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基于數據挖掘的建筑能耗異常檢測研究
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西安建筑科技大學(xué) 信息與控制工程學(xué)院

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國家自然科學(xué)基金資助項目(51678470)


Research on Abnormal Detection of Building Energy Consumption Based on Data Mining
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    摘要:

    建筑能耗異常檢測對于建筑管理和運行至關(guān)重要,論文提出了一種基于D-S證據理論的不平衡數據多劃分(Multi-partition,MP)聚類(lèi)算法,并構建MP算法能耗異常檢測模型對建筑能耗中的異常值進(jìn)行準確檢測。首先通過(guò)改進(jìn)的信任c均值算法將能耗數據集多劃分;利用基于K-NN的均值漂移算法確定數據集的真實(shí)類(lèi)別個(gè)數;然后根據密度合并規則對能耗數據進(jìn)行合并;最后對未合并的能耗數據再次劃分得到最終的能耗異常檢測結果。UCI數據集驗證結果表明,MP算法對于不平衡數據聚類(lèi)效果良好,能夠有效避免樣本“均勻效應”,降低錯誤率;通過(guò)對某大型商場(chǎng)建筑空調和照明用電能耗異常值檢測,驗證了MP算法能耗異常檢測模型的有效性。

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    Abnormal detection of building energy consumption is very important for building management and operation. In this paper, a multi-partition (MP) clustering algorithm for imbalanced data based on D-S evidence theory is proposed, and the energy consumption anomaly detection model of MP algorithm is constructed to accurately detect the abnormal values in building energy consumption. Firstly, the energy consumption data set is divided into multiple parts by the improved credal c-means algorithm. The KNN-based Mean-shift algorithm is used to determine the number of real categories of the data set. Then the energy consumption data is merged according to the density merging rules. Finally, the energy consumption data that is not merged is divided again to get the final abnormal energy consumption detection results. The UCI data set verification results show that the MP algorithm has a good clustering effect for imbalanced data, which can effectively avoid the "uniform effect" of samples and reduce the error rate. Through detecting the abnormal values of the energy consumption of air conditioning and lighting in a large shopping mall, the validity of the energy consumption anomaly detection model of MP algorithm is verified.

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段中興,梅思雨.基于數據挖掘的建筑能耗異常檢測研究計算機測量與控制[J].,2020,28(7):253-259.

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  • 收稿日期:2020-05-25
  • 最后修改日期:2020-05-26
  • 錄用日期:2020-05-27
  • 在線(xiàn)發(fā)布日期: 2020-07-14
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