Electric load forecasting under False Data Injection Attacks using deep learning

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dc.contributor.authorMoradzadeh, Arash
dc.contributor.authorMohammadpourfard, Mostafa
dc.contributor.authorKonstantinou, Charalambos
dc.contributor.authorGenc, Istemihan
dc.contributor.authorKim, Taesic
dc.contributor.authorMohammadi-Ivatloo, Behnam
dc.date.accessioned2023-01-20T09:08:08Z
dc.date.available2023-01-20T09:08:08Z
dc.date.issued2022en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractPrecise electric load forecasting at different time horizons is an essential aspect for electricity producers and consumers who participate in energy markets in order to maximize their economic efficiency. Moreover, accurate prediction of the electric load contributes toward robust and resilient power grids due to the error minimization of generators scheduling schemes. The accuracy of the existing electric load forecasting methods relies on data quality due to noisy real-world environments, and data integrity due to malicious cyber-attacks. This paper proposes a cyber-secure deep learning framework that accurately predicts electric load in power grids for a time horizon spanning from an hour to a week. The proposed deep learning framework systematically integrates Autoencoder (AE), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models (AE-CLSTM). The feasibility of the proposed solution is validated by using realistic grid data acquired from the distribution network of Tabriz, Iran. Compared to other load forecasting methods, the proposed method shows the highest accuracy in both a normal case with real-world noise and a stealthy False Data Injection Attack (FDIA). The proposed load forecasting method is practical and suitable for mitigating noise in real-world data and integrity attacks.en_US
dc.identifier.doi10.1016/j.egyr.2022.08.004en_US
dc.identifier.scopus2-s2.0-85135917015en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6111
dc.identifier.urihttps://doi.org/10.1016/j.egyr.2022.08.004
dc.identifier.volume8en_US
dc.identifier.wosWOS:000861249900007en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofEnergy Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCybersecurity; Deep learning; False Data Injection Attack; Load forecasting; Smart griden_US
dc.titleElectric load forecasting under False Data Injection Attacks using deep learningen_US
dc.typeArticleen_US

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