Resources

Accurate electricity price forecasting in volatile Day-Ahead Markets requires models that adapt quickly to changing conditions. This publication proposes a short-window learning approach (7–90 days) and compares deep learning with boosting methods across European markets. Results show that LightGBM delivers superior accuracy and robustness, particularly in capturing seasonal patterns and peak price events, supporting efficient and reliable forecasting under data-scarce conditions.

Accurate long-term assessment of wind resources is essential for reliable wind farm development, particularly in complex mountainous terrains. This publication presents an integrated approach combining short-term ground measurements with long-term ERA5 data through an advanced measure–correlate–predict method. By incorporating uncertainty analysis and terrain effects, the framework provides realistic estimates of annual energy production and exceedance probabilities, supporting more robust and bankable wind energy projections.

Traditional windmills of Lasithi Plateau have long supported local water supply through their distinctive fabric-sail rotor systems, designed to operate under varying wind conditions. Building on this heritage, the study examines the aerodynamic performance and structural behaviour of such systems, with particular attention to sail geometry, supporting structures, and passive deformation mechanisms. The results highlight how flexible sails can naturally regulate loads and enhance resilience in high winds, offering valuable insights for the design of modern, small-scale wind turbines inspired by low-cost and robust traditional solutions.

The evolution of energy systems toward smart grids is increasing the availability of building-level data, yet many buildings still face limited historical records, hindering accurate short-term load forecasting. Transfer learning offers a promising solution by enabling models trained on data-rich buildings to be adapted to data-scarce environments. By testing several forecasting models, including the advanced Temporal Fusion Transformer, this publication shows that this approach consistently improves prediction accuracy, with notable reductions in error and strong performance across different building contexts.

Wind energy penetration has radically increased in the last decade constituting one of the main renewable energy resources of the energy transition. However, its intermittent nature necessitate the development of accurate Wind Power Forecasting (WPF), essential in several applications, including grid reliability and cost minimisation. Despite advancements in this sector, a notable gap remains in integrating physics-informed (PI) approaches with transformer-based architectures. This study proposes a novel hybrid WPF model that integrates the Temporal Fusion Transformer (TFT) with theoretical power curve modelling techniques.

The Greek Day-Ahead Electricity Market facilitates a diverse array of order types and offers participants a plethora of choices. While Block Orders are theoretically supported, their accessibility is confined to a subset of market participants so as to ensure the Market Clearing Price formation and the technical feasibility of the resulting Market Schedules. This paper presents a quantitative analysis of the implications of lifting the currently applicable restrictions on the availability of Block Orders in the Greek Day-Ahead Electricity Market, based on past historical data.

The transition to battery electric vehicles (BEVs) necessitates the establishment of an effective charging infrastructure, especially in countries like Greece where BEV adoption has been slow. This paper presents a comprehensive multi-criteria decision analysis (MCDA) framework, utilizing the PROMETHEE II method, to facilitate the optimal siting of electric vehicle charging stations (EVCSs) in Greek municipalities.

Renewable energy valleys (REVs) represent a transformative concept poised to reshape global energy landscapes. These comprehensive ecosystems transition regions from conventional energy sources to sustainable, self-reliant hubs for renewable energy generation, distribution, and consumption. At their core, REVs integrate advanced information and communication technology (ICT), interoperable digital solutions, social innovation processes, and economically viable business models. Crete utilizes various energy sources to become energy-independent, lower carbon emissions, and enhance system resilience.

Reverse power flow, defined as the continuous flow of electricity in a direction opposite to the normal direction of the power flow in a grid, typically occurs in microgrids when the energy generated by the distributed electric power plants exceeds the local load demand. The framework builds upon deep learning models that forecast the electricity produced (photovoltaic systems) and consumed by the microgrid and an optimization algorithm that schedules its shiftable loads (electric vehicles) based on said forecasts.