Ioannis P. Panapakidis and Marios N. Moschakis
Comparison of Machine Learning Models for the Prediction of System Marginal Price of Greek Energy Market
148 - 152
2019
13
3
International Journal of Energy and Environmental Engineering
https://publications.waset.org/pdf/10010164
https://publications.waset.org/vol/147
World Academy of Science, Engineering and Technology
The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in dayahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producers perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neurofuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.
Open Science Index 147, 2019