Joint Household Characteristic Prediction via Smart Meter Data

Predicting specific household characteristics (e.g., age of person, household income, cooking style, etc.) from their everyday electricity consumption (i.e., smart meter data) enables energy provider to develop many intelligent business applications or help consumers to reduce their energy consumption. However, most existing works intend to predict single household characteristic via smart meter data independently, and ignore the joint analysis of different characteristics. In this paper, we consider each characteristic as an independent task and intend to predict multiple household characteristics simultaneously by designing a new multi-task learning formulation: discriminative multi-task relationship learning (DisMTRL). Specifically, two main challenges need to be handled: 1) task relationship, that is the embedded structure of relationships among different characteristics and 2) feature learning, there exist redundant features in original training data. To achieve these, our DisMTRL model aims to obtain a simple but robust weight matrix through capturing the intrinsic relatedness among different characteristics by task covariance matrix (MTRL) and incorporating the discriminative features via feature covariance matrix (Dis). For model optimization, we employ an alternating minimization strategy to learn the optimal weight matrix as well as the relationship between tasks by converting feature learning regularization as trace minimization problem. For evaluation, we adopt a smart meter dataset collected from 4232 households in Ireland at a 30 min granularity over an interval of 1.5 years. The experimental results justify the effectiveness of our proposed model.