23 citations · 48 across the 7 of their papers we have counts for
7 papers
MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model
Charilaos Papaioannou, Ioannis Tsantilas, Dimitris Giannakakos +9
Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-distribution error degrade the…
A Trustworthy By Design Classification Model for Building Energy Retrofit Decision Support
Panagiota Rempi, Sotiris Pelekis, Alexandros Menelaos Tzortzis +4
Improving energy efficiency in residential buildings is critical to combating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings, which contribut…
AI4EF: Artificial Intelligence for Energy Efficiency in the Building Sector
Alexandros Menelaos Tzortzis, Georgios Kormpakis, Sotiris Pelekis +4
AI4EF, Artificial Intelligence for Energy Efficiency, is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimizat…
Trustworthy artificial intelligence in the energy sector: Landscape analysis and evaluation framework
Sotiris Pelekis, Evangelos Karakolis, George Lampropoulos +4
The present study aims to evaluate the current fuzzy landscape of Trustworthy AI (TAI) within the European Union (EU), with a specific focus on the energy sector. The analysis enco…
Data-driven building energy efficiency prediction using physics-informed neural networks
Vasilis Michalakopoulos, Sotiris Pelekis, Giorgos Kormpakis +3
The analytical prediction of building energy performance in residential buildings based on the heat losses of its individual envelope components is a challenging task. It is worth…
Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series
Alexandros-Menelaos Tzortzis, Sotiris Pelekis, Evangelos Spiliotis +3
Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand…
DeepTSF: Codeless machine learning operations for time series forecasting
Sotiris Pelekis, Evangelos Karakolis, Theodosios Pountridis +4
This paper presents DeepTSF, a comprehensive machine learning operations (MLOps) framework aiming to innovate time series forecasting through workflow automation and codeless model…