Practical Application of Deep Modified Autoencoder Technique to Electricity Price Forecasting

This paper proposes a method for electricity price forecasting (EPF) with Deep Modified Autoencoder (DMAE). It is based on a deep model of the modified Autoencoder (MAE) that improves the learning process by adding noise to the original data. EPF is hard to handle due to higher volatility in comparison with other time series in power systems. Autoencoder (AE) plays a key role in reducing the dimensionality of input variables in Deep Neural Network (DNN), which is useful for constructing reasonable models with fewer parameters. In this paper, DMAE is proposed as a pretraining technique in Deep Neural Network (DNN). It may be regarded as an integration model that combines MAE with Stacked AE. The proposed method integrates DMAE with Multi-layer perceptron (MLP) to construct DNN. The effectiveness of the proposed method is tested for real data in Boston, MA, USA.