Majlesi Journal of Electrical Engineering Prediction of Equipment Failure Rates in Power Distribution Networks based on Machine-learning Method Volume 17 (2023) Issue 3, September 2023 2024 02 03 Prediction of Equipment Failure Rates in Power Distribution Networks based on Machine-learning Method 10.30486/mjee.2023.1994835.1238 EN Rita Jamasheva Department of Automation and Robotics, Almaty Technological University, Almaty, Kazakhstan Noor Hanoon Haroon Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq. Ahmed Read Al-Tameemi Department of Medical Laboratory Technics, Al-Nisour University College, Baghdad, Iraq. Israa Alhani Department of Medical Laboratory Technics, Mazaya University College, Dhi Qar, Iraq Ali Murad Khudadad Department of Medical Laboratory Technics, Al-Esraa University College, Baghdad, Iraq Bahira Abdulrazzaq Mohammed Department of Medical Engineering, Al-Hadi University College, Baghdad 10011 Iraq. Ali H. O. Al Mansor Department of Medical Laboratory Technics, Al-Zahrawi University College, Karbala, Iraq Mustafa Asaad Hussein National University of Science and Technology, Dhi Qar, Iraq Journal Article 2024 02 03 This paper explores the application of a machine learning approach to predict equipment failure rates in power distribution networks, motivated by the significant impact of power outages on citizens' daily lives and the economy. In this research, data on equipment failure rates and maintenance records were collected from power distribution networks in Baghdad, Iraq. The collected data underwent preprocessing, and features were extracted to train Adaptive Neuro-Fuzzy Inference System (ANFIS) and Periodic Autoregressive Moving Average (PARMA) time series models. To initiate the project, information regarding blackouts that occurred between January 2018 and December 2021 was retrieved from the database. The RMSE index results for the PARMA time series and ANFIS model are 3.518 and 2.264, respectively, demonstrating the superior performance of the ANFIS model in predicting equipment failure rates and its potential for future predictions. This study highlights the ANFIS model's capacity to anticipate equipment failure rates, potentially enhancing maintenance efficiency and reducing power outages in Baghdad. The error mean square was employed to evaluate the proposed models' error rate.