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 na?ve 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’ 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’s 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amin, M.: Restructuring the Electric Enterprise. In: Market Analysis and Resource Management, pp. 2–16. Kluwer Publishers, Dordrecht (2002)
Amin, M., Ballard, D.: Defining new markets for intelligent agents. IT Professional 2(4), 29–35 (2000)
Bagnall, A.J., Smith, G.D.: Game playing with autonomous adaptive agents in a simplified economic model of the uk market in electricity generation. In: IEEE-PES / CSEE International Conference on Power System Technology, POWERCON 2000, pp. 891–896 (2000)
Bellifemine, F., Poggi, A., Rimassa, R.: Developing multi-agent systems with JADE. In: Castelfranchi, C., Lespérance, Y. (eds.) ATAL 2000. LNCS, vol. 1986, pp. 89–101. Springer, Heidelberg (2001)
Conzelmann, G., Boyd, G., Koritarov, V., Veselka, T.: Multi-agent power market simulation using emcas 3, 2829–2834 (2005)
Freedman, D.: Statistical Models: Theory and Practice. Cambridge University Press, Cambridge (2005)
He, M., Jennings, N.R., fung Leung, H.: On agent-mediated electronic commerce. IEEE Trans. Knowl. Data Eng. 15(4), 985–1003 (2003)
Holland, J.H.: Genetic Algorithms and Classifier Systems: Foundations and Future Directions. In: Grefenstette, J.J. (ed.) Proceedings of the 2nd International Conference on Genetic Algorithms (ICGA 1987), Cambridge, MA, pp. 82–89. Lawrence Erlbaum Associates (1987)
Koesrindartoto, D.P., Sun, J.: An agent-based computational laboratory for testing the economic reliability of wholesale power market designs. Computing in Economics and Finance 2005 50, Society for Computational Economics (November 2005)
Koesrindartoto, D.P., Tesfatsion, L.S.: Testing the reliability of ferc’s wholesale power market platform: An agent-based computational economics approach. Staff General Research Papers 12326, Iowa State University, Department of Economics (May 2005)
Petrov, V., Sheble, G.: Power auctions bid generation with adaptive agents using genetic programming. In: I. of Electrical and E. Engineers (eds.) Proceedings of the 2000 North American Power Symposium (2000)
Praca, I., Ramos, C., Vale, Z., Cordeiro, M.: Mascem: a multiagent system that simulates competitive electricity markets. IEEE Intelligent Systems 18(6), 54–60 (2003)
Praca, I., Ramos, C., Vale, Z., Cordeiro, M.: Intelligent agents for negotiation and game-based decision support in electricity markets. Engineering intelligent systems for electrical engineering and communications 13(2), 147–154 (2005)
Praca, I., Ramos, C., Vale, Z., Cordeiro, M.: Testing the scenario analysis algorithm of an agent-based simulator for competitive electricity markets. In: ECMS (ed.) Proceedings 19th European Conference on Modeling and Simulation (2005)
Rosenschein, J.S., Zlotkin, G.: Rules of Encounter: Designing Conventions for Automated Negotiation Among Computers. MIT Press, Cambridge (1994)
Stone, P.: Learning and multiagent reasoning for autonomous agents. In: 20th International Joint Conference on Artificial Intelligence, pp. 13–30 (2007)
Symeonidis, A.L., Mitkas, P.A.: Agent Intelligence Through Data Mining. Springer Science and Business Media (2005)
Tellidou, A., Bakirtzis, A.: Multi-agent reinforcement learning for strategic bidding in power markets, pp. 408–413 (September 2006)
The FIPA Foundations. Foundation for intelligent physical agents specifications. Technical report, The FIPA Consortium (2003)
Vickrey, W.: Counterspeculation, auctions, and competitive sealed tenders. The Journal of Finance 16(1), 8–37 (1961)
Watkins, C.J.C.H.: Learning from Delayed Rewards. PhD thesis, King’s College, Cambridge, UK (1989)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufman, San Francisco (2000)
Wurman, P.R., Wellman, M.P., Walsh, W.E.: The michigan internet auctionbot: a configurable auction server for human and software agents. In: Second International Conference on Autonomous Agents, pp. 301–308. ACM Press, New York (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chrysopoulos, A.C., Symeonidis, A.L., Mitkas, P.A. (2009). Improving Agent Bidding in Power Stock Markets through a Data Mining Enhanced Agent Platform. In: Cao, L., Gorodetsky, V., Liu, J., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2009. Lecture Notes in Computer Science(), vol 5680. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03603-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-03603-3_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03602-6
Online ISBN: 978-3-642-03603-3
eBook Packages: Computer ScienceComputer Science (R0)