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检索条件"机构=School of Electrical and Computer Engineering Decision Support Systems"

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A Multi-Criteria decision support Tool for the Evaluation of Energy Storage systems: A Case Study in Battery Technology 15

A Multi-Criteria Decision Support Tool for the Evaluation of...

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Trachanas, Georgios P. Arsenopoulos, Apostolos Tsalidis, Theodoros Serepas, Filippos Markaki, Ourania Askounis, Dimitrios School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Greece

ISBN: (纸本)9798350368833

The increasing integration of renewable energy sources necessitates the deployment of efficient energy storage systems to ensure grid resilience, stability, and efficient operation. Selecting the optimal energy storage technology involves the evaluation across multiple criteria, reflecting technical, economic, and environmental dimensions, among others. This paper presents a decision support tool, based on an ensemble of Multi-Criteria decision-Making methods, to rank energy storage technologies. These methods are renowned for their ability to handle conflicting criteria and provide clear, actionable insights for decision-makers. A detailed case study concerning battery systems is also included, evaluating the alternatives against seven technology and economic type criteria. The results demonstrate the efficacy of the employed tool in identifying the most suitable energy storage technologies, providing valuable guidance for stakeholders in the selection and development of energy storage solutions. © 2024 IEEE.

关键词： Battery storage

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Future Heat Risk Index: The Case of Attica Regions, Greece 15

Future Heat Risk Index: The Case of Attica Regions, Greece

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Dimitriou, Iason C. Trachanas, Georgios P. Arsenopoulos, Apostolos Sarmas, Elissaios School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Greece

ISBN: (纸本)9798350368833

Extreme heat has become a common phenomenon especially the summer months. Dense urban environments suffer greatly from extreme heat events, which makes the everyday life of citizens difficult. For the more vulnerable population, such as the elderly, extreme heat waves can become dangerous for their health and even lethal. The study employs a Future Heat Risk Index to evaluate potential heat-related vulnerabilities under different climate change scenarios, particularly Representative Concentration Pathway 8.5 for the year 2040. Through the integration of climate change projections and spatial analysis, the research identifies areas at high risk of heat-related impacts and prioritizes mitigation strategies. Key findings underscore the importance of proactive planning and adaptation measures in enhancing resilience to extreme heat events, particularly in Mediterranean cities like those in Attica, Greece. The proposed mitigation strategies include implementation of vegetation enhancement and cold materials installation. The effects of both strategies are compared and discussed, highlighting the importance of incorporating climate change scenarios into decision-making processes to safeguard public health and well-being in a warming climate. © 2024 IEEE.

关键词： Geographic Information systems Heat Risk IPCC RCP Remote sensing Urban Planning

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A decision support Tool for Aggregation and Prioritisation of Energy Efficiency Investments in Buildings 15

A Decision Support Tool for Aggregation and Prioritisation o...

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Andreoulaki, Ioanna Stoian, Daniela Papapostolou, Aikaterini Stavrakaki, Andriana Marinakis, Vangelis School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350368833

Reducing buildings' energy consumption through energy efficiency interventions has become a key priority to mitigate climate change. To this end, stakeholders involved in the implementation and financing of energy efficiency projects in the building sector could benefit from the development of specific methodologies and user-friendly tools that can assist them by supporting them in managing large datasets and by simplifying decision making processes. In this context, the aim of this paper is to propose a decision-making tool for triggering investments in energy efficiency. The tool's workflow consists of three layers: project selection (layer 1) - narrowing down the list of potential energy efficiency projects according to investor preferences, aggregation (layer 2) - leveraging an optimisation algorithm to create clusters of similar projects, and finally, prioritisation (layer 3) - applying the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method to rank the clusters. The tool brings significant added value to stakeholders involved in financing energy efficiency interventions, offering standardisation and automation of procedures. © 2024 IEEE.

关键词： Decentralized finance

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Aggregated Flexibility Dynamic Forecasting of Residential Energy Prosumers for DR Programs 15

Aggregated Flexibility Dynamic Forecasting of Residential En...

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Papias, Ioannis Michalakopoulos, Vasilis Sarmas, Elissaios Marinakis, Vangelis School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350368833

In today's dynamic energy landscape, Demand Response (DR) programs have become essential for ensuring grid stability and operational efficiency. These programs empower prosumers to adjust their electricity consumption collectively or voluntarily, often against a predefined baseline load. Accurate estimation of this flexibility is pivotal for the success of DR programs, particularly in scenarios where only aggregated building data is available. This study highlights the significance of constructing a building's flexibility baseline load, which approximates its electricity consumption and production in the absence of specific DR events. The deviation of energy consumption and production from this baseline, by predetermined margins, signifies potential flexibility. However, accurately forecasting next-day load variations is essential for evaluating flexibility spread around the baseline. In addressing this need, we employ Long Short-Term Memory (LSTM) networks, optimizing their parameters to significantly enhance performance in predicting energy consumption and production for all weekdays. To validate our proposed solution, we conduct real-world experiments using prosumer data collected from a building in Austria. © 2024 IEEE.

关键词： Demand response

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Spotlighting the Significance of the SRI Methodological Tailoring at Country Level: A Case Study in Greece 15

Spotlighting the Significance of the SRI Methodological Tail...

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Arsenopoulos, Apostolos Stamatopoulos, Efstathios Samaras, Panagiotis Xexakis, Georgios Psarras, John National Technical University of Athens Decision Support Systems Laboratory School of Electrical and Computer Engineering Greece Holistic S.A. Greece

ISBN: (纸本)9798350368833

In addressing the challenges posed by climate change, energy poverty, and energy crises, there is an increasing focus on transitioning to green and fossil fuel-free alternatives, reflecting a strong commitment to sustainable development. The Smart Readiness Indicator, introduced by the European Union, aims to improve the energy efficiency of buildings, adjust operations to meet occupants' needs, and respond to signals from the electricity grid. Consequently, developing new methods for decarbonising the energy system has become essential to meet the energy demands of industries, businesses, transportation, and residential sectors. This necessitates a comprehensive understanding of the situation and the implementation of various actions to reform the regulatory framework, promote technological advancements, stimulate economic growth, and foster collective social efforts. This study focuses on customising the Smart Readiness Indicator methodological framework by adjusting the weighting factors of different domains based on the 'energy balance' method. Currently, the European Commission provides standard domain weighting values, however this study is focused on their tailoring in order to reflect specific climate zones. Utilising open data and consumption patterns, the customised weightings are applied to a typical Greek single-family house to demonstrate the differences and underscore the importance of tailored assessments for extracting more accurate results compared to the 'standard' methodology. The findings highlight significant improvements, especially in energy efficiency and occupant comfort functionalities, suggesting that tailored approaches can enhance the accuracy of SRI assessments. © 2024 IEEE.

关键词： Clean energy

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A Next Generation Library of AI-Based Data-Driven Services for the Built Environment 14

A Next Generation Library of AI-Based Data-Driven Services f...

引用

14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Sarmas, Elissaios Stamatopoulos, Stathis Kapsalis, Panagiotis Touloumis, Konstantinos Marinakis, Vangelis School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350318067

Nowadays, buildings account for almost 40% of the overall energy consumption. Thus, it is essential to involve a broad range of stakeholders throughout the building lifecycle, with the development of innovative, cross-cutting energy services, which exploit the ever-increasing volume of available data. In this paper we present a novel library of AI-based data-driven services for the built environment with the aim of addressing decarbonization challenges and of facilitating datadriven decision making. The issue of handling energy-related data in an efficient way is tackled, incorporating them in user-friendly, micro-services oriented technological components. The architecture of this AI-base library is thoroughly presented covering learning, optimization and simulation tasks. showcase our approach to energy resource management. © 2023 IEEE.

关键词： Intelligent systems

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An Integrated ML-Ops Framework for Automating AI-based Photovoltaic Forecasting

An Integrated ML-Ops Framework for Automating AI-based Photo...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Chorozoglou, Symeon Sarmas, Elissaios Marinakis, Vangelis National Technical University of Athens Decision Support Systems Laboratory School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350324457

Energy digitization holds significant importance for various energy applications, encompassing aspects like production, consumption, and distribution within power grids. The digital transformation of energy plays a pivotal role in enhancing the integration of Artificial Intelligence (AI) into energy management systems, leveraging extensive datasets. The development of AI systems and the utilization of Machine Learning (ML) techniques empower users with precise predictions related to renewable energy production, thereby expediting the shift towards clean energy. Nevertheless, the effective use of data demands a high level of expertise, thereby excluding energy stakeholders from the benefits modern technologies offer. In this paper, we introduce an AI forecasting system designed to bridge the knowledge gap in data processing methods and ML models for energy stakeholders. This system focuses on delivering a user-friendly interface for photovoltaic (PV) production forecasting by automating the entire ML operations pipeline. Consequently, users can obtain data-driven model results without the need to manually code all the requisite steps for model training and fine-tuning. To demonstrate the system's capabilities, we provide an experimental application using real PV data from a Portuguese aggregator. © 2023 IEEE.

关键词： Artificial Intelligence Forecasting Machine Learning Photovoltaics Renewable Energy Sources

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Comparison of Machine Learning Algorithms For Predicting CO2Emissions in the maritime domain 14

Comparison of Machine Learning Algorithms For Predicting CO2...

引用

14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Michalakopoulos, Vasilis Ilias, Loukas Kapsalis, Panagiotis Mouzakitis, Spiros Askounis, Dimitris School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350318067

The maritime industry is a significant source of global carbon dioxide (CO2) emissions, and the accurate prediction of emissions in this domain is of paramount importance. In this paper, we conduct a comparative analysis of machine learning algorithms for predicting CO2 emissions in the maritime domain. Using a unique dataset, comprising historical maritime data for different vessel voyages, including vessel type, voyage information and environmental factors, we employ decision trees, a number of regression, and artificial neural network algorithms. Performance evaluation is conducted using established metrics such as R2 (%), Mean Absolute Error, Normalized Root Mean Squared Error and Symmetric Mean Biased Error. Our findings reveal that the Extra Trees Regressor and Multi-layer Perceptron regressor algorithms outperform the other methods in terms of prediction accuracy demonstrating the least amount of error. Our research contributes to the existing literature by highlighting the potential of machine learning in predicting maritime CO2 emissions and provides insights for further research, such as exploring alternative algorithms and incorporating real-time data Integration. © 2023 IEEE.

关键词： Carbon dioxide

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A Comprehensive Approach to Real-Time and Batch Processing for Energy-Efficient IoT Homes: Leveraging Lambda Architecture and Data Lakes 15

A Comprehensive Approach to Real-Time and Batch Processing f...

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Serepas, Filippos Papias, Ioannis Bellos, Nikolaos Marinakis, Vangelis Holistic Ike Athens Greece School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350368833

The incorporation of IoT technology into energy-efficient home systems has resulted in a surge in data volume, prompting the need for sophisticated storage and processing solutions. This paper proposes a system that integrates the Lambda Architecture with data lakes to address real-time and batch processing needs in the context of energy-efficient homes. By leveraging technologies such as TimescaleDB for short-term storage and Apache Hudi for long-term storage, coupled with Kafka for data streaming, the system ensures efficient data management and analysis. Real-time insights are provided through GraphQL-powered visualizations, while batch processing facilitates advanced analytics and machine learning model training. The proposed system addresses the dual demands of end-users seeking real-time insights and data scientists requiring extensive datasets for analysis. © 2024 IEEE.

关键词： Smart homes

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Distributed Ledger Technology in Energy Services: The InEExS Project Objectives and Approach 14

Distributed Ledger Technology in Energy Services: The InEExS...

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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Papapostolou, Aikaterini Andreoulaki, Ioanna Divolis, Sokratis Anagnostopoulos, Filippos Marinakis, Vangelis School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece Amsterdam Netherlands

ISBN: (纸本)9798350318067

The digitalization trend is prominent in a wide variety of sectors, and the energy sector is no exception. The incorporation of blockchain, a Distributed Ledger Technology (DLT), in energy services has been examined in literature and quite a few endeavors of DLT adoption in energy applications have been implemented by companies, universities and other organizations. The European LIFE project 'InEExS' pursues to apply DLT through specific business cases, so as to offer improved digital energy services and encourage energy efficiency. In this paper, a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis has been conducted as a first stage of DLT potential usages evaluation in the contexts of the InEExS, with the purpose of demonstrating the reasons why blockchain could support the digitalization of energy applications and acceleration of energy transition, while pointing out the most important barriers that should be addressed to ensure that DLT integration would be truly beneficial. The decentralized nature of blockchain, combined with the transparency and safety it provides, make it a very promising technology for energy management and trading implementations, among others. However, technical constraints, such as the scalability problem, security threats, as well as sociopolitical and regulatory barriers should not be neglected. The findings of our SWOT analysis are combined with an assessment of prospective blockchain usages in the business cases deployed by the InEExS, so that the best practices to optimally exploit DLT in various energy applications, within and beyond the project, are identified. © 2023 IEEE.

关键词： Smart contract

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