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

Abel AIROBOMAN

Article Details

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

Authors:

Abel AIROBOMAN

Publisher:

Nisantasi University

Main Topics:

Applied physics

Journal:

International Journal of Engineering Science and Application 2022 Vol.6 Issue 1, pp.12-28

Document Type:

Article

Publication Date:

3/2022

Document Language:

English

Document ID (Org No.):

2000719110

Frequency:

Monthly

eISSN:

2587-2176

pISSN:

2548-1185

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.

Article Details

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

Authors: Abel AIROBOMAN

Publisher: Nisantasi University

Main Topics: Applied physics

Journal: International Journal of Engineering Science and Application 2022 Vol.6 Issue 1, pp.12-28

Document Type: Article

Publication Date: 3/2022

Document Language: English

Document ID (Org No.): 2000719110

Frequency: Monthly

eISSN: 2587-2176

pISSN: 2548-1185

Issues

Authors:

Abel AIROBOMAN

Publisher:

Nisantasi University

Main Topics:

Applied physics

Journal:

International Journal of Engineering Science and Application 2022 Vol.6 Issue 1, pp.12-28

Document Type:

Article

Publication Date:

3/2022

Document Language:

English

Document ID (Org No.):

2000719110

Frequency:

Monthly

eISSN:

2587-2176

pISSN:

2548-1185