Forecasting power market clearing price and its discrete PDF using a Bayesian-based classification method

In this paper, a classification method combined with a simple regression model is presented to predict the discrete PDF of power market clearing prices (MCP), which is critical for much decision-making such as optimizing bidding strategies, and is very difficult to predict because of high market uncertainties. Our basic idea is to form a number of classes by discretizing the variable to be predicted, and thus convert the time series prediction problem into a pattern classification problem. The classes are clustered based on historical data, and the input statistics (e.g., input mean and covariance matrix, etc.) corresponding to each class are obtained via clustering. Given a new input, these classes are sorted according to the posterior probabilities calculated by using the Bayes' formula. The prediction is then generated by using an auto-regression method (AR) for the classes with high posterior probabilities. The classes are then updated after the actual output is available. The method developed can be seen as an RBF with variable structure. The posterior probabilities can be viewed as an approximation of the discrete PDF, providing valuable information for decision-makers and for what if analysis. The developed method has been used to predict the on-peak and off-peak average MCPs in the New England market. The testing results show that it outperforms the RBF network in terms of prediction accuracy and computational time.