<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>.

The emergence of Dilute Magnetic Semiconductors (DMS) with a potentials for spintronic application have attracted much researches attention, special consideration has been given to ZnO semiconductor material due to its wide band gap of 3.37 eV, large exciting binding energy of 60 meV, moreover, its ferromagnetic behavior at room temperature when doped with transition metals. MxZn1-xO (M = Fe or Ni) nanoparticles were synthesized by microwave assisted synthesis method calcined at 600°C. The structural, morphological and magnetic properties of these nanoparticles were studied using X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM) and Vibrating Sample Magnetometer (VSM) respectively. Single phase Wurtzite hexagonal crystal structure was observed for the undoped and Fe doped ZnO nanoparticles with no any impurity, whereas Ni doped ZnO nanoparticles shows the formation of NiO impurities. The magnetic measurement reveals a diamagnetic behavior for the undoped ZnO meanwhile a clear room temperature ferromagnetism was observed for both Fe and Ni doped ZnO. Fe doped ZnO present a high saturation magnetization compared to Ni doped ZnO. However, Ni doped ZnO present high coercivity. The research was confirmed that Fe doped ZnO material will be good material combination for spintronic applications.

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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.