Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Technologies

DOI: 10.3390/technologies13100435

Authors: Jana Mižáková, Branislav Piteľ, Pavlo Pomin, Alexander Hošovský

Institution: Technical University of Košice

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $1,930