Framework to Predict Energy Prices and Trades in the Wholesale Market of PowerTAC

Filipe Reis, Helena Ferreira, Ana Rocha

2022

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.

Paper Citation

in Harvard Style

Reis F., Ferreira H. and Rocha A. (2022). Framework to Predict Energy Prices and Trades in the Wholesale Market of PowerTAC. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-547-0, pages 359-366. DOI: 10.5220/0010898000003116

in Bibtex Style

@conference{icaart22,
author={Filipe Reis and Helena Ferreira and Ana Rocha},
title={Framework to Predict Energy Prices and Trades in the Wholesale Market of PowerTAC},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2022},
pages={359-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010898000003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Framework to Predict Energy Prices and Trades in the Wholesale Market of PowerTAC
SN - 978-989-758-547-0
AU - Reis F.
AU - Ferreira H.
AU - Rocha A.
PY - 2022
SP - 359
EP - 366
DO - 10.5220/0010898000003116