A Pricing Model for the Nigerian Electricity Transmission Using Artificial Neural Network

For the restructuring of Nigeria’s power sector to be effective, efforts must be made to evolve an excellent pricing methodology that will economically be able to accommodate disintegrated units like generating, transmitting and distributing/retailing units. This work is aimed at developing an improved electricity transmission pricing scheme for the Nigerian Network using Artificial Neural Network. A model based on artificial neural network for improved electricity transmission pricing for the Nigerian Network was developed in order to forecast the transmission price for a longer period of time. The performance of the neural network model was evaluated by applying the actual transmission pricing data from Transmission Company of Nigeria to predict the prices. Raw data was collected from Transmission Company of Nigeria, Port-Harcourt district. The data was trained, tested and validated on the MATLAB/Simulink environment. Forecast results revealed that the model performed very well with a mean absolute percentage error of 0.09%, an average mean error of 0.5 and a regression value of 0.99. It was concluded that the improved and modified transmission use of system pricing is the best pricing method which will be acceptable to consumers and also ensure recovery of transmission cost in Nigeria. It was recommended that Artificial Intelligent-based techniques (ANN) in particular, must be implemented for long-term improved electricity transmission pricing forecast for the Transmission Company of Nigeria.