FORECASTING OF MAXIMUM PEAK LOAD DEMAND OF ABUJA REGION INTERCONNECTED POWER NETWORK USING ARTIFICIAL NEURAL NETWORK

Year 2022, Volume: 6 Issue: 1, 12 - 28, 30.03.2022

Abel Aıroboman

Abstract

This paper presents an artificial neural network (ANN) based approach for long-term Maximum Load Demand Forecasting (MLDF) of 33KV substations of Abuja region interconnected network. Historical data were collected from the nine sub stations under Abuja region between 2013 to 2017. The data collated were analyzed using feed forward back propagation of ANN in training, testing and forecasting of load demand. The performance accuracy of ANN was assessed using Mean Square Error (MSE). The simulation results revealed that ANN shows quite good performance as a forecasting tool due to the trends and the large data set available. Further results showed an increase in load demand for all feeders except for Akwanga feeder within 2021-2022. Meanwhile, Apo feeder will have the highest MLDF of 176.43MW while Akwanga will have the lowest MLDF of 24.94MW by the year 2022.
Keywords: Abuja, ANN, Forecasting, Load, Network.

Keywords

ANN, Forecasting, Load, Network., Abuja

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