of a developing country across Horizon 2050 plus. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Malik, M. Mahad – PersonEntity: Name: NameFull: Asim, Hamza Waheed – PersonEntity: Name: NameFull: Kazmi, Syed Ali Abbas – PersonEntity: Name: NameFull: Mujahid, Kamran – PersonEntity: Name: NameFull: Ansari, Muhammad Waleed IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1387585X Numbering: – Type: volume Value: 28 – Type: issue Value: 1 Titles: – TitleFull: Environment, Development & Sustainability Type: main |
| ResultId | 1 |
