Data-Driven Copy-Paste Imputation for Energy Time Series

Abstract

A cornerstone of the worldwide transition to smart grids are smart meters. Smart meters typically collect and provide energy time series that are vital for various applications, such as grid simulations, fault-detection, load forecasting, load analysis, and load management. Unfortunately, these time series are often characterized by missing values that must be handled before the data can be used. A common approach to handle missing values in time series is imputation. However, existing imputation methods are designed for power time series and do not take into account the total energy of gaps, resulting in jumps or constant shifts when imputing energy time series. In order to overcome these issues, the present paper introduces the new Copy-Paste Imputation (CPI) method for energy time series. The CPI method copies data blocks with similar properties and pastes them into gaps of the time series while preserving the total energy of each gap. The new method is evaluated on a real-world dataset that contains six shares of artificially inserted missing values between 1 and 30%. It outperforms by far the three benchmark imputation methods selected for comparison. The comparison furthermore shows that the CPI method uses matching patterns and preserves the total energy of each gap while requiring only a moderate run-time.

pyWATTS: Python Workflow Automation Tool for Time Series

“…Lastly, we maintain an up-to-date documentation. Based on the annotated source code and restructured text files, the documentation 8 of pyWATTS is automatically generated using sphinx 9 and readthedoc 10 .…”

“…The developer team, for example, wants to use pyWATTS in various research applications in the future. For preprocessing, we plan to extend pyWATTS with the Copy Paste Imputation of missing values for energy time series as described in [10]. We also plan to use pyWATTS for time series forecasting, e. g. by using Profile Neural Networks [3].…”