Document Type
Article
Language
eng
Publication Date
2018
Publisher
MDPI
Source Publication
Energies
Source ISSN
1996-1073
Abstract
Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE).
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Merkel, Gregory; Povinelli, Richard James; and Brown, Ronald H., "Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression" (2018). Electrical and Computer Engineering Faculty Research and Publications. 507.
https://epublications.marquette.edu/electric_fac/507
ADA accessible version
Comments
Published version. Energies, Vol. 11, No. 8 (2018): 2008. DOI. © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).