Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting

Peng Liu, Peijun Zheng and Ziyu Chen
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Peng Liu: Yangzhong Intelligent Electrical Institute, North China Electric Power University, Yangzhong 212200, China
Peijun Zheng: Yangzhong Intelligent Electrical Institute, North China Electric Power University, Yangzhong 212200, China
Ziyu Chen: Yangzhong Intelligent Electrical Institute, North China Electric Power University, Yangzhong 212200, China

Energies, 2019, vol. 12, issue 12, 1-15

Abstract: Accurate short-term electric load forecasting is significant for the smart grid. It can reduce electric power consumption and ensure the balance between power supply and demand. In this paper, the Stacked Denoising Auto-Encoder (SDAE) is adopted for short-term load forecasting using four factors: historical loads, somatosensory temperature, relative humidity, and daily average loads. The daily average loads act as the baseline in final forecasting tasks. Firstly, the Denoising Auto-Encoder (DAE) is pre-trained. In the symmetric DAE, there are three layers: the input layer, the hidden layer, and the output layer where the hidden layer is the symmetric axis. The input layer and the hidden layer construct the encoding part while the hidden layer and the output layer construct the decoding part. After that, all DAEs are stacked together for fine-tuning. In addition, in the encoding part of each DAE, the weight values and hidden layer values are combined with the original input layer values to establish an SDAE network for load forecasting. Compared with the traditional Back Propagation (BP) neural network and Auto-Encoder, the prediction error decreases from 3.66% and 6.16% to 2.88%. Therefore, the SDAE-based model performs well compared with traditional methods as a new method for short-term electric load forecasting in Chinese cities.

Keywords: short-term load forecasting; deep learning; stacked denoising auto-encoder neural network (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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