Short term load forecast of Kano zone using artificial intelligent techniques

Abstract

<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>.

Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue

“…Choosing the perfect historical data is important during the iteration process since it will minimise errors measures. In [14], author evaluate the performance of neuro fuzzy and non-linear auto-regressive by using mean absolute percentage error (MAPE) and root mean square deviation (RMSE) achieving the short term load forecast. Though, on the other hand, the power system has its own unique challenge in electricity areas.…”

Medium term load demand forecast of Kano zone using neural network algorithms

“…Over the years, many researches were conducted on the Nigerian power sector and its related challenges. Nevertheless, most of these researches centre on general problems of power generation or transmission or distribution or combine and the researches on load demand focuses on the Nigerian wide electricity demand [1,2] or demand of a town or city [3] or may be short term forecast [2,4].…”