District heating systems that distribute heat through pipelines to residential and commercial buildings have been widely used in Northern Europe, and according to the latest study, district heating shares the most heat supply market in Sweden. Therefore, energy efficiency of district heating systems is of great interest to energy stakeholders. However, it is not uncommon that district heating systems fail to achieve the expected performance due to various faults or inappropriate operations. Night setback is one control strategy, which has been proved to be not a suitable setting for well-insulated modern buildings in terms of both economic factors and energy efficiency. From the literature, shapelets algorithms not only provide interpretable results but also proved to be effective in time series classification. However, they have not been explored to solve the problem in energy domain. In this study, a feature augmentation approach is proposed based on learning time series shapelets and shapelet transformation, aiming to improve the performance of classifiers for night setback classification. To evaluate the effectiveness of the proposed approach, data of 10 anonymous substations in Sweden are used in the case study. The proposed method is applied to six commonly used baseline classifiers: Support Vector Classifier, Multilayer Perceptron Neural Network, Logistic Regression, K-Nearest Neighbor, Decision Trees, and Random Forest. Precision, recall, and f1 score are used as the performance measures. The results of out-of-sample testing show that it is possible to improve the generalization ability of classifiers by applying the proposed approach. In addition, the highest f1 score of out-of-sample testing is achieved by DT classifier whose f1 score is increased from 0.599 to 0.711 for identifying night setback case and from 0.749 to 0.808 for identifying nonnight setback case using the proposed feature augmentation approach.

中文翻译：

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基于学习时间序列小波变换的分类器特征增强用于区域供热站夜间倒退分类

通过管道将热量分配到住宅和商业建筑的区域供热系统在北欧已得到广泛使用，根据最新研究，区域供热在瑞典的供热市场中占有最大份额。因此，区域供热系统的能源效率引起了能源利益相关者的极大兴趣。但是，由于各种故障或不适当的操作，区域供热系统未能达到预期的性能并不少见。夜间倒退是一种控制策略，从经济因素和能源效率两方面，事实证明，该策略不适用于隔热良好的现代建筑。根据文献，shapelets算法不仅提供了可解释的结果，而且在时间序列分类中被证明是有效的。然而，他们尚未探索解决能源领域的问题。在这项研究中，提出了一种基于学习时间序列shapelet和shapelet变换的特征增强方法，旨在提高夜挫分类的分类器性能。为了评估该方法的有效性，案例研究中使用了瑞典10个匿名变电站的数据。所提出的方法应用于六个常用的基线分类器：支持向量分类器，多层感知器神经网络，逻辑回归，K最近邻，决策树和随机森林。精度，召回率和f1分数用作绩效指标。样本外测试的结果表明，通过应用所提出的方法可以提高分类器的泛化能力。此外，