Utility companies are an integral part of the smart grid, providing consumers with a broad range of energy management programs. The quality of service is based on the measurements obtained from smart metering infrastructures, which can further be improved by sensing at finer resolutions. However, sensing at higher resolutions poses serious challenges both in terms of storage and communication overload due to overgrowing traffic. Compressive sensing is a data compression technique that accounts for the sparsity of electricity consumption pattern in a transformation basis and achieves subNyquist compression. To the best of the authors’ knowledge, this is the first study to use the semi-tensor product (STP) for compressed sensing (CS) of power consumption data in the smart grid. In contrast to the conventional CS, the proposed approach has the advantage of reducing the dimension of the sensing matrix needed to sense the signal, thereby significantly lowering the storage requirements. In this regard, we present a comparative study highlighting the difference in compression performance with the conventional CS and STP based CS, where the transformation basis used is Haar and Hankel. We present the results on three publicly available datasets at different sampling rates and outline the key findings of the study.