IRMAC: Interpretable Refined Motifs in Binary Classification for smart grid applications
نویسندگان
چکیده
Modern power systems are experiencing the challenge of high uncertainty with increasing penetration renewable energy resources and electrification heating systems. In this paradigm shift, understanding electricity users' demand is utmost value to retailers, aggregators, policymakers. However, behind-the-meter (BTM) equipment appliances at household level unknown other stakeholders mainly due privacy concerns tight regulations. paper, we seek identify residential consumers based on their BTM equipment, rooftop photovoltaic (PV) electric heating, using imported/purchased data from utility meters. To solve problem an interpretable, fast, secure, maintainable solution, propose integrated method called Interpretable Refined Motifs And binary Classification (IRMAC). The proposed comprises a novel shape-based pattern extraction technique, Motif (RM) discovery, single-neuron classifier. first part extracts sub-pattern long time series considering frequency occurrences, average dissimilarity, dynamics while emphasising specific times annotated distances. second identifies types linear complexity preserving transparency algorithms. With real Australia Denmark, tested verified in identifying PV owners electrical system users.
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ژورنال
عنوان ژورنال: Engineering Applications of Artificial Intelligence
سال: 2023
ISSN: ['1873-6769', '0952-1976']
DOI: https://doi.org/10.1016/j.engappai.2022.105588