An Accurate Medium-Term Load Forecasting based on Hybrid Technique

Abstract

An accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load demand is fluctuated in a day. In this paper, a hybrid Ant-Lion Optimizer Least-square Support Vector Machine (ALO-LSSVM) is proposed to forecast 24-hour load demand for the next year. Ant-Lion Optimizer (ALO) is utilized to optimize the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimization is to minimize the Mean Absolute Percentage Error (MAPE). The performance of ALO-LSSVM technique was compared with those obtained from LS-SVM technique through a 10-fold cross-validation procedure. The historical hourly load data are analyzed and appropriate features are selected for the model. There are 24 inputs and 24 outputs vectors for this model which represents 24-hour load demand for whole year. The results revealed that the high accuracy of prediction could be achieved using ALO-LSSVM.