Dynamic Fuzzy Clustering method for decision support in electricity markets negotiation
-
-
[1] GECAD - Polytechnic of Porto
-
- Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 5, Nº. 1, 2016, págs. 23-35
- Idioma: inglés
- DOI: 10.14201/ADCAIJ2016512336
- Enlaces
-
Resumen
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.
-
Referencias bibliográficas
- Alvarado-Pérez, J.C., Peluffo-Ordó-ez, D.H., Therón, R., 2015. Bridging the gap between human knowledge and machine learning. ADCAIJ Adv....
- Chamoso, P., De La Prieta, F., 2015. Swarm-Based Smart City Platform: A Traffic Application. ADCAIJ Adv. Distrib. Comput. Artif. Intell. Journal;...
- Dent, I., Craig, T., Aickelin, U., Rodden, T., 2014. Variability of Behaviour in Electricity Load Profile Clustering; Who Does Things at the...
- Faia, R., Pinto, T., Vale, Z., 2015a. Dynamic Fuzzy Estimation of Contracts Historic Information Using an Automatic Clustering Methodology....
- Faia, R., Pinto, T., Vale, Z., Pires, E.J.S., 2015b. Portfolio Optimization for Electricity Market Participation with Particle Swarm. 2015...
- Ferreira, A.S., Pozo, A., Gonçalves, R.A., 2015. An Ant Colony based Hyper-Heuristic Approach for the Set Covering Problem. ADCAIJ Adv. Distrib....
- Hu, X.B., Wang, M., Paolo, E. Di, 2013. Calculating Complete and Exact Pareto Front for Multiobjective Optimization: A New Deterministic Approach...
- Hwang, C.-L., Masud, A.S.M., 1979. Multiple Objective Decision Making — Methods and Applications, Lecture Notes in Economics and Mathematical...
- 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...
- Mahmoudi-Kohan, N., Moghaddam, M.P., Bidaki, S.M., 2009. Evaluating performance of WFA K-means and Modified Follow the leader methods for...
- 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...
- Pinto, T., Vale, Z., Sousa, T.M., Praça, I., Santos, G., Morais, H., 2014. Adaptive Learning in Agents Behaviour: A Framework for Electricity...
- Praça, I., Ramos, C., Vale, Z., Cordeiro, M., 2003. MASCEM: a multiagent system that simulates competitive electricity markets. IEEE Intell....
- 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
