Soft computing based load forecasting using artificial neural networks: a case study of Lagos, Nigeria

J. G. Adenle; O. A. Ajeigbe; O. A. Ojo; I. G. Adebayo

J. G. Adenle, Engr. Department of Electrical and Electronic Engineering, Ajayi Crowther University, Oyo
O. A. Ajeigbe, Dr. Department of Electrical and Electronics Engineering, Ajayi Crowther University, https://orcid.org/0000-0001-6828-3228
O. A. Ojo 3African Institute for Science Policy and Innovation, Obafemi Awolowo University, Ile-Ife
I. G. Adebayo, Dr. Department of Electrical/Electronic Engineering, LAUTECH Ogbomoso

Keywords: Soft computing, load forecasting, Artificial Neural Networks (ANN), Load consumption, Multi-layered Perceptron (MLP) network.

Abstract

This study introduces a soft computing approach using Artificial Neural Networks (ANN) for load forecasting, specifically focusing on predicting the minimum and maximum load power. The goal is to efficiently allocate the expected power to suitable load centers. The analysis utilizes a 3-year historical dataset of load consumption in Lagos, a city in Western Nigeria. A Multi-layered Perceptron (MLP) network is employed to generate short-term load forecasts for the area. The inputs for the network include monthly data, while the output parameters are load data obtained from the energy company, which are used to predict power needs in the geographical area. The ANN training employs supervised learning and the back-propagation algorithm, implemented through MATLAB & SIMULINK. The input and target data are preprocessed and normalized within the range of -1 and 1. The network is continuously trained until desirable regression values and a disparity graph are achieved. The study demonstrates significant success with regression values of 0.96, 0.97, and 0.97 obtained over three consecutive years (2021/2022, 2022/2023 and 2023/2024) which indicate that the model accurately predict the load of year 2024. The developed model holds promise for independent power companies in Nigeria to enhance load allocation planning and forecast expected revenue.