Deep Neural Network Regression for Short-Term Load Forecasting of Natural Gas

Authors

Gregory Merkel, Marquette University
Richard J. Povinelli, Marquette UniversityFollow
Ronald H. Brown, Marquette UniversityFollow

Document Type

Conference Proceeding

Language

eng

Publication Date

2017

Publisher

International Institute of Forecasters

Source Publication

International Symposium on Forecasting

Abstract

This paper proposes a short-term load forecasting method for natural gas using deep learning. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields like image recognition and speech processing. This paper explores many aspects of using deep neural networks for time series forecasting. It is determined that the proposed network outperforms traditional artificial neural networks and linear regression based forecasters.

Comments

Published version. Published as part of the proceedings of the International Symposium on Forecasting. Publisher link. © 2017 The Authors. Used with permission.

Recommended Citation

Merkel, Gregory; Povinelli, Richard J.; and Brown, Ronald H., "Deep Neural Network Regression for Short-Term Load Forecasting of Natural Gas" (2017). Electrical and Computer Engineering Faculty Research and Publications. 287.
https://epublications.marquette.edu/electric_fac/287