Authors:
Filipe Pinto Reis
;
Helena Viegas Ferreira
and
Ana Paula Rocha
Affiliation:
Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
Keyword(s):
Energy Market Prediction, Energy Market Simulation, Microservice Architecture, Machine Learning, Deep Learning, Regression, Classification.
Abstract:
Machine and Deep Learning techniques have been widely used in the PowerTAC competition to forecast the price of energy as a bulk, amongst other ends. In order to allow agents to quickly set up, train, and test python-built models, we developed a framework based on a micro-service architecture suitable for predicting wholesale market prices in PowerTAC. The architecture allows for algorithms to be implemented in Python as opposed to the language used in PowerTAC, Java. This paper also presents two datasets, one for the task of classifying whether trades occur, and another for the task of predicting the clearing price of trades that occur. We benchmark these results with basic methods like linear regression, random forest, and a neural network.