Feature Augmentation of Classifiers Using Learning Time Series Shapelets Transformation for Night Setback Classification of District Heating Substations

摘要

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.