23 citations · 25 across the 6 of their papers we have counts for
7 papers
Distributed JEPA: A Self-Supervised Framework for Energy Forecasting
Liana Toderean, Tudor Cioara, Vasilis Michalakopoulos +3
Traditional energy forecasting solutions rely on task-specific supervision and energy asset representations, limiting transferability and the ability to capture general temporal dy…
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…
Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows
Vasilis Michalakopoulos, Christoforos Menos-Aikateriniadis, Elissaios Sarmas +3
This study investigates the performance of machine learning models in forecasting electricity Day-Ahead Market (DAM) prices using short historical training windows, with a focus on…
From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins
Gabriel Antonesi, Tudor Cioara, Ionut Anghel +3
Artificial intelligence (AI) has long promised to improve energy management in smart grids by enhancing situational awareness and supporting more effective decision-making. While t…
A multi-dimensional unsupervised machine learning framework for clustering residential heat load profiles
Vasilis Michalakopoulos, Elissaios Sarmas, Viktor Daropoulos +4
Central to achieving the energy transition, heating systems provide essential space heating and hot water in residential and industrial environments. A major challenge lies in effe…
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…
A Machine Learning-Based Framework for Clustering Residential Electricity Load Profiles to Enhance Demand Response Programs
Vasilis Michalakopoulos, Elissaios Sarmas, Ioannis Papias +3
Load shapes derived from smart meter data are frequently employed to analyze daily energy consumption patterns, particularly in the context of applications like Demand Response (DR…