<span lang="EN-MY">Load forecast provides useful information for effective electricity dispatch, planning for future expansion and significantly enhances operational efficiency. Conventional techniques yield unsatisfactory forecast which results in high energy losses and in turn leads to high operational cost and suppressed electricity demand. This paper presents hybrid neuro fuzzy (HNF) and Nonlinear Auto-Regressive with eXogeneous input (NARX) neural network for the short term load prediction of Kano region Nigeria.  Simulation results obtained demonstrated the generalization capabilities of the models in predicting the load accurately well by achieving MAPE of 0.025% and 0.6551% for the HNF model and NARX network model respectively. The models could serve as promising tool for predicting Kano Zone load demand</span>.

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.