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

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Slow Convergence of Interacting Kalman Filters in Word-of-Mouth Social Learning 60

Slow Convergence of Interacting Kalman Filters in Word-of-Mo...

引用

60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Krishnamurthy, Vikram Rojas, Cristian R. School of Electrical and Computer Engineering Cornell University United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden

ISBN: (纸本)9798331541033

We consider word-of-mouth social learning involving m Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measurement of the conditional mean of the previous Kalman filter. The prior is updated by the m-th Kalman filter. When m=2, and the observations are noisy measurements of a Gaussian random variable, the covariance goes to zero as k-1/3 for 7kappa;observations, instead of O(k-1) in the standard Kalman filter. In this paper we prove that for m agents, the covariance decreases to zero as k-(2m-1), i.e, the learning slows down exponentially with the number of agents. We also show that by artificially weighing the prior at each time, the learning rate can be made optimal as k-1. The implication is that in word-of-mouth social learning, artificially re-weighing the prior can yield the optimal learning rate. © 2024 IEEE.

关键词： Kalman filters

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An Integrated AI-Based Information System for Energy Forecasting 14

An Integrated AI-Based Information System for Energy Forecas...

引用

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

作者： Skaloumpakas, Panagiotis Mylona, Zoi Strompolas, Sofoklis Sarmas, Elissaios Doukas, Haris Holistic Ike Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350318067

The aim of reducing carbon emissions through the digitization of the energy sector involves several challenges. One potential solution is the integration of renewable energy sources;however, the volatile and intermittent nature of this type of energy requires precise forecasting. This paper presents a novel, intelligent information system that covers the entire data lifecycle in the energy sector, including data acquisition, processing and storage, model development and training, and a user-friendly web application. The system was tested in three real-life use cases and demonstrated effectiveness and versatility in addressing the intricate data challenges faced by the energy industry. The paper concludes with suggestions for further research in this area. © 2023 IEEE.

关键词： Forecasting

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Energy Sector Digitilisation: A Security Framework Application for Role-Based Access Management 14

Energy Sector Digitilisation: A Security Framework Applicati...

引用

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

作者： Kormpakis, Georgios Kapsalis, Panagiotis Alexakis, Konstantinos Mylona, Zoi Pelekis, Sotiris Marinakis, Vangelis School of Electrical and Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece Holistic SA Athens Greece

ISBN: (纸本)9798350318067

Data security and privacy in the energy sector and especially in buildings' energy management, is an emerging issue which cannot be underestimated, thus, calls for solid and robust solutions. The reliable functioning of energy management data platforms is essential for the efficient operation and performance of buildings, as well as the well-being of occupants. The fast-paced digital advances have aided this direction, but they have also created increased challenges for operation disruption mitigation and immutable services and outcomes. Data obtained from energy management systems frequently comprises highly sensitive and personal information. Consequently, access to such data, whether for reading purposes or for conducting experiments and research, must be strictly restricted and clearly defined. Through security frameworks data protection and information privacy, mitigation of risks and vulnerabilities can be ensured. In this paper, an end-to-end Security Framework is proposed for securing sensitive resources in energy management systems using Keycloak, OAuth2 and UMA 2 protocols as well as an extra security layer on top of the Keycloak, so as to enhance the overall security and avoid immediate interaction with Keycloak, which is the main user storage and access management component. The main concept of this framework is the controlled access of sensitive data and services, such as datasets, databases, functionalities, URLs, APIs, etc., leveraging the OAuth2 access tokens capabilities and the roles of the users. Our proposed architecture ensures that all the functionalities of the involved tools and protocols are properly secured end-to-end. The several existing functionalities of a number of tools and related protocols are properly leveraged and further extended, resulting to a fine-grained architecture with an extra security layer that offers administrative, security and identity access management and orchestration. © 2023 IEEE.

关键词： Authentication

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Circumstance Evaluation Using Extreme Value Analysis on Charging Station Data: The Case of DEI Blue in Greece 14th

Circumstance Evaluation Using Extreme Value Analysis on Cha...

引用

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

作者： Panagoulias, Dimitrios P. Sarmas, Elissaios Marinakis, Vangelis Tsihrintzis, George A. Department of Informatics University of Piraeus Piraeus18534 Greece Decision Support Systems Lab School of Electrical and Computer Engineering National Technical University of Athens Zografou-Athens15773 Greece

ISBN: (纸本)9783031674259

We apply Extreme Value Analysis on sets of Electric Vehicle (EV) charging data with the goal to extract indications in the data of future trends in the demand for EV charging. The datasets have been provided by DEI blue, a service designed to provide integrated e-mobility solutions to individuals, companies and public administration throughout Greece. Eventhough the data are limited as they have been collected during a period of only twelve months, our analysis still reveals trends in the data highlighting a probable seasonality. The seasonality is observed during periods of extreme heat, regional population mobility and incoming visitors. These occurrences impact technical aspects of battery efficiency, grid capacity and a shift of behavioural usage patterns. Our findings cannot be ignored by policy makers as they are expected to intensify in the years to come due to the fast-rising number of EVs in use. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electrification

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent systems under STL Specifications 63

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Ag...

引用

63rd IEEE Conference on decision and Control, CDC 2024

作者： Engelaar, M.H.W. Zhang, Z. Vlahakis, E.E. Dimarogonas, D.V. Lazar, M. Haesaert, S. Netherlands Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden

ISBN: (纸本)9798350316339

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024 IEEE.

关键词： Stochastic systems

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Constructive Function Approximation with Local Models 32

Constructive Function Approximation with Local Models

引用

32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Mavridis, Christos N. Johansson, Karl Henrik School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden

ISBN: (纸本)9798350395440

We introduce a constructive function approximation approach as a general tool, particularly useful in adaptive and data-driven methods for perception and control. The key idea is to estimate of a collection of simple local models as opposed to a single and complex regression model trained in the entire input space. We use principles from the Online Deterministic Annealing (ODA) optimization framework to construct an adaptive partition of the input space, which enables the introduction of local function approximation models within each subset of the partition. We show that both the partitioning and the local model training algorithms are stochastic approximation algorithms that operate online, and with the same observations, as part of a two-timescale stochastic approximation scheme. This process constitutes a heuristic method to gradually increase the complexity of the function approximation framework in a task-agnostic manner, giving emphasis to regions of the input space where the regression error is high. As a result this framework has inherent explainability properties, and is suitable for continuous learning applications where regression improvement without retraining from scratch is crucial. Simulation results illustrate the properties of the proposed approach. © 2024 IEEE.

关键词： Heuristic methods

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BD4NRG QUERY ENGINE – INTUITIVE, EFFICIENT AND FEDERATED QUERYING ON BIG DATA 21

BD4NRG QUERY ENGINE – INTUITIVE, EFFICIENT AND FEDERATED QU...

引用

21st International Conference on e-Society, ES 2023 and 19th International Conference on Mobile Learning, ML 2023

作者： Touloumis, Konstantinos Karakolis, Evangelos Kapsalis, Panagiotis Marinakis, Vangelis Psarras, John Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens 9 Iroon Polytechniou Str. Athens15773 Greece

ISBN: (纸本)9789898704474

Due to the rapid development of Internet of Things (IoT) technology during the last decade, there has been recorded a significant growth in the size of data collected by data warehouses, especially on the ones connected to sensors and meters on the energy sector. Querying such data, gathered by heterogeneous data sources is a complicated task in terms of space allocation, scalability, and integration. Big data analytics methods try to address the problem of querying big data by bringing together different data sources of different formats with a high level of abstraction. This paper presents a query engine that, on the one hand addresses the problem of querying big data stored on different and heterogeneous databases and on the other hand, simplifies the steps needed by the user for defining and executing queries on the aforementioned databases through an intuitive and easy to use interface, so as to facilitate high level analytical services. Particular attention is given to securing the tool by employing access resource management thus preventing unauthorized users from accessing the requested resources. © 2023 Proceedings of the International Conferences on e-Society 2023, ES 2023 and Mobile Learning 2023, ML 2023. All rights reserved.

关键词： Data Analytics

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A DATA ACQUISITION FRAMEWORK FOR BUILDING ENERGY MANAGEMENT 21

A DATA ACQUISITION FRAMEWORK FOR BUILDING ENERGY MANAGEMENT

引用

21st International Conference on e-Society, ES 2023 and 19th International Conference on Mobile Learning, ML 2023

作者： Touloumis, Konstantinos Kapsalis, Panagiotis Sarmas, Elissaios Stamatopoulos, Stathis Marinakis, Vangelis Doukas, Haris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens 9 Iroon Polytechniou Str. Athens15773 Greece

ISBN: (纸本)9789898704474

Collecting data for providing energy analytics services has closely been dependent on Building Management systems (BMSs). A BMS connected to a set of sensors and other electronic devices aims at observing a collection of rooms in terms of temperature, humidity, and other energy related metrics with the goal of maximining user comfort while preserving energy consumption. The tasks of collecting, preprocessing, storing and querying data on a BMS faces the same challenges with those of querying big data in terms of scalability, data heterogeneity, and integration. This paper presents a framework that addresses the problem of collecting sensor data in one enriched data warehouse which follows a common ontology model. Data from various data sources is homogenized by appropriate data preprocessing and feature extraction techniques. The framework allows timeseries-oriented querying with the target outcome of providing stakeholders high detailed analytics services for decision making on energy consumption optimization, building renovation and financing. © 2023 Proceedings of the International Conferences on e-Society 2023, ES 2023 and Mobile Learning 2023, ML 2023. All rights reserved.

关键词： Big data

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An Advanced Visualisation Engine with Role-Based Access Control for Building Energy Visual Analytics 13

An Advanced Visualisation Engine with Role-Based Access Cont...

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13th International Conference on Information, Intelligence, systems and Applications, IISA 2022

作者： Kormpakis, Georgios Kapsalis, Panagiotis Alexakis, Konstantinos Pelekis, Sotiris Karakolis, Evangelos Doukas, Haris National Technical University of Athens Decision Support Systems Laboratory School of Electrical and Computer Engineering Athens Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

One of the main challenges of today's societies is the avoidance of the climate change since the climate crisis in now more evident than ever. Buildings have a large share of total energy consumption and, thus, it is obvious that actions should be taken to reduce their needs. Taking into consideration that nowadays data related to building's metrics are available in significantly higher rate than in the past, due to the advance of the related technologies, it is necessary to find ways to exploit them in order to draw useful inferences regarding their consumptions and how they can be reduced. For that reason, in this paper we present a Visualisation Engine, which offers a variety of visualisations over stored data. With the usage of the proposed Visualisation Engine, we envision to be able to conduct sufficient research over the data, to generate insightful information regarding their behaviour, and to assist the development of useful solutions towards the direction of more energy efficient buildings. © 2022 IEEE.

关键词： Energy efficiency

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A Reasoning Engine Architecture for Building Energy Metadata Management 13

A Reasoning Engine Architecture for Building Energy Metadata...

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13th International Conference on Information, Intelligence, systems and Applications, IISA 2022

作者： Kapsalis, Panagiotis Kormpakis, Giorgos Alexakis, Konstantinos Karakolis, Evangelos Mouzakitis, Spiros Askounis, Dimitris National Technical University of Athens Decision Support Systems Laboratory School of Electrical and Computer Engineering Athens Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

During the Buildings' lifecycle, massive amounts of data, that contain information related to their energy consumption, are generated. Towards the creation of smart building networks, this produced information must be intercepted and harmonized according to building ontologies and schemas. The pattern recognition from building metadata is based on inferencing and intelligent querying, that can be achieved with the utilization of graph and property databases that deploy and host building information. This paper presents a Reasoning Engine Architecture implemented in the context of the H2020 project called MATRYCS that persists building semantic information. It will be leveraged to support real life applications by improving the inference operations. © 2022 IEEE.

关键词： Energy efficiency

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