Monthly Maximum load Demand Forecasting for Sulaimani Governorate Using Different Weather Conditions Based on Artificial Neural Network Model


  • Najat Hassan Abdulkareem MoEl (Ministry of Electricity-KRG), Electricity control center, Sulaimani/Iraq



Midterm monthly load forecast, Artificial Neural Network, Multi-Layer Perceptron (MLP), Actual load, Predicted Load, Yearly ahead


Medium-term forecasting is an important category of electric load forecasting that covers a time span of up to 1 year ahead. It suits outage and maintenance planning, as well as load switching operation. There is an on-going attention toward putting new approaches to the task. Recently, artificial neural network has played a successful role in various applications. This paper is presents a monthly peak load demand forecasting for Sulaimani (located in North Iraq) using the most widely used traditional method based on an artificial natural network, the performance of the model is tested on the actual historical monthly demand of the governorate for the years 2014–2018. The standard mean absolute percentage error (MAPE) method is used to evaluate the accuracy of forecasting models, the results obtained show a very good estimation of the load. The MAPE is 0.056.


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