97 citations · 276 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
Targeted demand response for flexible energy communities using clustering techniques
Sotiris Pelekis, Angelos Pipergias, Evangelos Karakolis +4
The present study proposes clustering techniques for designing demand response (DR) programs for commercial and residential prosumers. The goal is to alter the consumption behavior…
In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance
Sotiris Pelekis, Evangelos Karakolis, Francisco Silva +5
In power grids, short-term load forecasting (STLF) is crucial as it contributes to the optimization of their reliability, emissions, and costs, while it enables the participation o…
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
Sotiris Pelekis, Ioannis-Konstantinos Seisopoulos, Evangelos Spiliotis +4
Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets. However, the non-linearity and non-stationarity of electrici…