Gregory D. Merkel
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Gregory D. Merkel has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Richard J. Povinelli
Department of Electrical and Computer Engineering, Marquette University, 1515 W. Wisconsin Avenue, Milwaukee, WI 53233, USA
1 shared publication - Ronald H. Brown
Opus College of Engineering, Marquette University, Milwaukee, WI 53233, USA
1 shared publication
Latest Publications
- Open Access
Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †
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).
Open AccessShort-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †
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).