Currently smart meter data analytics has received enormous attention because it allows utility companies to analyze customer consumption behavior in real time. However, the amount of data generated by these sensors is very large. As a result, analytics performed on top of it become very expensive. Furthermore, smart meter data contains very detailed energy consumption measurement which can lead to customer privacy breach and all risks associated with it. In this work, we address the problem on how to reduce smart meter data numerosity and its detailed measurement while maintaining its analytics accuracy. We convert the data into symbolic representation and allow various machine learning algorithms to be performed on top of it. In addition, our symbolic representation admit an additional advantage to allow also algorithms which usually work on nominal and string to be run on top of smart meter data. We provide an experiment for classification and forecasting tasks using real-world data. And finally, we illustrate several directions to extend our work further.