Multistep Electricity Price Forecasting for Deregulated Energy Markets: GAN-Based Reinforcement Learning

Electricity Price Forecasting (EPF) plays a vital role in smart grid applications for deregulated electricity markets. Most of the studies tend to investigate the electricity market influencers using forecasting techniques, often losing sight of significance on the sensibility of EPF models to the unstable real-time environment. This project will address a novel EPF based on deep reinforcement learning. The proposed approach uses generative adversarial networks (GAN) to collect synthetic data and increase training set effectively and increase the adaptation of the forecasting system to the environment. The data collected will be fed to a Deep Q learning to generate the final predictions. The proposed GAN-DQL will also be assessed on real data to prove the proposed model advantages compared to several machine learning solutions

Back-Propagation Learning of Infinite-Dimensional Dynamical Systems

This paper presents numerical studies of applying back-propagation learning to a delayed recurrent neural network (DRNN). The DRNN is a continuoustime recurrent neural network having time delayed feedbacks and the backpropagation learning is to teach spatio-temporal dynamics to the DRNN. Since the time-delays make the dynamics of the DRNN infinite-dimensional, the learning algorithm and the learning capability of the DRNN are different from those of the ordinary recurrent neural network (ORNN) having no time-delays. First, two types of learning algorithms are developed for a class of DRNNs. Then, using chaotic signals generated from the Mackey- Glass equation and the R¨ossler equations, learning capability of the DRNN is examined. Comparing the learning algorithms, learning capability, and robustness against noise of the DRNN with those of the ORNN and time delay neural network (TDNN), advantages as well as disadvantages of the DRNN are investigated