Long-Term Load Forecasting for the 330 kV Nigerian Network using Artificial Neural Network

I. G. Abdulrahman, Y. S. Haruna, U. O. Aliyu, L. O. Ismail, K. A. Ibrahim

Abstract


The availability of reliable, consistent, and sufficient electricity is crucial for the economic, infrastructural, and overall development of any society or country. As the population and socio-economic activities increase, the demand for electricity also rises. To ensure that the supply meets the required demand, it is essential to prioritize early planning, accurate forecasting, and regular maintenance of the utility system. Numerous researchers have highlighted the superiority of artificial intelligence, particularly artificial neural networks, over other methods and models for load forecasting and optimization. This is due to their ability to learn from historical data and identify patterns in electricity consumption. In this study, a long-term load demand forecasting technique was developed using artificial neural networks (ANN) with the Nigerian power system as a case study. Historical load data, gross domestic product (GDP), population figures, and long-term climatic changes (temperature) from 2005 to 2019 were utilized to train and simulate the neural network for long-term load forecasting on the 330 kV Nigerian network. The MATLAB software was employed for modeling, with the data divided into training (70%), validation (15%), and testing (15%) sets. Through simulations conducted on the predicted data, the Mean Average Percentage Error (MAPE) was determined to be 0.71%. This value is lower compared to previous works, indicating the effectiveness of the developed technique in accurately forecasting long-term load demand. By leveraging artificial neural networks and historical data, this approach provides a reliable tool for optimizing electricity supply planning and management, thereby contributing to the sustainable development of the Nigerian power system.

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