Comparison of lossless compression schemes for high rate electrical grid time series for smart grid monitoring and analysis

Abstract The smart power grid of the future will utilize waveform level monitoring with sampling rates in the kilohertz range for detailed grid status assessment. To this end, we address the challenge of handling large raw data amount with its quasi-periodical characteristic via lossless compression. We compare different freely available algorithms and implementations with regard to compression ratio, computation time and working principle to find the most suitable compression strategy for this type of data. Algorithms from the audio domain (ALAC, ALS, APE, FLAC & TrueAudio) and general archiving schemes (LZMA, Delfate, PPMd, BZip2 & Gzip) are tested against each other. We assemble a dataset from openly available sources (UK-DALE, MIT-REDD, EDR) and establish dataset independent comparison criteria. This combination is a first detailed open benchmark to support the development of tailored lossless compression schemes and a decision support for researchers facing data intensive smart grid measurements.