Anthonios C. Chrysopoulos

Post-Doctoral Researcher
Electrical & Computer Engineering Department
Aristotle University of Thessaloniki, GR 54124, Thessaloniki, Greece

Tel: +30 2310 99 6349
Fax: +30 2310 99 6398
Email: achryso (at) issel [dot] ee [dot] auth [dot] gr

LinkedIn

Research Interests

  • User Profiling and Strategies on Energy Stock Market by means of Data Mining and Agent Technology
  • Energy Consumers Modelling and Research on Demand – Response Programs through Data Mining and Agent Technology
  • Engineering trading agents

Education

2009-2003 Diploma of Electrical and Computer Engineering
Electrical and Computer Engineering Department
Aristotle University of Thessaloniki, Greece
Diploma Thesis: “Implementation of A Multi-Agent Platform For Decision Making in Energy Stock Markets”

Professional Experience

10/2003 – 10/2007 Technical Support
Computer Life Α.Ε.
Thessaloniki, Greece
10/2009 – 12/2011 Technical Support
“Alexandros Baltatzis” European Research Project
Aristotle University of Thessaloniki, Greece
11/2011 – today Developer and Analyst
Cassandra FP7 Project
Aristotle University of Thessaloniki, Greece

Teaching Experience

10/2010 – today Teaching Assistant for Data Structures
Electrical and Computer Engineering Department
Aristotle University of Thessaloniki, Greece
10/2010 – today Teaching Assistant for Software Engineering
Electrical and Computer Engineering Department
Aristotle University of Thessaloniki, Greece

Memberships

  • Member of the Technical Chamber of Greece
  • IEEE & Computer Society Student Member

Publications

2017

Inproceedings Papers

Maria Th. Kotouza, Antonios C. Chrysopoulos and Pericles A. Mitkas
"Segmentation of Low Voltage Consumers for Designing Individualized Pricing Policies"
European Energy Market (EEM), 2017 14th International Conference, pp. 1-6, IEEE, Dresden, Germany, 2017 Jun

In recent years, the Smart Grid paradigm has opened a vast set of opportunities for all participating parties in the Energy Markets (i.e. producers, Distribution and Transmission System Operators, retailers, consumers), providing two-way data communication, increased security and grid stability. Furthermore, the liberation of distribution and energy services has led towards competitive Energy Market environments [4]. In order to maintain their existing customers\' satisfaction level high, as well as reaching out to new ones, suppliers must provide better and more reliable energy services, that are specifically tailored to each customer or to a group of customers with similar needs. Thus, it is necessary to identify segments of customers that have common energy characteristics via a process called Consumer Load Profiling (CLP) [16].

@inproceedings{2017Kotouza,
author={Maria Th. Kotouza and Antonios C. Chrysopoulos and Pericles A. Mitkas},
title={Segmentation of Low Voltage Consumers for Designing Individualized Pricing Policies},
booktitle={European Energy Market (EEM), 2017 14th International Conference},
pages={1-6},
publisher={IEEE},
address={Dresden, Germany},
year={2017},
month={06},
date={2017-06-06},
doi={https://doi.org/10.1109/EEM.2017.7981862},
issn={2165-4093},
isbn={978-1-5090-5499-2},
abstract={In recent years, the Smart Grid paradigm has opened a vast set of opportunities for all participating parties in the Energy Markets (i.e. producers, Distribution and Transmission System Operators, retailers, consumers), providing two-way data communication, increased security and grid stability. Furthermore, the liberation of distribution and energy services has led towards competitive Energy Market environments [4]. In order to maintain their existing customers\\' satisfaction level high, as well as reaching out to new ones, suppliers must provide better and more reliable energy services, that are specifically tailored to each customer or to a group of customers with similar needs. Thus, it is necessary to identify segments of customers that have common energy characteristics via a process called Consumer Load Profiling (CLP) [16].}
}

2016

Journal Articles

Antonios Chrysopoulos, Christos Diou, Andreas Symeonidis and Pericles A. Mitkas
"Response modeling of small-scale energy consumers for effective demand response applications"
Electric Power Systems Research, 132, pp. 78-93, 2016 Mar

The Smart Grid paradigm can be economically and socially sustainable by engaging potential consumers through understanding, trust and clear tangible benefits. Interested consumers may assume a more active role in the energy market by claiming new energy products/services on offer and changing their consumption behavior. To this end, suppliers, aggregators and Distribution System Operators can provide monetary incentives for customer behavioral change through demand response programs, which are variable pricing schemes aiming at consumption shifting and/or reduction. However, forecasting the effect of such programs on power demand requires accurate models that can efficiently describe and predict changes in consumer activities as a response to pricing alterations. Current work proposes such a detailed bottom-up response modeling methodology, as a first step towards understanding and formulating consumer response. We build upon previous work on small-scale consumer activity modeling and provide a novel approach for describing and predicting consumer response at the level of individual activities. The proposed models are used to predict shifting of demand as a result of modified pricing policies and they incorporate consumer preferences and comfort through sensitivity factors. Experiments indicate the effectiveness of the proposed method on real-life data collected from two different pilot sites: 32 apartments of a multi-residential building in Sweden, as well as 11 shops in a large commercial center in Italy.

@article{2015ChrysopoulosEPSR,
author={Antonios Chrysopoulos and Christos Diou and Andreas Symeonidis and Pericles A. Mitkas},
title={Response modeling of small-scale energy consumers for effective demand response applications},
journal={Electric Power Systems Research},
volume={132},
pages={78-93},
year={2016},
month={03},
date={2016-03-01},
url={http://issel.ee.auth.gr/wp-content/uploads/2017/01/Response-modeling-of-small-scale-energy-consumers-for-effective-demand-response-applications.pdf},
abstract={The Smart Grid paradigm can be economically and socially sustainable by engaging potential consumers through understanding, trust and clear tangible benefits. Interested consumers may assume a more active role in the energy market by claiming new energy products/services on offer and changing their consumption behavior. To this end, suppliers, aggregators and Distribution System Operators can provide monetary incentives for customer behavioral change through demand response programs, which are variable pricing schemes aiming at consumption shifting and/or reduction. However, forecasting the effect of such programs on power demand requires accurate models that can efficiently describe and predict changes in consumer activities as a response to pricing alterations. Current work proposes such a detailed bottom-up response modeling methodology, as a first step towards understanding and formulating consumer response. We build upon previous work on small-scale consumer activity modeling and provide a novel approach for describing and predicting consumer response at the level of individual activities. The proposed models are used to predict shifting of demand as a result of modified pricing policies and they incorporate consumer preferences and comfort through sensitivity factors. Experiments indicate the effectiveness of the proposed method on real-life data collected from two different pilot sites: 32 apartments of a multi-residential building in Sweden, as well as 11 shops in a large commercial center in Italy.}
}

2015

Conference Papers

Konstantinos Vavliakis, Anthony Chrysopoulos, Kyriakos C. Chatzidimitriou, Andreas L. Symeonidis and Pericles A. Mitkas
"CASSANDRA: a simulation-based, decision-support tool for energy market stakeholders"
SimuTools, 2015 Dec

Energy gives personal comfort to people, and is essential for the generation of commercial and societal wealth. Nevertheless, energy production and consumption place considerable pressures on the environment, such as the emission of greenhouse gases and air pollutants. They contribute to climate change, damage natural ecosystems and the man-made environment, and cause adverse e ects to human health. Lately, novel market schemes emerge, such as the formation and operation of customer coalitions aiming to improve their market power through the pursuit of common bene ts.In this paper we present CASSANDRA, an open source1,expandable software platform for modelling the demand side of power systems, focusing on small scale consumers. The structural elements of the platform are a) the electrical installations (i.e. households, commercial stores, small industries etc.), b) the respective appliances installed, and c) the electrical consumption-related activities of the people residing in the installations.CASSANDRA serves as a tool for simulation of real demandside environments providing decision support for energy market stakeholders. The ultimate goal of the CASSANDRA simulation functionality is the identi cation of good practices that lead to energy eciency, clustering electric energy consumers according to their consumption patterns, and the studying consumer change behaviour when presented with various demand response programs.

@conference{2015VavliakisSimuTools,
author={Konstantinos Vavliakis and Anthony Chrysopoulos and Kyriakos C. Chatzidimitriou and Andreas L. Symeonidis and Pericles A. Mitkas},
title={CASSANDRA: a simulation-based, decision-support tool for energy market stakeholders},
booktitle={SimuTools},
year={2015},
month={00},
date={2015-00-00},
url={http://issel.ee.auth.gr/wp-content/uploads/2016/09/CASSANDRA_SimuTools.pdf},
abstract={Energy gives personal comfort to people, and is essential for the generation of commercial and societal wealth. Nevertheless, energy production and consumption place considerable pressures on the environment, such as the emission of greenhouse gases and air pollutants. They contribute to climate change, damage natural ecosystems and the man-made environment, and cause adverse e ects to human health. Lately, novel market schemes emerge, such as the formation and operation of customer coalitions aiming to improve their market power through the pursuit of common bene ts.In this paper we present CASSANDRA, an open source1,expandable software platform for modelling the demand side of power systems, focusing on small scale consumers. The structural elements of the platform are a) the electrical installations (i.e. households, commercial stores, small industries etc.), b) the respective appliances installed, and c) the electrical consumption-related activities of the people residing in the installations.CASSANDRA serves as a tool for simulation of real demandside environments providing decision support for energy market stakeholders. The ultimate goal of the CASSANDRA simulation functionality is the identi cation of good practices that lead to energy eciency, clustering electric energy consumers according to their consumption patterns, and the studying consumer change behaviour when presented with various demand response programs.}
}

2014

Journal Articles

Antonios Chrysopoulos, Christos Diou, A.L. Symeonidis and Pericles A. Mitkas
"Bottom-up modeling of small-scale energy consumers for effective Demand Response Applications"
EAAI, 35, pp. 299- 315, 2014 Oct

In contemporary power systems, small-scale consumers account for up to 50% of a country?s total electrical energy consumption. Nevertheless, not much has been achieved towards eliminating the problems caused by their inelastic consumption habits, namely the peaks in their daily power demand and the inability of energy suppliers to perform short-term forecasting and/or long-term portfolio management. Typical approaches applied in large-scale consumers, like providing targeted incentives for behavioral change, cannot be employed in this case due to the lack of models for everyday habits, activities and consumption patterns, as well as the inability to model consumer response based on personal comfort. Current work aspires to tackle these issues; it introduces a set of small-scale consumer models that provide statistical descriptions of electrical consumption patterns, parameterized from the analysis of real-life consumption measurements. These models allow (i) bottom-up aggregation of appliance use up to the overall installation load, (ii) simulation of various energy efficiency scenarios that involve changes at appliance and/or activity level and (iii) the assessment of change in consumer habits, and therefore the power consumption, as a result of applying different pricing policies. Furthermore, an autonomous agent architecture is introduced that adopts the proposed consumer models to perform simulation and result analysis. The conducted experiments indicate that (i) the proposed approach leads to accurate prediction of small-scale consumption (in terms of energy consumption and consumption activities) and (ii) small shifts in appliance usage times are sufficient to achieve significant peak power reduction.

@article{2014chrysopoulosEAAI,
author={Antonios Chrysopoulos and Christos Diou and A.L. Symeonidis and Pericles A. Mitkas},
title={Bottom-up modeling of small-scale energy consumers for effective Demand Response Applications},
journal={EAAI},
volume={35},
pages={299- 315},
year={2014},
month={10},
date={2014-10-01},
url={http://issel.ee.auth.gr/wp-content/uploads/2017/01/Bottom-up-modeling-of-small-scale-energy-consumers-for-effective-Demand-Response-Applications.pdf},
doi={http://10.1016/j.engappai.2014.06.015},
keywords={Small-scale consumer models;Demand simulation;Demand Response Applications},
abstract={In contemporary power systems, small-scale consumers account for up to 50% of a country?s total electrical energy consumption. Nevertheless, not much has been achieved towards eliminating the problems caused by their inelastic consumption habits, namely the peaks in their daily power demand and the inability of energy suppliers to perform short-term forecasting and/or long-term portfolio management. Typical approaches applied in large-scale consumers, like providing targeted incentives for behavioral change, cannot be employed in this case due to the lack of models for everyday habits, activities and consumption patterns, as well as the inability to model consumer response based on personal comfort. Current work aspires to tackle these issues; it introduces a set of small-scale consumer models that provide statistical descriptions of electrical consumption patterns, parameterized from the analysis of real-life consumption measurements. These models allow (i) bottom-up aggregation of appliance use up to the overall installation load, (ii) simulation of various energy efficiency scenarios that involve changes at appliance and/or activity level and (iii) the assessment of change in consumer habits, and therefore the power consumption, as a result of applying different pricing policies. Furthermore, an autonomous agent architecture is introduced that adopts the proposed consumer models to perform simulation and result analysis. The conducted experiments indicate that (i) the proposed approach leads to accurate prediction of small-scale consumption (in terms of energy consumption and consumption activities) and (ii) small shifts in appliance usage times are sufficient to achieve significant peak power reduction.}
}

2013

Incollection

Themistoklis Diamantopoulos, Andreas Symeonidis and Anthonios Chrysopoulos
"Designing robust strategies for continuous trading in contemporary power markets"
Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets, pp. 30-44, Springer Berlin Heidelberg, 2013 Jan

In contemporary energy markets participants interact with each other via brokers that are responsible for the proper energy flow to and from their clients (usually in the form of long-term or short- term contracts). Power TAC is a realistic simulation of a real-life energy market, aiming towards providing a better understanding and modeling of modern energy markets, while boosting research on innovative trad- ing strategies. Power TAC models brokers as software agents, competing against each other in Double Auction environments, in order to increase their client base and market share. Current work discusses such a bro- ker agent architecture, striving to maximize his own profit. Within the context of our analysis, Double Auction markets are treated as microeco- nomic systems and, based on state-of-the-art price formation strategies, the following policies are designed: an adaptive price formation policy, a policy for forecasting energy consumption that employs Time Series Analysis primitives, and two shout update policies, a rule-based policy that acts rather hastily, and one based on Fuzzy Logic. The results are quite encouraging and will certainly call for future research.

@incollection{2013DiamantopoulosAMEC-DTSMEM,
author={Themistoklis Diamantopoulos and Andreas Symeonidis and Anthonios Chrysopoulos},
title={Designing robust strategies for continuous trading in contemporary power markets},
booktitle={Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets},
pages={30-44},
publisher={Springer Berlin Heidelberg},
year={2013},
month={01},
date={2013-01-01},
url={http://issel.ee.auth.gr/wp-content/uploads/2017/01/Designing-Robust-Strategies-for-Continuous-Trading-in-Contemporary-Power-Markets.pdf},
doi={http://link.springer.com/chapter/10.1007/978-3-642-40864-9_3#page-1},
keywords={aiming towards providing a better understanding and modeling of modern energy markets;competing against each other in Double Auction environments;striving to maximize his own profit. Within the context of our analysis;Double Auction markets are treated as microeconomic systems and;based on state-of-the-art price formation strategies;the following policies are designed: an adaptive price formation policy;a policy for forecasting energy consumption that employs Time Series Analysis primitives;and two shout update policies;a rule-based policy that acts rather hastily},
abstract={In contemporary energy markets participants interact with each other via brokers that are responsible for the proper energy flow to and from their clients (usually in the form of long-term or short- term contracts). Power TAC is a realistic simulation of a real-life energy market, aiming towards providing a better understanding and modeling of modern energy markets, while boosting research on innovative trad- ing strategies. Power TAC models brokers as software agents, competing against each other in Double Auction environments, in order to increase their client base and market share. Current work discusses such a bro- ker agent architecture, striving to maximize his own profit. Within the context of our analysis, Double Auction markets are treated as microeco- nomic systems and, based on state-of-the-art price formation strategies, the following policies are designed: an adaptive price formation policy, a policy for forecasting energy consumption that employs Time Series Analysis primitives, and two shout update policies, a rule-based policy that acts rather hastily, and one based on Fuzzy Logic. The results are quite encouraging and will certainly call for future research.}
}

2013

Inproceedings Papers

Antonios Chrysopoulos, Christos Diou, Andreas L. Symeonidis and Pericles Mitkas
"Agent-based small-scale energy consumer models for energy portfolio management"
Proceedings of the 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2013), pp. 45-50, Atlanta, GA, USA, 2013 Jan

Locating software bugs is a difficult task, especially if they do not lead to crashes. Current research on automating non-crashing bug detection dictates collecting function call traces and representing them as graphs, and reducing the graphs before applying a subgraph mining algorithm. A ranking of potentially buggy functions is derived using frequency statistics for each node (function) in the correct and incorrect set of traces. Although most existing techniques are effective, they do not achieve scalability. To address this issue, this paper suggests reducing the graph dataset in order to isolate the graphs that are significant in localizing bugs. To this end, we propose the use of tree edit distance algorithms to identify the traces that are closer to each other, while belonging to different sets. The scalability of two proposed algorithms, an exact and a faster approximate one, is evaluated using a dataset derived from a real-world application. Finally, although the main scope of this work lies in scalability, the results indicate that there is no compromise in effectiveness.

@inproceedings{2013ChrysopoulosIAT,
author={Antonios Chrysopoulos and Christos Diou and Andreas L. Symeonidis and Pericles Mitkas},
title={Agent-based small-scale energy consumer models for energy portfolio management},
booktitle={Proceedings of the 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2013)},
pages={45-50},
address={Atlanta, GA, USA},
year={2013},
month={01},
date={2013-01-01},
url={http://issel.ee.auth.gr/wp-content/uploads/2016/04/Agent-based-small-scale-energy-consumer-models-for-energy-portfolio-management.pdf},
keywords={Load Forecasting},
abstract={Locating software bugs is a difficult task, especially if they do not lead to crashes. Current research on automating non-crashing bug detection dictates collecting function call traces and representing them as graphs, and reducing the graphs before applying a subgraph mining algorithm. A ranking of potentially buggy functions is derived using frequency statistics for each node (function) in the correct and incorrect set of traces. Although most existing techniques are effective, they do not achieve scalability. To address this issue, this paper suggests reducing the graph dataset in order to isolate the graphs that are significant in localizing bugs. To this end, we propose the use of tree edit distance algorithms to identify the traces that are closer to each other, while belonging to different sets. The scalability of two proposed algorithms, an exact and a faster approximate one, is evaluated using a dataset derived from a real-world application. Finally, although the main scope of this work lies in scalability, the results indicate that there is no compromise in effectiveness.}
}

2012

Inproceedings Papers

Athanasios Papadopoulos, Konstantinos Toumpas, Antonios Chrysopoulos and Pericles A. Mitkas
"Exploring Optimization Strategies in Board Game Abalone for Alpha-Beta Seach"
IEEE Conference on Computational Intelligent and Games (CIG), pp. 63-70, Granada, Spain, 2012 Sep

This paper discusses the design and implementation of a highly efficient MiniMax algorithm for the game Abalone.For perfect information games with relatively low branching factor for their decision tree (such as Chess, Checkers etc.) anda highly accurate evaluation function, Alpha-Beta search proved to be far more efficient than Monte Carlo Tree Search. In recentyears many new techniques have been developed to improve the efficiency of the Alpha-Beta tree, applied to a variety of scientific fields. This paper explores several techniques for increasing the efficiency of Alpha-Beta Search on the board game of Abalone while introducing some new innovative techniques that proved to be very effective. The main idea behind them is the incorporation of probabilistic features to the otherwise deterministic Alpha-Beta search.

@inproceedings{2012PapadopoulosCIG,
author={Athanasios Papadopoulos and Konstantinos Toumpas and Antonios Chrysopoulos and Pericles A. Mitkas},
title={Exploring Optimization Strategies in Board Game Abalone for Alpha-Beta Seach},
booktitle={IEEE Conference on Computational Intelligent and Games (CIG)},
pages={63-70},
address={Granada, Spain},
year={2012},
month={09},
date={2012-09-11},
url={http://issel.ee.auth.gr/wp-content/uploads/2016/04/Exploring-Optimization-Strategies-in-Board-Game-Abalone-for-Alpha-Beta-Search.pdf},
abstract={This paper discusses the design and implementation of a highly efficient MiniMax algorithm for the game Abalone.For perfect information games with relatively low branching factor for their decision tree (such as Chess, Checkers etc.) anda highly accurate evaluation function, Alpha-Beta search proved to be far more efficient than Monte Carlo Tree Search. In recentyears many new techniques have been developed to improve the efficiency of the Alpha-Beta tree, applied to a variety of scientific fields. This paper explores several techniques for increasing the efficiency of Alpha-Beta Search on the board game of Abalone while introducing some new innovative techniques that proved to be very effective. The main idea behind them is the incorporation of probabilistic features to the otherwise deterministic Alpha-Beta search.}
}

2011

Inproceedings Papers

Kyriakos C. Chatzidimitriou, Antonios C. Chrysopoulos, Andreas L. Symeonidis and Pericles A. Mitkas
"Enhancing Agent Intelligence through Evolving Reservoir Networks for Prediction in Power Stock Markets"
Agent and Data Mining Interaction 2011 Workshop held in conjuction with the conference on Autonomous Agents and Multi-Agent Systems (AAMAS) 2011, pp. 228-247, 2011 Apr

In recent years, Time Series Prediction and clustering have been employed in hyperactive and evolving environments -where temporal data play an important role- as a result of the need for reliable methods to estimate and predict the pattern or behavior of events and systems. Power Stock Markets are such highly dynamic and competitive auction environments, additionally perplexed by constrained power laws in the various stages, from production to transmission and consumption. As with all real-time auctioning environments, the limited time available for decision making provides an ideal testbed for autonomous agents to develop bidding strategies that exploit time series prediction. Within the context of this paper, we present Cassandra, a dynamic platform that fosters the development of Data-Mining enhanced Multi-agent systems. Special attention was given on the efficiency and reusability of Cassandra, which provides Plug-n-Play capabilities, so that users may adapt their solution to the problem at hand. Cassandra’s functionality is demonstrated through a pilot case, where autonomously adaptive Recurrent Neural Networks in the form of Echo State Networks are encapsulated into Cassandra agents, in order to generate power load and settlement price prediction models in typical Day-ahead Power Markets. The system has been tested in a real-world scenario, that of the Greek Energy Stock Market.

@inproceedings{2012ChatzidimitriouAAMAS,
author={Kyriakos C. Chatzidimitriou and Antonios C. Chrysopoulos and Andreas L. Symeonidis and Pericles A. Mitkas},
title={Enhancing Agent Intelligence through Evolving Reservoir Networks for Prediction in Power Stock Markets},
booktitle={Agent and Data Mining Interaction 2011 Workshop held in conjuction with the conference on Autonomous Agents and Multi-Agent Systems (AAMAS) 2011},
pages={228-247},
year={2011},
month={04},
date={2011-04-19},
url={http://issel.ee.auth.gr/wp-content/uploads/2017/01/Enhancing-Agent-Intelligence-through-Evolving-Reservoir-Networks-for-Predictions-in-Power-Stock-Markets.pdf},
keywords={Neuroevolution;Power Stock Markets;Reservoir Computing},
abstract={In recent years, Time Series Prediction and clustering have been employed in hyperactive and evolving environments -where temporal data play an important role- as a result of the need for reliable methods to estimate and predict the pattern or behavior of events and systems. Power Stock Markets are such highly dynamic and competitive auction environments, additionally perplexed by constrained power laws in the various stages, from production to transmission and consumption. As with all real-time auctioning environments, the limited time available for decision making provides an ideal testbed for autonomous agents to develop bidding strategies that exploit time series prediction. Within the context of this paper, we present Cassandra, a dynamic platform that fosters the development of Data-Mining enhanced Multi-agent systems. Special attention was given on the efficiency and reusability of Cassandra, which provides Plug-n-Play capabilities, so that users may adapt their solution to the problem at hand. Cassandra’s functionality is demonstrated through a pilot case, where autonomously adaptive Recurrent Neural Networks in the form of Echo State Networks are encapsulated into Cassandra agents, in order to generate power load and settlement price prediction models in typical Day-ahead Power Markets. The system has been tested in a real-world scenario, that of the Greek Energy Stock Market.}
}

2009

Inproceedings Papers

Antonios C. Chrysopoulos, Andreas L. Symeonidis and Pericles A. Mitkas
"Improving agent bidding in Power Stock Markets through a data mining enhanced agent platform"
Agents and Data Mining Interaction workshop AAMAS 2009, pp. 111-125, Springer-Verlag, Budapest, Hungary, 2009 May

Like in any other auctioning environment, entities participating in Power Stock Markets have to compete against other in order to maximize own revenue. Towards the satisfaction of their goal, these entities (agents - human or software ones) may adopt different types of strategies - from naive to extremely complex ones - in order to identify the most profitable goods compilation, the appropriate price to buy or sell etc, always under time pressure and auction environment constraints. Decisions become even more difficult to make in case one takes the vast volumes of historical data available into account: goods\\\\92 prices, market fluctuations, bidding habits and buying opportunities. Within the context of this paper we present Cassandra, a multi-agent platform that exploits data mining, in order to extract efficient models for predicting Power Settlement prices and Power Load values in typical Day-ahead Power markets. The functionality of Cassandra is discussed, while focus is given on the bidding mechanism of Cassandra\\\\92s agents, and the way data mining analysis is performed in order to generate the optimal forecasting models. Cassandra has been tested in a real-world scenario, with data derived from the Greek Energy Stock market.

@inproceedings{2009ChrysopoulosADMI,
author={Antonios C. Chrysopoulos and Andreas L. Symeonidis and Pericles A. Mitkas},
title={Improving agent bidding in Power Stock Markets through a data mining enhanced agent platform},
booktitle={Agents and Data Mining Interaction workshop AAMAS 2009},
pages={111-125},
publisher={Springer-Verlag},
address={Budapest, Hungary},
year={2009},
month={05},
date={2009-05-10},
url={http://issel.ee.auth.gr/wp-content/uploads/2016/04/Improving-agent-bidding-in-Power-Stock-Markets-through-a-data-mining-enhanced-agent-platform.pdf},
keywords={exploit data mining;multi-agent platform;predict Power Load;predict Power Settlement},
abstract={Like in any other auctioning environment, entities participating in Power Stock Markets have to compete against other in order to maximize own revenue. Towards the satisfaction of their goal, these entities (agents - human or software ones) may adopt different types of strategies - from naive to extremely complex ones - in order to identify the most profitable goods compilation, the appropriate price to buy or sell etc, always under time pressure and auction environment constraints. Decisions become even more difficult to make in case one takes the vast volumes of historical data available into account: goods\\\\\\\\92 prices, market fluctuations, bidding habits and buying opportunities. Within the context of this paper we present Cassandra, a multi-agent platform that exploits data mining, in order to extract efficient models for predicting Power Settlement prices and Power Load values in typical Day-ahead Power markets. The functionality of Cassandra is discussed, while focus is given on the bidding mechanism of Cassandra\\\\\\\\92s agents, and the way data mining analysis is performed in order to generate the optimal forecasting models. Cassandra has been tested in a real-world scenario, with data derived from the Greek Energy Stock market.}
}

Anthonios C. Chrysopoulos, Andreas L. Symeonidis and Pericles A. Mitkas
"Creating and Reusing Metric Graphs for Evaluating Agent Performance in the Supply Chain Management Domain"
Third Electrical and Computer Engineering Department Student Conference, pp. 245-267, IGI Global, Thessaloniki, Greece, 2009 Apr

The scope of this chapter is the presentation of Data Mining techniques for knowledge extraction in proteomics, taking into account both the particular features of most proteomics issues (such as data retrieval and system complexity), and the opportunities and constraints found in a Grid environment. The chapter discusses the way new and potentially useful knowledge can be extracted from proteomics data, utilizing Grid resources in a transparent way. Protein classification is introduced as a current research issue in proteomics, which also demonstrates most of the domain – specific traits. An overview of common and custom-made Data Mining algorithms is provided, with emphasis on the specific needs of protein classification problems. A unified methodology is presented for complex Data Mining processes on the Grid, highlighting the different application types and the benefits and drawbacks in each case. Finally, the methodology is validated through real-world case studies, deployed over the EGEE grid environment.

@inproceedings{2009ChrysopoulosECEDSC,
author={Anthonios C. Chrysopoulos and Andreas L. Symeonidis and Pericles A. Mitkas},
title={Creating and Reusing Metric Graphs for Evaluating Agent Performance in the Supply Chain Management Domain},
booktitle={Third Electrical and Computer Engineering Department Student Conference},
pages={245-267},
publisher={IGI Global},
address={Thessaloniki, Greece},
year={2009},
month={04},
date={2009-04-10},
url={http://issel.ee.auth.gr/wp-content/uploads/2016/02/Creating-and-Reusing-Metric-Graphs-for-Evaluating-Agent-Performance-in-the-Supply-Chain-Management-Domain.pdf},
keywords={Evaluating Agent Performance},
abstract={The scope of this chapter is the presentation of Data Mining techniques for knowledge extraction in proteomics, taking into account both the particular features of most proteomics issues (such as data retrieval and system complexity), and the opportunities and constraints found in a Grid environment. The chapter discusses the way new and potentially useful knowledge can be extracted from proteomics data, utilizing Grid resources in a transparent way. Protein classification is introduced as a current research issue in proteomics, which also demonstrates most of the domain – specific traits. An overview of common and custom-made Data Mining algorithms is provided, with emphasis on the specific needs of protein classification problems. A unified methodology is presented for complex Data Mining processes on the Grid, highlighting the different application types and the benefits and drawbacks in each case. Finally, the methodology is validated through real-world case studies, deployed over the EGEE grid environment.}
}