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2017 | OriginalPaper | Chapter

Effect of Transfer Functions in Deep Belief Network for Short-Term Load Forecasting

Authors : Xiaoyu Zhang, Rui Wang, Tao Zhang, Yajie Liu, Yabin Zha

Published in: Bio-inspired Computing: Theories and Applications

Publisher: Springer Singapore

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Abstract

Deep belief network (DBN) has become one of the most popular techniques for short-term load forecasting. The transfer functions play a vital role on the effective of DBN. In this study, different combinations of three commonly used transfer functions, i.e., logsig, purelin and tansig, in a DBN are examined. Experimental results show that a combination of purelin and tansig transfer functions produces the best load forecasting, and is therefore recommended to use.

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Metadata
Title
Effect of Transfer Functions in Deep Belief Network for Short-Term Load Forecasting
Authors
Xiaoyu Zhang
Rui Wang
Tao Zhang
Yajie Liu
Yabin Zha
Copyright Year
2017
Publisher
Springer Singapore
Book
Bio-inspired Computing: Theories and Applications

Print ISBN: 978-981-10-7178-2

Electronic ISBN: 978-981-10-7179-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-7179-9_40

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