A Gaussian Process Regression for Natural Gas Consumption Prediction Based on Time Series Data

For several economical, financial and operational reasons, forecasting energy demand becomes a key instrument in energy system management. This paper develops a natural gas forecasting approach, which consists of two major phases: 1) it classifies the natural gas consumption daily pattern sequences into different groups with similar attributes. 2) the design and training of multiple autoregressive Gaussian Process models phase is carried out using the Algerian natural gas market data together with exogenous inputs consisting in weather (temperature) and calendar (day of the week, hour indicator) factors. The main novelty in this work consists of the investigation of multiple different clustering techniques for better analysis and clustering of natural gas consumption data. The impact of the obtained clusters, by each technique, is then summarized and evaluated with respect to the prediction accuracy.