Analyzing Big Smart Metering Data Towards Differentiated User Services: A Sublinear Approach

Abstract

With the advances of the information and communications technology, and smart meters in particular, fine grained user electricity usage of households is available for analyzing electricity usage behaviors. The information makes it possible for utility companies to provide differentiated user services from the time-of-use perspective, i.e., different pricing for users based upon when and how users consume power. In this paper, we present a methodology on differentiated user services based on extracted characteristic consumer load shapes (usage profiles as a function of time) from a large smart meter data set. We identify distinct user subgroups based upon their actual historic usage patterns, which are represented by the proposed electricity usage distributions. Since the big electricity user data cover millions of users and for each user the data are multi-dimensional and in fine-time granularity, we thus propose a sublinear algorithm to make the computation of the differentiated user service model efficient. The algorithm requests an input of only a small portion of users, and a sublinear amount of the electricity data from each of these selected users. We prove that the algorithm provides performance guarantees. Our simulated evaluation demonstrates the effectiveness of our algorithm and the differentiating user service model.