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.
Highlights
The stability of the electric power system is guaranteed by the balance between electric load and supply
The trend extrapolation adopted in this paper uses the information of historical data to find the trend of historical load changes based on Stacked Denoising Auto-Encoder (SDAE) unsupervised algorithm
The advantage of SDAE is its capability to continuously update the dataset based on predicted results, which enables it to learn the patterns of previous fluctuations and correct the fluctuation trend of data in the training set by the latter consequence [34]
Summary
The stability of the electric power system is guaranteed by the balance between electric load and supply. Machine learning methods, including artificial neural network (ANN), random forest (RF) and support vector regression (SVR) have been widely used to predict the change of electric loads. In [21], Tian et al designed a novel model combining LSTM and convolutional neural networks (CNN) In this model, the LSTM was used to learn the useful information for a long time and capture the long-term dependency. The rest of this paper is structured as follows: in Section 2, the background of electric load forecasting is discussed; in Section 3, the SDAE deep neural network is introduced; in Section 4, an SDAE-based model is proposed and its forecasting results are shown; in Section 5, conclusions and future work are presented
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