Convolutional Neural Networks for Predicting Power Outages in Baghdad

Saja Jafar Jawad; Shaymaa. W. Al-Shammari

doi:10.29194/NJES.28020212

Authors

Saja Jafar Jawad Department of Computer Engineering, College of Engineering, Al-Nahrain University, Baghdad-Iraq.
Dr. Shaymaa. W. Al-Shammari Department of Computer Engineering, College of Engineering, Al-Nahrain University, Baghdad-Iraq. https://orcid.org/0000-0003-1720-5486

DOI:

https://doi.org/10.29194/NJES.28020212

Keywords:

CNN, Electricity Outage, Deep Learning, Baghdad City

Abstract

Power outages are a common and persistent problem in Iraq, significantly impacting various aspects of life and business. These interruptions disrupt routine household tasks and hinder more complex technical operations in industries and services. Emphasizing the need for careful management and proactive solutions. This paper introduces a real-world time series dataset for Baghdad city, including historical outages, weather conditions (such as temperature), and power overloads, and analyzes the correlation among these parameters in different seasons. The research uses this dataset to train one-dimensional Convolutional Neural Networks (1D CNN) to find patterns and relationships that can accurately predict when power outages can happen in the long term and short term to improve the management of the Baghdad electricity grid through data-driven networks. This model was evaluated using performance metrics, and the results show that CNN is accurate in predicting outages in the short term with a Mean Absolute Error (MAE) of (0.0077), whereas, in the long term, it has achieved an MAE of (0.0775). These predictive models have the potential to facilitate the development of proactive measures aimed at reducing the impact of power outages by anticipating potential outages in advance. This research focuses on enhancing the reliability and efficiency of Baghdad's electricity supply, ultimately contributing to economic growth and stability.

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Author Biographies

Saja Jafar Jawad, Department of Computer Engineering, College of Engineering, Al-Nahrain University, Baghdad-Iraq.

Saja Jafar Jawad graduated from Computer Engineering from the University of Technology in 2010. work in the Ministry of Science and Technology and currently a master’s student in the Department of Computer Engineering at Al-Nahrain University.
Dr. Shaymaa. W. Al-Shammari , Department of Computer Engineering, College of Engineering, Al-Nahrain University, Baghdad-Iraq.

Shaymaa Waleed Al-Shammari received the B.Sc. in Computer Engineering in 2005, M.Sc. in Computer Engineering in 2008 from Al-Nahrain University, Baghdad, Iraq, and Ph.D. in Computer Engineering / Distributed Systems in 2017 from University of Salford, Manchester,
UK.
She is a lecturer in Al-Nahrain University, Baghdad, Iraq since 2008. Her research interests are in the subject of cloud computing, Deep learning, optimization, web services, QoS, QoE, network performance and published many papers related to these subjects.

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