A Short-Term Load Forecasting of 33kV, 11kV and 415V Electrical Systems using HMLP Network

Abstract

this research is utilizing three different voltages for load flow forecasting to establish a short-term online forecasting. Upon completion of this research, several neural network learning algorithms will be compared that is an Adaptive Learning Recursive Prediction Error, Modified Recursive Prediction Error, Recursive Prediction Error and Back Propagation. A network entitled Hybrid Multilayered Perceptron Network is coupled to these training algorithms. By using an on-line model, it is applied to estimate the future trend. The future trend network model is train using nonlinear autoregressive moving average with an exogenous input. The projecteded data is collected from the utility power supplies of 33kV, 11kV and 415V at three different locations in MARA University of Teknologi, Penang, Malaysia. Three different sets of data are applied to evaluate the performance of these learning algorithms. From the investigational results gathered, it showed that Adaptive Learning Recursive Prediction Error learning algorithm can be more enhanced the performance of other learning algorithm as an online model in the series of 0.45 dB to 9.481 dB of Mean Square Error during validation.