Stacked Denoising Auto-Encoder for Short-Term Load Forecasting
Deep Learning with Stacked Denoising Auto-Encoder Algorithm
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The Stacked Denoising Auto-Encoder (SDAE) is adopted firstly for short-term load forecasting using four factors. The daily average loads act as the baseline in final forecasting tasks. In this research, 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 ...
The Stacked Denoising Auto-Encoder (SDAE) is adopted firstly for short-term load forecasting using four factors. The daily average loads act as the baseline in final forecasting tasks. In this research, 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.
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