Time Sequence Data Analysis of power consumption behavior classification based on K-Mean Clustering Model
This paper analyzes the consumer's energy consumption model, that is, the time series data generated at four quarters per day. Analyze changes in the level of consumption in the daily granularity using random-based verification. The clustering technique is used to kaggle datasets into multiple...
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| Veröffentlicht in: | NeuroQuantology 2022-01, Vol.20 (6), p.7637 |
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| creator | Kayalvizhy, V Banumathi, A |
| description | This paper analyzes the consumer's energy consumption model, that is, the time series data generated at four quarters per day. Analyze changes in the level of consumption in the daily granularity using random-based verification. The clustering technique is used to kaggle datasets into multiple clusters based on similar features and characteristics. |
| doi_str_mv | 10.14704/nq.2022.20.6.NQ22762 |
| format | Article |
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| ispartof | NeuroQuantology, 2022-01, Vol.20 (6), p.7637 |
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| source | EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Classification Clustering Computer science Consumers Customers Data analysis Electricity Energy consumption Machine learning Power Power consumption Time series |
| title | Time Sequence Data Analysis of power consumption behavior classification based on K-Mean Clustering Model |
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