[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.22937/IJCSNS.2022.22.3.28

A New Approach to Load Shedding Prediction in GECOL Using Deep Learning Neural Network

Abusida, Ashraf Mohammed (Department of Computer Engineering, Kastamonu University)
Hancerliogullari, Aybaba (Art & science faculty, Physics Department, Kastamonu University)

Publication Information

International Journal of Computer Science & Network Security / v.22, no.3, 2022 , pp. 220-228 More about this Journal

Abstract

The directed tests produce an expectation model to assist the organization's heads and professionals with settling on the right and speedy choice. A directed deep learning strategy has been embraced and applied for SCADA information. In this paper, for the load shedding expectation overall power organization of Libya, a convolutional neural network with multi neurons is utilized. For contributions of the neural organization, eight convolutional layers are utilized. These boundaries are power age, temperature, stickiness and wind speed. The gathered information from the SCADA data set were pre-handled to be ready in a reasonable arrangement to be taken care of to the deep learning. A bunch of analyses has been directed on this information to get a forecast model. The created model was assessed as far as precision and decrease of misfortune. It tends to be presumed that the acquired outcomes are promising and empowering. For assessment of the outcomes four boundary, MSE, RMSE, MAPE and R2 are determined. The best R2 esteem is gotten for 1-overlap and it was 0.98.34 for train information and for test information is acquired 0.96. Additionally for train information the RMSE esteem in 1-overlap is superior to different Folds and this worth was 0.018.

Keywords

Deep learning; Prediction; GECOL; Load Shedding;

Citations & Related Records

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