Analysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting

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dc.contributor.authorKuzlu, Murat
dc.contributor.authorSarp, Salih
dc.contributor.authorCatak, Ferhat Ozgur
dc.contributor.authorCali, Umit
dc.contributor.authorZhao, Yanxiao
dc.contributor.authorElma, Onur
dc.contributor.authorGuler, Ozgur
dc.date.accessioned2025-01-27T20:54:30Z
dc.date.available2025-01-27T20:54:30Z
dc.date.issued2024
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe solar photovoltaics (PV) energy resources have become more important with their significant contribution to the current power grid among renewable energy resources. However, the integration of the solar PV causes reliability issues in the power grid due to its high dependence on the weather condition. The predictability and stability of forecasting are critical for fully utilizing solar power. This study presents an Artificial Neural Network (ANN)-based solar PV power generation forecasting using a public dataset to form a basis experimental testbed to demonstrate analysis and impact of deceptive data attacks with adversarial machine learning. In addition, it evaluates the algorithms' performance using the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Average Error (MAE) metrics for two main cases, i.e., with and without adversarial machine learning attacks. The results show that the ANN-based models are vulnerable to adversarial attacks.
dc.description.sponsorshipCommonwealth Cyber Initiative, an investment in the advancement of cyber R AMP;D, innovation, and workforce development in Virginia
dc.description.sponsorshipThis work was supported in part by the Commonwealth Cyber Initiative, an investment in the advancement of cyber R &D, innovation, and workforce development in Virginia. For more information about CCI, visit cyberinitiative.org.
dc.identifier.doi10.1007/s00202-022-01601-9
dc.identifier.endpage1823
dc.identifier.issn0948-7921
dc.identifier.issn1432-0487
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85134671120
dc.identifier.scopusqualityQ2
dc.identifier.startpage1815
dc.identifier.urihttps://doi.org/10.1007/s00202-022-01601-9
dc.identifier.urihttps://hdl.handle.net/20.500.12428/26091
dc.identifier.volume106
dc.identifier.wosWOS:000828969200001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofElectrical Engineering
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectSolar PV energy generation forecasting
dc.subjectAdversarial machine learning attacks
dc.subjectForecasting
dc.titleAnalysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting
dc.typeArticle

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