Analyzing electricity consumption via data mining |
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Authors: | Jinshuo Liu Huiying Lan Yizhen Fu Hui Wu Peng Li |
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Institution: | School of Computer,Wuhan University,Wuhan 430072,Hubei,China;2.International School of Software,Wuhan University, Wuhan 430072,Hubei,China |
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Abstract: | This paper proposes a model to analyze the massive data of electricity. Feature subset is determined by the correlation-based
feature selection and the data-driven methods. The attribute season can be classified successfully through five classifiers
using the selected feature subset, and the best model can be determined further. The effects on analyzing electricity consumption
of the other three attributes, including months, businesses, and meters, can be estimated using the chosen model. The data
used for the project is provided by Beijing Power Supply Bureau. We use WEKA as the machine learning tool. The models we built
are promising for electricity scheduling and power theft detection. |
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Keywords: | feature selection multi-classification prediction model data analysis |
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