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

Gregory D. Merkel, Richard J. Povinelli and Ronald H. Brown
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Gregory D. Merkel: Opus College of Engineering, Marquette University, Milwaukee, WI 53233, USA
Richard J. Povinelli: Opus College of Engineering, Marquette University, Milwaukee, WI 53233, USA
Ronald H. Brown: Opus College of Engineering, Marquette University, Milwaukee, WI 53233, USA

Energies, 2018, vol. 11, issue 8, 1-12

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 ).

Keywords: short term load forecasting; artificial neural networks; deep learning; natural gas (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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