Forecasting market clearing price using self-organizing map neural network
Abstract
Forecasting the market-clearing price MCP) is the most essential task for any decision-making in electricity market. Artificial neural network (ANN) is a preferable forecasting method. However, there still exist some theoretic shortcomings in ANN method, such as the time-consuming sample training and convergence problem. Especially when the characteristic of sample is hard to capture, those phenomena will be more explicit. To solve the problem, based on the characteristic of self-organizing and clustering of self-organizing map (SOM), this paper proposes a method to deal with the sample dataset of the BP model, which can perform a data analysis, and then form a new training dataset. By using the BP network on the new dataset analyzed by SOM for the prediction, the efficiency is advanced remarkably and the prediction is satisfactory.
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