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- W3118503757 abstract "Identifying patterns in electric utility net load data in a time-series format is very useful in preparing the operation for next day. Machine learning algorithms have been used in other domains and those concepts are applied in this paper on real-world net load measurement data. Clustering is the practice of grouping data with similar characteristics as determined by the distance measure. The K-means clustering algorithm is utilized here with actual electric utility data. The paper uses the standard distance measure, Euclidean distance (ED), and compares its performance against the dynamic time warping (DTW) measure. An actual case study with real data is presented, and DTW distance measure-based method observed to result better accuracy compared to the ED based method for substation net load measurements predominantly with residential customers." @default.
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- W3118503757 date "2020-10-12" @default.
- W3118503757 modified "2023-09-23" @default.
- W3118503757 title "Improving the Accuracy of Clustering Electric Utility Net Load Data using Dynamic Time Warping" @default.
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- W3118503757 doi "https://doi.org/10.1109/td39804.2020.9299915" @default.
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