Learning matrix profile method for discord-based attribution of electricity consumption pattern behavior

Abstract

This paper frames itself in analyzing complex electricity load measurements to attribute the consumption behavior to a specific entity. Attribution may be seen as the first step in several grid-based activities, like energy management, privacy, and identification of illicit activities. This work proposes and tests a novel approach that utilizes consumption discords as the analysis carrier for attributing multiple load patterns to specific consumers. The discord-driven analysis is performed utilizing the synergism of the Matrix Profile with each of two well-established supervised learning classification methods: the K-Nearest-Neighbor and Support-Vector-Machine. The proposed approach is applied in attributing electricity consumption pattern behaviors to a set of academic institutions that are comprised of multiple academic units. Notably, multiple units within the same entity exhibit different consumption behavior, thus imposing a high challenge in attributing the unit’s consumption behavior to its entity of origin when there is a variety of targeted entities. Obtained results demonstrate that the load discord-based MP-KNN and MP-SVM combinations provide higher identification accuracy than the state-of-the-art method of performing supervised classification with a feed-forward artificial neural network of the full load patterns. The identification accuracy of the proposed method outperformed the ANN load classification by approximately 13%.

Keywords:

• Matrix Profile
• Supervised Learning
• Consumption attribution

Disclosure statement

No potential conflict of interest was reported by the authors.