Modeling of bidding prices using soft computing techniques

In this paper an strategy for discovering patterns over the continuous domain of bidding prices is proposed. In particular, the proposed method represents bidding functions as points in a multidimensional space where a clustering algorithm is applied. On the other hand, as a result of this method, a dramatic reduction over the search space of bidding strategies is achieved. In addition, some relations of dominance over bidding strategies are found, improving the pattern recognition process of agentpsilas bidding behavior. This method is applied on the bidding prices database for some GENCOpsilas of the colombian power market. Furthermore, an application of some data mining algorithms is presented with the purpose of quantifying some hypothesis formulated on the effect of hydrology over spot and bidding prices.