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In this paper a case study of a multi-agent system architecture is presented focusing on load imbalance reduction in distribution and end-use consumption parts of the power network. Through a case study is shown the effectiveness of using the proposed multi-agent system architecture. This work also reinforces the confidence in multi-agent systems as a promising approach to address the needs of contemporary Energy Management Systems, which are rapidly moving toward distributed and automated solutions.

The majority of distribution management functionalities rely on load profiles. Customer classification and load analysis have the largest impact on them. In this paper a novel approach for load profile generation is presented. The presented work is based on artificial neural networks: sparse autoencoders and deep belief networks in order to reveal hidden features from data sets.