Principal Component Regression with Artificial Neural Network to Improve Prediction of in Electricity Demand

Planning for electricity demand is a key factor for the success in the development of any countries. Such success can only be achieved if the demand for electricity is predicted correctly and accurately. This study introduces a new hybrid approach that combines Principle Component Regression (PCR) and Back-Propagation Neural Networks (BPNN) techniques in order to improve the accuracy of the electricity demand prediction rates. The study includes 13 factors that related to electricity demand, and data for these factors have been collected in Malaysia. The new combination (PCR-BPNN) starts to solve the problem of collinearity among the input dataset, and hence, the reliability of the results. The work focuses also on the errors that recoded at that output stage of the electricity prediction models due to changes in the patterns of the input dataset. The accuracy and reliability of the results have been improved through the new proposed model. Validations have been achieved for the proposed model through comparing the value of three performance indicators of the PCR-BPNN with the performance rates of three major prediction models. Results show the outperformance of the PCR-BPNN over the other types of the electricity prediction models.