Improved Medium Term Approach for Load Forecasting of Nigerian Electricity Network Using Artificial Neuro-Fuzzy Inference System: A Case Study of University of Nigeria, Nsukka.

The demand for electrical energy continues to rise at an alarming rate, while supply is not proportional to power demand. Thus, in order to accomplish appropriate preparation and operation of an electrical power system network, it is crucial to properly assess the present and future consumption of electric power systems. Meeting the electric load needs entails actively anticipating the amount of energy required to meet the subscribers' power demand. To forecast energy demand, many algebraic and artificial intelligence schemes have been used. This paper proposes an improved medium-term forecasting scheme that employs an artificial neural network (ANN). The model was developed and trained with the help of an artificial neuro-fuzzy inference system (ANFIS). The data used, which ranged from 2014 to 2019, was obtained from the University of Nigeria Nsukka's (UNN) research and energy Centre, and the works department of the same university. The model was designed and simulated using MATLAB 2018a, and the results were evaluated using MAPE. The proposed system used a robust machine learning based scheme. Unlike conventional systems, the system demonstrated a MAPE of 4.34% and a correlation of 0.91144315 at predicted and actual loads, as shown in Table 2. The proposed system shows that the predicted loads has 25th and 75th percentiles of 0.58MW and 0.98WM, while that of the actual loads are 0.649MW and 0.936MW. [ABSTRACT FROM AUTHOR]