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- W2073003026 abstract "The advent smart meters and advanced communication infrastructures catalyzes numerous smart grid applications such as dynamic demand response, and paves the way to solve challenging research problems in sustainable energy consumption. The space solution possibilities are restricted primarily by the huge amount generated data requiring considerable computational resources and efficient algorithms. To overcome this Big Data challenge, data clustering techniques have been proposed. Current approaches however do not scale in the face the increasing dimensionality problem, where a cluster point is represented by the entire customer consumption time series. To overcome this aspect we first rethink the way cluster points are created and designed, and then devise OPTIC, an efficient online time series clustering technique for demand response (DR), in order to analyze high volume, high dimensional energy consumption time series data at scale, and on the fly. OPTIC is randomized in nature, and provides optimal performance guarantees (Section 2.3.2) in a computationally efficient manner. Unlike prior work we (i) study the consumption properties the whole population simultaneously rather than developing individual models for each customer separately, claiming it to be a 'killer' approach that breaks the of dimensionality in online time series clustering, and (ii) provide tight performance guarantees in theory to validate our approach. Our insights are driven by the field sociology, where collective behavior often emerges as the result individual patterns and lifestyles. We demonstrate the efficacy OPTIC in practice using real-world data obtained from the fully operational USC microgrid." @default.
- W2073003026 created "2016-06-24" @default.
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- W2073003026 date "2015-07-14" @default.
- W2073003026 modified "2023-10-14" @default.
- W2073003026 title "Challenge" @default.
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- W2073003026 doi "https://doi.org/10.1145/2768510.2770932" @default.
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