Recurrent neural network based user classification for smart grids

Power consuming users and buildings with different power consumption patterns may be treated with different conditions and can be taken into consideration with different parameters during capacity planning and distribution. Thus the automated, unsupervised categorization of power consumers is a very important task of smart power transmission systems. Knowing the behavioral categories of power consumers better models can be created which can be used for better behavior forecast which is an important task for load balancing. One of the existing best solutions for consumer classification is the consumption forecast based scheme which applies nonlinear forecast techniques to determine the class assignment for new consumers. In this paper, we present new results on the classification of consumers using recurrent neural networks in the forecast based classification framework. The results are compared with existing classification methods using real, measured power consumption data. We demonstrate that consumer classification performed by recurrent neural networks can outperform existing methods as in several cases the correct class assignment rate is near to 100%.