Subject: electricity, price forecasting, machine learning, deep learning
Year: 2022
Type: Proceedings
Title: Forecasting the prices of the day-ahead electricity markets using real data from SEEPEX
Author: Petkovski, Pavle
Author: Dedinec, Aleksandar
Abstract: Forecasting the day-ahead electricity prices can be significant for every business involved in the electricity market. In this paper, we compare different machine learning techniques and algorithms using real data from Serbian Power Exchange, weather data from Serbian capital city Belgrade and generation per production type data for Serbian electricity production. Then on this data, we train different machine learning models: Linear Regression, Decision Trees, Support Vector Machines, Random Forest models, Extreme Gradient Boosting models, Deep Learning models. Metric that we used for comparison between models is the coefficient of determination.
Publisher:
Relation: The 19th International Conference on Informatics and Information Technologies – CIIT 2022
Identifier: oai:repository.ukim.mk:20.500.12188/25702
Identifier: http://hdl.handle.net/20.500.12188/25702
