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

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Filipe Pinto Reis (Author) Other; Helena Viegas Ferreira (Author) Other; Ana Paula Rocha (Author) FEUP

Conference proceedings International

Title: Proceedings of the 14th International Conference on Agents and Artificial Intelligence, ICAART 2022, Volume 3, Online Streaming, February 3-5, 2022

Pages: 359-366

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00W-DY8

DOI: 10.5220/0010898000003116

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8