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

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.

[1]  Ruifang Liu,et al.  Stacked denoising autoencoder and dropout together to prevent overfitting in deep neural network , 2015, 2015 8th International Congress on Image and Signal Processing (CISP).

[2]  Wing W. Y. Ng,et al.  A robust correlation analysis framework for imbalanced and dichotomous data with uncertainty , 2019, Inf. Sci..

[3]  C.S. Ozveren,et al.  Short term load forecasting using Multiple Linear Regression , 2007, 2007 42nd International Universities Power Engineering Conference.

[4]  Jong Yih Kuo,et al.  Using Stacked Denoising Autoencoder for the Student Dropout Prediction , 2017, 2017 IEEE International Symposium on Multimedia (ISM).

[5]  Tareq Hossen,et al.  Short-term load forecasting using deep neural networks (DNN) , 2017, 2017 North American Power Symposium (NAPS).

[6]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[7]  Hermann Ney,et al.  From Feedforward to Recurrent LSTM Neural Networks for Language Modeling , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[8]  Wei Xiong,et al.  Stacked Convolutional Denoising Auto-Encoders for Feature Representation , 2017, IEEE Transactions on Cybernetics.

[9]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[10]  Ling Liu,et al.  A Hybrid Neural Network Model for Power Demand Forecasting , 2019, Energies.

[11]  Richard J. Povinelli,et al.  Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression † , 2018, Energies.

[12]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[13]  Vahid Rostami,et al.  Adaptive Color Mapping for NAO Robot Using Neural Network , 2014 .

[14]  Xiangang Li,et al.  Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition , 2014, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Yongqiang Wang,et al.  Efficient Training and Evaluation of Recurrent Neural Network Language Models for Automatic Speech Recognition , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[16]  Jaime Lloret,et al.  A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings , 2014, IEEE Communications Surveys & Tutorials.

[17]  Chia-Nan Ko,et al.  Short-term load forecasting using lifting scheme and ARIMA models , 2011, Expert Syst. Appl..

[18]  Tomonobu Senjyu,et al.  Next day load curve forecasting using recurrent neural network structure , 2004 .

[19]  Eenjun Hwang,et al.  Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron , 2018, Energies.

[20]  Shahrokh Valaee,et al.  Locomotion Activity Recognition Using Stacked Denoising Autoencoders , 2018, IEEE Internet of Things Journal.

[21]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[22]  Yuan Zhang,et al.  Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.

[23]  Ran Li,et al.  Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN , 2018, IEEE Transactions on Smart Grid.

[24]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[25]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[26]  Jian Ma,et al.  A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network , 2018, Energies.

[27]  Yu Li,et al.  Bilinear models-based short-term load rolling forecasting of smart grid , 2012, Proceedings of the 31st Chinese Control Conference.

[28]  Yi Wang,et al.  Enhancing short-term probabilistic residential load forecasting with quantile long–short-term memory , 2009 .

[29]  Zijun Zhang,et al.  Short-Term Electricity Price Forecasting With Stacked Denoising Autoencoders , 2017, IEEE Transactions on Power Systems.

[30]  Vladimir Ceperic,et al.  A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines , 2013, IEEE Transactions on Power Systems.

[31]  Dianguo Xu,et al.  A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest , 2016 .

[32]  Yuqing Chang,et al.  Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting , 2018, Energies.

[33]  Jürgen Schmidhuber,et al.  A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks , 2006 .

[34]  Lin Wang,et al.  Denoising hybrid noises in image with stacked autoencoder , 2015, 2015 IEEE International Conference on Information and Automation.

[35]  J. W. Taylor,et al.  Short-Term Load Forecasting With Exponentially Weighted Methods , 2012, IEEE Transactions on Power Systems.

[36]  Ali Ouni,et al.  Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting , 2019, Energies.

[37]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.