A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers

This study deals with the description and prediction of the daily consumption of natural gas at the level of individual customers. Unlike traditional group averaging approaches, we are faced with the irregularities of individual consumption series posed by inter-individual heterogeneity, including zeros, missing data, and abrupt consumption pattern changes. Our model is of the nonlinear regression type, with individual customer-specific parameters that, nevertheless, have a common distribution corresponding to the nonlinear mixed effects model framework. It is advantageous to build the model conditionally. The first condition, whether a particular customer has consumed or not, is modeled as a consumption status in an individual fashion. The prediction performance of the proposed model is demonstrated using a real dataset of 62 individual customers, and compared with two more traditional approaches: ARIMAX and ARX.