Predicting the future trends of customer gas demand as precisely as possible is vital for securing the supply chain from production to distribution. The operations at Air Liquide require the predictions of a Machine Learning forecaster to be coupled with rigorous Uncertainty Quantification (UQ), building trustworthy and informative prediction intervals. To address these industrial needs, we propose to apply Conformal Prediction (CP), a framework that can provide probabilistic guarantees for any underlying predictive model. The problem is formulated as time series forecasting, which may counter the CP hypothesis of data exchangeability. Nevertheless, our experiments show that CP methods enhance the predictive coverage of the tested UQ approaches. We also test EnbPI, a conformal method designed specifically for time series, and propose a locally adaptive variant. To carry out our experiments with prediction intervals using multiple regression models, we introduce our new python library PUNCC and a novel dataset (around 10k observations) provided by Air Liquide which leverages over 7 years of data of weekly gas consumption.