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Title |
A regional energy consumption analysis model using a novel outlier removal algorithm and k-means clustering method |
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Author |
Yuchen Wang, Shuxiang Xu, Wei Liu |
| Citation |
Vol. 16 No. 3 pp. 21-29 |
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Abstract |
The regional energy-saving work is an important part of China’s energy conservation projects. In this paper, we developed a regional energy consumption analysis model using the energy consumption data from a typical Chinese industrial city, Shaoxing. We incorporated a well-known data mining tool, the k-means clustering method, into our model to automatically classify our energy consumption data into low, medium and high clusters representing different energy consumption levels. This classification provides a basis for further analysis to help governments and enterprises to use energy more efficiently. However, there are a few of potential outliers in our data set, and the result of k-means might be strongly influenced by these outliers. To reduce the impact of these extremely large data points, we proposed a distance-based outliers removal algorithm as well as a corresponding parameters choosing algorithm which provides tuning parameters to make a balance between keeping and removing far away points. The experimental results show that our algorithms can effectively reduce the influence of outliers and make the k-means results more meaningful. The relationship between levels of consumption and industrial output was also examined as one possible way of further analysis based on our model. |
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Keywords |
machine learning data mining k-means outlier removal regional energy consumption analysis model |
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