TELKOMNIKA Telecommunication, Computing, Electronics and Control
Medium term load demand forecast of Kano zone using neural network algorithms

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Medium term load demand forecast of Kano zone using neural network algorithms

Capability, Layer, Load, Neural network, Weight

Electricity load forecasting refers to projection of future load requirements of an area or region or country through appropriate use of historical load data. One of several challenges faced by the Nigerian power distribution sectors is the overloaded power distribution network which leads to poor voltage distribution and frequent power outages. Accurate load demand forecasting is a key in addressing this challenge. This paper presents a comparison of generalized regression neural network (GRNN), feed-forward neural network (FFNN) and radial basis function neural network for medium term load demand estimation. Experimental data from Kano electricity distribution company (KEDCO) were used in validating the models. The simulation results indicated that the neural network models yielded promising results having achieved a mean absolute percentage error (MAPE) of less than 10% in all the considered scenarios. The generalization capability of FFNN is slightly
better than that of RBFNN and GRNN model. The models could serve as a valuable and promising tool for the forecasting of the load demand.

Huzaimu Lawal Imam, Muhammad Sani Gaya, G. S. M. Galadanci

DOI: 10.12928/TELKOMNIKA.v18i4.14032

Universitas Ahmad Dahlan

August 2020

Sri Wahyuni

ISSN: 1693-6930

http://journal.uad.ac.id/index.php/TELKOMNIKA

English

TELKOMNIKA Telecommunication, Computing, Electronics and Control