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Unsupervised and Supervised Learning Combined Power Load Curve Classification Based on Sequential Trajectory Feature Extraction Algorithm
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DOI：10.1109/ACCESS.2022.3201552.png)
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Advancements in smart grid technology and the extensive applications of electric power big data have made in-depth exploration of the behavioral characteristics of power consumers highly necessary for further development of the electricity market. This paper proposes an effective and interpretable load curve classification method that based on sequential trajectory feature learning and a random forest algorithm. Firstly, the unsupervised K-medoid clustering algorithm is used to obtain and filter precise category labels. Next, the fused lasso generalized eigenvector (FLAG) technique is used to search for interpretable sub-sequences from the labeled data in order to properly account for the sequential trajectory feature of load curves and increase the speed of the computation process. Following that, shapelet transformation is used to extract the sequential trajectory features from original data. Finally, in order to inherit the interpretability of shapelet, the random forest is trained on the sequential trajectory features. The simulated examples based on the real load curves of the specific city in China were investigated in order to assess the performance of the proposed load curve categorization approach. The results of the simulation demonstrate that the proposed approach has considerable advantages in terms of effectiveness, accuracy, and interpretability of load classification.
Keyword:
Classification algorithms
Clustering algorithms
Random forests
Load modeling
Feature extraction
Data mining
Signal processing algorithms
Unsupervised learning
Supervised learning
Sequential analysis
Load curve classification
K-mediods
shapelet
random forest
sequential trajectory features
unsupervised learning
supervised learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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