A Cyber-Secure generalized supermodel for wind power forecasting based on deep federated learning and image processing. (1st September 2022)

• Record Type: Journal Article
• Authors: Moayyed, Hamed; Moradzadeh, Arash; Mohammadi-Ivatloo, Behnam; Aguiar, A. Pedro; Ghorbani, Reza
• Is Part Of: Energy conversion and management. Volume 267(2022)
• Publication Date: 2022-09-01
• Subjects: Renewable energy sources -- Wind energy -- Forecasting -- Federated learning -- Convolutional neural network; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.115852
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Wind power generation forecasting based on Deep Federated Learning. A cyber-secure supermodel for wind power forecasting in critical regions. A novel Image Processing approach was applied for cyber-attack detection. Performance of the proposed model was evaluated under a false data injection attack. The presented results confirmed the superiority of the proposed model. Abstract: Accurate wind power forecasting is one of the most important operations within the economic dispatch problem to increase the performance of power and energy systems. Accordingly, this study proposes a cyber-resilient hybrid approach based on the Federated Learning and Convolutional Neural Network (CNN) procedure for short-term wind power generation forecasting in different regions of Iran. Generalizability, data independence, forecasting for regions where no training data is available, and preserving the security and privacy of data are prominent features of the proposed method. The federated network was designed with an architecture of 9 clients to perform the training process and extract the salient features from the data associated with each region in each client via the CNN technique. Then, the generalized global supermodel is produced based on the extracted features in each client to forecast the wind power in new and unknown regions such as Mahshahr, Bojnord, and Lootak that had no training data available and had no effect on global supermodel generation. Various scenarios were developed to test the robustness of the suggested methodology. In the first scenario, wind power forecasting is performed based on the suggested technique. In this scenario, the accuracy of the generalized supermodel to forecast wind power generation in each of the Mahshahr, Bojnord, and Lootak regions is 84%, 85%, and 74%, respectively. The second scenario models the scaling attack by changing the wind speed parameters to evaluate the performance of forecasting models against the data integrity attack. In this scenario, an evaluation of the forecast results based on various performance metrics is conducted highlighting the accuracy reduction of the forecast model, due to the damage caused by cyber-attacks on the input data. In the third scenario, the detection of cyber-attack is done based on the image processing-based technique. The presented results emphasize the accurate performance and high generalizability of the cyber-resilient global supermodel in forecasting wind power in various regions of Iran.