A5058865022- Environmental Impact and Sustainability
- Energy Efficiency and Management
- Climate Change Policy and Economics
- Multi-Criteria Decision Making
- Global Energy Security and Policy
- Integrated Energy Systems Optimization
- Building Energy and Comfort Optimization
- Global Energy and Sustainability Research
- Smart Grid Energy Management
- Energy Load and Power Forecasting
- Radiomics and Machine Learning in Medical Imaging
- Energy and Environment Impacts
- Social Acceptance of Renewable Energy
- Energy, Environment, and Transportation Policies
- Sustainability and Climate Change Governance
- Cognitive Science and Mapping
- Hybrid Renewable Energy Systems
- Sustainable Supply Chain Management
- Energy, Environment, Economic Growth
- Sustainable Building Design and Assessment
- Solar Radiation and Photovoltaics
- Risk and Portfolio Optimization
- Renewable energy and sustainable power systems
- Process Optimization and Integration
- Optimization and Mathematical Programming
National Technical University of Athens
2015-2024
University of Piraeus
2024
École Supérieure des Sciences Commerciales d’Angers
2020
Universidad Politécnica de Madrid
2020
Decision Systems (United States)
2020
The energy sector is closely interconnected with the building and integrated Information Communication Technologies (ICT) solutions for effective management supporting decision-making at building, district city level are key fundamental elements making a Smart. available systems designed intended exclusively predefined number of cases without allowing expansion interoperability other applications that partially due to lack semantics. This paper presents an advanced Internet Things (IoT)...
Accurately forecasting solar plants production is critical for balancing supply and demand scheduling distribution networks operation in the context of inclusive smart cities energy communities. However, problem becomes more demanding, when there insufficient amount data to adequately train models, due being recently installed or because lack smart-meters. Transfer learning (TL) offers capability transferring knowledge from source domain different target domains resolve related problems....
Short-term photovoltaic (PV) power forecasting is essential for integrating renewable energy sources into the grid as it provides accurate and timely information on expected output of PV systems. Deep learning (DL) networks have shown promising results in this area, but depending weather conditions particularities each system, different DL architectures may perform best. This paper proposes a meta-learning method to improve one-hour-ahead deterministic forecasts systems by dynamically...
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). Nevertheless, one most important challenges this endeavor lies identifying suitable consumer clusters with similar behaviors. In paper, we present a novel machine learning based framework order achieve optimal load profiling through real case study, utilizing almost 5000 households London. Four widely used...
Abstract Climate action to achieve the Paris Agreement should respect United Nations Sustainable Development Goals. Here, we use an integrated assessment modelling framework comprising nine climate policy models and quantify impacts of decarbonisation pathways on Goals in European Union at regional national levels. We show that scenario-consistent assumptions future socio-economic trends current policies would improve energy- carbon-related aspects sustainability reduce inequalities....
Climate change is considered among the most critical risks that global society faces in this century. So far, climate policy strategies have been evaluated by means of a variety climate-economy models, or Integrated Assessment Models (IAMs), aim supporting climate-related decision making. However, their inherent complexity, number and nature driving assumptions, usual exclusion stakeholders from modelling process raise issue extent to which they can provide fruitful insights for makers....
In Greece, the renewable energy potential and a low-quality building stock constitute background of possible low-carbon transition. This transition, however, faces significant uncertainties, ranging from long-term effects ongoing economic recession technological lock-ins, to stability regulatory framework issues public acceptance. Such uncertainties may eventually give rise barriers to, as well severe social consequences of, envisaged Here, in structured approach eliciting knowledge embedded...
Europe's capacity to explore the envisaged pathways that achieve its near- and long-term energy climate objectives needs be significantly enhanced. In this perspective, we discuss how is supported by climate-economy models, international modelling teams are organised within structured communication channels consortia as well coordinate multi-model analyses provide robust scientific evidence. Noting lack of such a dedicated channel for highly active yet currently fragmented European...
This paper investigates an optimal sizing strategy for islanded building microgrid. The microgrid composites a rooftop Photovoltaic (PV) system, Battery Energy Storage System (BESS), ice-Thermal (ice-TESS), and loads. loads are divided into two sets based on their ability to participate in demand response: i) Plugged Loads (PL) such as lights, ii) Cooling (CL) air-conditioners. is must be supplied with local generation resources. Therefore, the BESS deployed offset PV output's variability,...
This study introduces an energy management method that smooths electricity consumption and shaves peaks by scheduling the operating hours of water pumping stations in a smart fashion. Machine learning models are first used to accurately forecast consumed produced renewable sources on hourly level. Then, forecasts exploited algorithm optimally allocates pumps with objective minimize predicted peaks. Constraints related operation also considered. The performance proposed is evaluated...
Energy efficiency is critical for meeting global energy and climate targets, requiring however significant investments. Due to the lack of mature decision-support systems utilization traditional investment mechanisms that focus on economical aspects projects neglect their environmental impact, such can experience difficulties in being funded. In interim, impact digitization era more apparent than ever, as algorithms data availability quality have significantly improved. This study aspires...
Reducing greenhouse gas emissions and energy cost in the building sector largely relies on effective management. Yet, when it comes to heating or cooling, savings may translate uncomfortable conditions for users. To ensure thermal comfort with a minimal consumption, this paper we propose modular methodology that dynamically schedules operating hours of heating/cooling system using forecasts. decide time mode operation, our approach utilizes indoor air temperature relative humidity...