Electric load forecasting under False Data Injection Attacks using deep learning

Arash Moradzadeh, Mostafa Mohammadpourfard*, Charalambos Konstantinou, Istemihan Genc, Taesic Kim, Behnam Mohammadi-Ivatloo

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Makalebilirkişi

21 Atıf (Scopus)

Özet

Precise 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.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)9933-9945
Sayfa sayısı13
DergiEnergy Reports
Hacim8
DOI'lar
Yayın durumuYayınlandı - Kas 2022

Bibliyografik not

Publisher Copyright:
© 2022 The Author(s)

Finansman

This work was supported by European Commission Horizon 2020 Marie Skłodowska-Curie Actions Cofund program and TÜBİTAK (Project Number: 120C080 ). The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mostafa Mohammadpourfard reports financial support was provided by European Commission Horizon. Mostafa Mohammadpourfard reports financial support was provided by TUBITAK. Mostafa MOhammadpourfard reports a relationship with European Commission Horizon that includes: funding grants.

FinansörlerFinansör numarası
European Commission Horizon
European Commission Horizon 2020 Marie Skłodowska-Curie Actions Cofund program
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu120C080

    Parmak izi

    Electric load forecasting under False Data Injection Attacks using deep learning' araştırma başlıklarına git. Birlikte benzersiz bir parmak izi oluştururlar.

    Alıntı Yap