  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.
