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.
Y. Zha—This work was supported by the National Natural Science Foundation of China (Nos. 61403404, 61773390, 715711871371181) and the Distinguished Natural Science Foundation of Hunan Province (No. 2017JJ1001).
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Zhang, X., Wang, R., Zhang, T., Liu, Y., Zha, Y. (2017). Effect of Transfer Functions in Deep Belief Network for Short-Term Load Forecasting. In: He, C., Mo, H., Pan, L., Zhao, Y. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2017. Communications in Computer and Information Science, vol 791. Springer, Singapore. https://doi.org/10.1007/978-981-10-7179-9_40
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