  Concept drift has potential in smart grid analysis because the socio-economic
behaviour of consumers is not governed by the laws of physics. Likewise there
are also applications in wind power forecasting. In this paper we present
decision tree ensemble classification method based on the Random Forest
algorithm for concept drift. The weighted majority voting ensemble aggregation
rule is employed based on the ideas of Accuracy Weighted Ensemble (AWE) method.
Base learner weight in our case is computed for each sample evaluation using
base learners accuracy and intrinsic proximity measure of Random Forest. Our
algorithm exploits both temporal weighting of samples and ensemble pruning as a
forgetting strategy. We present results of empirical comparison of our method
with original random forest with incorporated "replace-the-looser" forgetting
andother state-of-the-art concept-drfit classifiers like AWE2.
