Dynamic Fuzzy Clustering Method for Decision Support in Electricity Markets Negotiation
Abstract Artificial Intelligence (AI) methods contribute to the construction of systems where there is a need to automate the tasks. They are typically used for problems that have a large response time, or when a mathematical method cannot be used to solve the problem. However, the application of AI brings an added complexity to the development of such applications. AI has been frequently applied in the power systems field, namely in Electricity Markets (EM). In this area, AI applications are essentially used to forecast / estimate the prices of electricity or to search for the best opportunity to sell the product. This paper proposes a clustering methodology that is combined with fuzzy logic in order to perform the estimation of EM prices. The proposed method is based on the application of a clustering methodology that groups historic energy contracts according to their prices’ similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts’ history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts.
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Alvarado-Pérez, J.C., Peluffo-Ordó-ez, D.H., Therón, R., 2015. Bridging the gap between human knowledge and machine learning. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 4, 54. http://dx.doi.org/10.14201/adcaij2015415464
Chamoso, P., De La Prieta, F., 2015. Swarm-Based Smart City Platform: A Traffic Application. ADCAIJ Adv. Distrib. Comput. Artif. Intell. Journal; Vol 4, No 2.
Dent, I., Craig, T., Aickelin, U., Rodden, T., 2014. Variability of Behaviour in Electricity Load Profile Clustering; Who Does Things at the Same Time Each Day? In: Perner, P. (Ed.), Advances in Data Mining. Applications and Theoretical Aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings. Springer International Publishing, Cham, pp. 70–84.http://dx.doi.org/10.1007/978-3-319-08976-8_6
Faia, R., Pinto, T., Vale, Z., 2015a. Dynamic Fuzzy Estimation of Contracts Historic Information Using an Automatic Clustering Methodology. In: Bajo, J., Hallenborg, K., Pawlewski, P., Botti, V., Sánchez-Pi, N., Duque Méndez, N.D., Lopes, F., Julian, V. (Eds.), Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability - The PAAMS Collection SE - 23. Springer International Publishing, pp. 270–282. http://dx.doi.org/10.1007/978-3-319-19033-4_23
Faia, R., Pinto, T., Vale, Z., Pires, E.J.S., 2015b. Portfolio Optimization for Electricity Market Participation with Particle Swarm. 2015 26th Int. Work. Database Expert Syst. Appl.
Ferreira, A.S., Pozo, A., Gonçalves, R.A., 2015. An Ant Colony based Hyper-Heuristic Approach for the Set Covering Problem. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 4, 1. http://dx.doi.org/10.14201/adcaij201541121
Hu, X.B., Wang, M., Paolo, E. Di, 2013. Calculating Complete and Exact Pareto Front for Multiobjective Optimization: A New Deterministic Approach for Discrete Problems. IEEE Trans. Cybern.
Hwang, C.-L., Masud, A.S.M., 1979. Multiple Objective Decision Making — Methods and Applications, Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, Berlin, Heidelberg.
Jain, A.K., 2010. Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31, 651–666. http://dx.doi.org/10.1016/j.patrec.2009.09.011
Li, H., Tesfatsion, L., 2009. Development of open source software for power market research: The AMES test bed [WWW Document]. J. Energy Mark.
MacQueen, J., 1967. Some Methods for Classification and Analysis of MultiVariate Observations. In: 5th Symposium on Mathematical Statistics and Probability. pp. 281–297.
Mahmoudi-Kohan, N., Moghaddam, M.P., Bidaki, S.M., 2009. Evaluating performance of WFA K-means and Modified Follow the leader methods for clustering load curves. Power Syst. Conf. Expo. 2009. PSCE '09. IEEE/PES.
Mcculloch, W., Pitts, W., 1943. A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics. http://dx.doi.org/10.1007/BF02478259
Meeus, L., Purchala, K., Belmans, R., 2005. Development of the Internal Electricity Market in Europe. Electr. J. 18, 25–35. http://dx.doi.org/10.1016/j.tej.2005.06.008
Pinto, T., Morais, H., Sousa, T.M., Sousa, T., Vale, Z., Praca, I., Faia, R., Pires, E.J.S., 2015. Adaptive Portfolio Optimization for Multiple Electricity Markets Participation. Neural Networks Learn. Syst. IEEE Trans.
Pinto, T., Vale, Z., Sousa, T.M., Praça, I., Santos, G., Morais, H., 2014. Adaptive Learning in Agents Behaviour: A Framework for Electricity Markets Simulation. Integr. Comput. Eng. 21, 399–415.
Praça, I., Ramos, C., Vale, Z., Cordeiro, M., 2003. MASCEM: a multiagent system that simulates competitive electricity markets. IEEE Intell. Syst.
Santos, G., Pinto, T., Praça, I., Vale, Z., 2016. MASCEM: Optimizing the performance of a multi-agent system. Energy 111, 513–524. http://dx.doi.org/10.1016/j.energy.2016.05.127
Shahidehpour, M., Yamin, H., Li, Z., 2002. Market Overview in Electric Power Systems. Mark. Oper. Electr. Power Syst. Sched. Risk Manag.
Zadeh, L.A., 1965. Fuzzy sets. Inf. Control 8, 338–353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
Chamoso, P., De La Prieta, F., 2015. Swarm-Based Smart City Platform: A Traffic Application. ADCAIJ Adv. Distrib. Comput. Artif. Intell. Journal; Vol 4, No 2.
Dent, I., Craig, T., Aickelin, U., Rodden, T., 2014. Variability of Behaviour in Electricity Load Profile Clustering; Who Does Things at the Same Time Each Day? In: Perner, P. (Ed.), Advances in Data Mining. Applications and Theoretical Aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings. Springer International Publishing, Cham, pp. 70–84.http://dx.doi.org/10.1007/978-3-319-08976-8_6
Faia, R., Pinto, T., Vale, Z., 2015a. Dynamic Fuzzy Estimation of Contracts Historic Information Using an Automatic Clustering Methodology. In: Bajo, J., Hallenborg, K., Pawlewski, P., Botti, V., Sánchez-Pi, N., Duque Méndez, N.D., Lopes, F., Julian, V. (Eds.), Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability - The PAAMS Collection SE - 23. Springer International Publishing, pp. 270–282. http://dx.doi.org/10.1007/978-3-319-19033-4_23
Faia, R., Pinto, T., Vale, Z., Pires, E.J.S., 2015b. Portfolio Optimization for Electricity Market Participation with Particle Swarm. 2015 26th Int. Work. Database Expert Syst. Appl.
Ferreira, A.S., Pozo, A., Gonçalves, R.A., 2015. An Ant Colony based Hyper-Heuristic Approach for the Set Covering Problem. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 4, 1. http://dx.doi.org/10.14201/adcaij201541121
Hu, X.B., Wang, M., Paolo, E. Di, 2013. Calculating Complete and Exact Pareto Front for Multiobjective Optimization: A New Deterministic Approach for Discrete Problems. IEEE Trans. Cybern.
Hwang, C.-L., Masud, A.S.M., 1979. Multiple Objective Decision Making — Methods and Applications, Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, Berlin, Heidelberg.
Jain, A.K., 2010. Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31, 651–666. http://dx.doi.org/10.1016/j.patrec.2009.09.011
Li, H., Tesfatsion, L., 2009. Development of open source software for power market research: The AMES test bed [WWW Document]. J. Energy Mark.
MacQueen, J., 1967. Some Methods for Classification and Analysis of MultiVariate Observations. In: 5th Symposium on Mathematical Statistics and Probability. pp. 281–297.
Mahmoudi-Kohan, N., Moghaddam, M.P., Bidaki, S.M., 2009. Evaluating performance of WFA K-means and Modified Follow the leader methods for clustering load curves. Power Syst. Conf. Expo. 2009. PSCE '09. IEEE/PES.
Mcculloch, W., Pitts, W., 1943. A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics. http://dx.doi.org/10.1007/BF02478259
Meeus, L., Purchala, K., Belmans, R., 2005. Development of the Internal Electricity Market in Europe. Electr. J. 18, 25–35. http://dx.doi.org/10.1016/j.tej.2005.06.008
Pinto, T., Morais, H., Sousa, T.M., Sousa, T., Vale, Z., Praca, I., Faia, R., Pires, E.J.S., 2015. Adaptive Portfolio Optimization for Multiple Electricity Markets Participation. Neural Networks Learn. Syst. IEEE Trans.
Pinto, T., Vale, Z., Sousa, T.M., Praça, I., Santos, G., Morais, H., 2014. Adaptive Learning in Agents Behaviour: A Framework for Electricity Markets Simulation. Integr. Comput. Eng. 21, 399–415.
Praça, I., Ramos, C., Vale, Z., Cordeiro, M., 2003. MASCEM: a multiagent system that simulates competitive electricity markets. IEEE Intell. Syst.
Santos, G., Pinto, T., Praça, I., Vale, Z., 2016. MASCEM: Optimizing the performance of a multi-agent system. Energy 111, 513–524. http://dx.doi.org/10.1016/j.energy.2016.05.127
Shahidehpour, M., Yamin, H., Li, Z., 2002. Market Overview in Electric Power Systems. Mark. Oper. Electr. Power Syst. Sched. Risk Manag.
Zadeh, L.A., 1965. Fuzzy sets. Inf. Control 8, 338–353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
Faia, R., Pinto, T., & Vale, Z. (2016). Dynamic Fuzzy Clustering Method for Decision Support in Electricity Markets Negotiation. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 5(1), 23–35. https://doi.org/10.14201/ADCAIJ2016512336
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