Handling the amount of data generated by smart meters is a challenging task for storage, computation and transmission through cellular wireless networks. Data compression and aggregation of this data will be necessary in order to reduce the data volume generated by smart meters. The aim of this work is to investigate different compression techniques in the context of the smart grid communication infrastructure. We study the performance of conventional data compression algorithms applied to daily load profiles of a typical consumer residence. We have proposed applying the Adaptive Huffman(AH) and Lempel-Ziv Welsh (LZW) algorithms on different parts of the network topology (smart meters and data aggregators), and we study the performance and complexity of compression for typical energy measure sampling periods of 10 minutes to one hour. Our results show a significant advantage to applying compression at the aggregator as well as in smart meters, at the cost of extra complexity.
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