Transformation based Parallel Forecasting of Individual Gas Consumption from Smart Meter Data

Abstract

<p>The prediction of energy consumption of users is an important requirement for utilities. Machine learning using neural networks has been applied for predicting household energy consumption due to effectiveness in capturing correlations and patterns. However, the result is predicted by learning over the behavior of the training data as a whole and ignoring the differences between sub-group behavior (i.e., groups of users with similar characteristics). The prediction of energy consumption of users is an important requirement for utilities. Exploiting the behavioral differences between different subgroups in the population can improve the system’s capability to perform parallel processing of similar tasks. This paper proposes an innovative model to simultaneously predict gas consumption of multiple households or population subgroups. The proposed method is based on the use of data transformations and focuses on differences in individual behavior. The evaluation of the model is discussed from three aspects: prediction accuracy, risk of privacy, as well as system robustness. The results shows that the proposed system can achieve multiple prediction simultaneously with reasonable accuracy and be secure against noise and attacks on private information. </p>