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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. [ABSTRACT FROM AUTHOR]

Energy for fulfilling basic community/individual needs has come to constitute the first article of expectation in all contemporary societies. The exploitation of renewables notably solar in electricity generation has brought relief to the fulfilment of energy demand especially among susceptible communities. In this paper yearly minimum solar radiation of Kano (12.05°N; 08.2°E; altitude 472.5 m; 3 air density 1.1705 kg/m3) for 46 years is used to generate a prediction model that fits the data using autoregressive moving average (ARMA) and a new model termed autoregressive moving average process (ARMAP). Comparison between the ARMA and ARMAP models showed a tremendous improve in the sum of square error reduction between the actual data and the forecasted data by 47%. [ABSTRACT FROM AUTHOR]