Direct Multiple-Step-Ahead Forecasting of Daily Gas Consumption in Non-Residential Buildings Using Wavelet/RNN-Based Models and Data Augmentation— Comparative Evaluation

The article focuses on forecasting 7-day daily natural gas consumption for a healthcare facility in Slovakia during the winter season (1 October–30 April). The goal is to optimise operational costs while maintaining user comfort and considering economic and environmental indicators. The prediction is based on historical gas consumption and temperature data from eleven heating seasons (taking into account external factors such as COVID-19 and geopolitical conflicts). Linear regression and counting of residuals, Wavelet decomposition and Long Short-Term Memory (LSTM) neural networks were used. Two approaches were tested: firstly, data augmentation using Wavelet decomposition and creating an LSTM model and secondly, individual prediction of wavelet components by LSTM and combining the best-performing models. The second approach, which forecasted each wavelet component separately and then reconstructed the final prediction, yielded the best accuracy (nMAE = 5.71%, NRMSE = 7.80%). The results showed that using predicted temperatures slightly reduced accuracy. Overall, the Wavelet-LSTM model proved to be the most effective method for forecasting gas consumption in healthcare facilities during winter.