Assessment of unsupervised standard pattern recognition methods for industrial energy time series


Finding and extracting standard patterns in energy time series is very important to many real-world applications. Hence, there exists a multitude of pattern recognition algorithms with a majority of them being supervised ones. The advantage of supervision is that it can easily be checked if the algorithm is performing well or not. However, if no labels are available, an unsupervised pattern search is necessary. This search is faced with the challenge of how to measure success. Thus the question arises, when is a found pattern – for example a motif or a mean cluster curve – really describing the standard behaviour of a process and not just some kind of irrelevant behaviour? The present paper introduces a new method to assess two methods – namely clustering and motif discovery – in their quest to find standard profiles in energy time series data from industrial processes. Although both methods share the same aim, the results are incongruent. This finding has profound implications for real-world applications.

e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems