# IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications

arXiv: https://arxiv.org/abs/2109.13732 · HTML: https://mlsanity.com/paper/2109.13732/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Understanding Overparameterization in Generative Adversarial Networks](https://mlsanity.com/paper/2104.05605/) (2021)
- [Matrix Profile Goes MAD: Variable-Length Motif And Discord Discovery in Data Series](https://mlsanity.com/paper/2008.13447/) (2020)
- [Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW](https://mlsanity.com/paper/2009.07907/) (2020)
- [Transient Stability Analysis with Physics-Informed Neural Networks](https://mlsanity.com/paper/2106.13638/) (2021)
- [A CNN adapted to time series for the classification of Supernovae](https://mlsanity.com/paper/1901.00461/) (2019)
- [Efficient Matrix Profile Computation Using Different Distance Functions](https://mlsanity.com/paper/1901.05708/) (2019)
- [Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters](https://mlsanity.com/paper/2012.07449/) (2020)
- [FastDTW is approximate and Generally Slower than the Algorithm it Approximates](https://mlsanity.com/paper/2003.11246/) (2020)

## Abstract (arXiv)

Modern power systems are experiencing the challenge of high uncertainty with the increasing penetration of renewable energy resources and the electrification of heating systems. In this paradigm shift, understanding electricity users' demand is of utmost value to retailers, aggregators, and policymakers. However, behind-the-meter (BTM) equipment and appliances at the household level are unknown to the other stakeholders mainly due to privacy concerns and tight regulations. In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) systems and electric heating, using imported/purchased energy data from utility meters. To solve this problem with an interpretable, fast, secure, and maintainable solution, we propose an integrated method called Interpretable Refined Motifs And binary Classification (IRMAC). The proposed method comprises a novel shape-based pattern extraction technique, called Refined Motif (RM) discovery, and a single-neuron classifier. The first part extracts a sub-pattern from the long time series considering the frequency of occurrences, average dissimilarity, and time dynamics while emphasising specific times with annotated distances. The second part identifies users' types with linear complexity while preserving the transparency of the algorithms. With the real data from Australia and Denmark, the proposed method is tested and verified in identifying PV owners and electrical heating system users.