Forecasting natural gas consumption in a cement plant – a case study

Previsão do consumo de gás natural em uma fábrica de cimento – estudo de caso

Abstract

Abstract  This study aims to investigate the forecasting of natural gas consumption in a cement plant in Rio de Janeiro, amidst the transition from the regulated to the open natural gas market, contextualized in the Brazil’s “new gas law” policies. We use demand forecasting to potentially reduce gas acquisition, transportation, and storage costs in the transport pipeline grid. For this purpose, the company’s historical daily demand data has been collected, as well as other exogenous data on related daily time series, all to be used in the training of univariate and multivariate forecasting models. The univariate models fitted in this study are the Naïve Method, Mean Method and Holt-Winters models. The multivariate forecasting models are dynamic regression and SARIMAX models, both of which link the target time series with the external-related ones. After training, these models are used to make predictions on the validation data period, and their accuracy is compared between themselves over five different accuracy metrics, thus highlighting the performance and suitability of each model. The results indicate that multivariate models present significantly better accuracy compared to univariate ones in the test period. The best results from the multivariate models present a MAPE value of 9.9% and an MPE of -0.3%.

Keywords:
Demand forecasting; Natural gas; Time series; Open gas market