Paper Info
Title
Dynamic Fuzzy Estimation of Contracts Historic Information Using an Automatic Clustering Methodology
Abstract
With the recent liberalization of electricity markets, market players need to decide whether to and how to participate in each electricity market type that is available to them. The search for the best opportunities to sell or buy the required energy is, however, not an easy task. Moreover, the changes that electricity markets are constantly suffering make this an highly dynamic environment, with huge associated unpredictability. Decision support tools become, therefore, essential for market players to be able to take the best advantage from market participation. This paper proposes a methodology to estimate the expected prices of bilateral contracts based on the analysis of contracts' historic log. The proposed method is based on the application of a clustering methodology that groups the historic 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 log.
Year
DOI
Venue
2015
10.1007/978-3-319-19033-4_23
Communications in Computer and Information Science
Keywords
Field
DocType
Clustering,Electricity markets,Fuzzy logic,Portfolio optimization
Econometrics,Electricity market,Electricity,Computer science,Fuzzy logic,Decision support system,Portfolio optimization,Artificial intelligence,Liberalization,Missing data,Cluster analysis,Machine learning
Conference
Volume
ISSN
Citations 
524
1865-0929
2
PageRank 
References 
Authors
0.41
11
3
Name
Order
Citations
PageRank
Ricardo Faia1143.74
Tiago Pinto26825.43
Zita A. Vale339085.67