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Energies 2018, 11(8), 2008; https://doi.org/10.3390/en11082008

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

Opus College of Engineering, Marquette University, Milwaukee, WI 53233, USA
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Received: 29 June 2018 / Revised: 25 July 2018 / Accepted: 1 August 2018 / Published: 2 August 2018
(This article belongs to the Special Issue Short-Term Load Forecasting by Artificial Intelligent Technologies)
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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). View Full-Text
Keywords: short term load forecasting; artificial neural networks; deep learning; natural gas short term load forecasting; artificial neural networks; deep learning; natural gas
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Merkel, G.D.; Povinelli, R.J.; Brown, R.H. Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression . Energies 2018, 11, 2008.

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