Title: “Electricity Price Forecasting with Attention-Based Neural Network Architectures”

Abstract:
In recent years, electricity markets have undergone significant changes, driven by the introduction of auction-based power trading mechanisms, ambitious greenhouse-gas emis-
sion targets, and the increasing penetration of Renewable Energy Sources (RES), which together make electricity price forecasting a highly volatile, multifactorial problem. These developments have heightened the importance of quantifying forecast uncertainty and have motivated growing interest in probabilistic forecasting, particularly with deep learning models. This thesis contributes to probabilistic electricity price forecasting by applying and evaluating modern attention-based architectures, namely the Transformer, the Temporal Fusion Transformer (TFT), PatchTST, and the foundation model Chronos-2, adapted to produce quantile forecasts. The models are first evaluated on the GEFCom2014 dataset under the same experimental setting as the original competition, on which, to the best of our knowledge, such deep learning architectures have not previously been applied; their results are compared against both classical statistical and neural benchmarks, as well as the competition-winning solutions. To assess robustness under more realistic and demanding conditions the same framework is then applied to recent Greek electricity market data, which exhibit high volatility, missing values, and negative prices. The forecasting task is to predict the next 24 hours from a historical observation window together with available exogenous features, with performance evaluated using the pinball loss alongside reliability and sharpness metrics. The results show that the proposed models consistently outperform the classical benchmarks on GEFCom2014, in some cases matching or exceeding the competition-winning solutions. On the more complex Greek data the models remain effective, though with some degradation attributable to the increased difficulty of the series, indicating room for further improvement. Overall, Chronos-2 achieves the strongest performance, followed closely by the Transformer.

Date-Time: 5/10/2026 – 16:00 PM
Examination Committee:
Associate Professor Aggelos Pikrakis, University of Piraeus
Dr. Vassilis Katsouros, Director, ILSP/Athena Research Center
Dr. Kosmas Kritsis, ILSP/Athena Research Center

Msc Thesis Presentation
Monday, October 5 · 16:00pm – 17:00pm
Time zone: Europe/Athens
Webex joining info
Video call link: https://unipi.webex.com/meet/webex-host2