Learning ELM-Tree from big data based on uncertainty reduction

Abstract—Learning ELM-Tree from big data based on uncertainty reduction. In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is not a novel concept, < Final Year Projects > it has great potential in the Big Data domain because it reduces computational complexity. The local SVR approach presented here is compared to traditional SVR and to deep neural networks with an H2O machine learning platform for Big Data. Local SVR outperformed both SVR and H2O deep learning in terms of prediction accuracy and computation time. Especially significant was the reduction in training time, local SVR training was an order of magnitude faster than SVR or H2O deep learning.