Electric Load Forecasting Using EEMD and Machine Learning Techniques

The significance of electricity cannot be overlooked in terms of advancements in economic and technological fields. In this study, Ensemble Empirical Mode Decomposition (EEMD) method in combination with the Ensemble Bi-Long Short Term Memory (EBiLSTM) and Support Vector Machine (SVM) is used. Non linear and non stationary IMFs are forecast using EBiLSTM forecasting algorithm as it performs efficiently in complex and non linear scenario. Whereas, linear IMFs are forecast using SVM as EBiLSTM take high computational time unlike SVM. The proposed technique EEMD-EBiLSTM-SVM gives good results.