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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Filipe Pinto Reis (Autor) Outra; Helena Viegas Ferreira (Autor) Outra; Ana Paula Rocha (Autor) FEUP

Ata de Conferência Internacional

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

Páginas: 359-366

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8