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- W3169066860 abstract "The spread of smart meters has given grid companies access to a large amount of power load data. Load data contains a great deal of knowledge that helps distinguish load patterns from user categories. It is common practice to cluster daily load curves to mine load patterns. However, the load data collected by smart meters are high-dimensional data, and the common clustering algorithm has poor effect. Therefore, this paper presents an algorithm based on feature engineering and uniform manifold approximation to solve this problem. This algorithm consists of three stages. First, feature extraction is carried out on the pre-processed intelligent meter data, then the extracted statistical features are mixed with the original data as the input data of the dimensionality reduction algorithm, and finally, k-means algorithm is used to cluster the data after dimensionality reduction. Experimental results show that the proposed method has higher clustering accuracy than other methods." @default.
- W3169066860 created "2021-06-22" @default.
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- W3169066860 date "2021-04-01" @default.
- W3169066860 modified "2023-09-27" @default.
- W3169066860 title "Load Curve Clustering Based on Feature Engineering and Uniform Manifold Approximation" @default.
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- W3169066860 doi "https://doi.org/10.1109/acpee51499.2021.9437006" @default.
- W3169066860 hasPublicationYear "2021" @default.
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