Energy clearing price prediction and confidence interval estimation with cascaded neural networks

Abstract

The energy market clearing prices (MCPs) in deregulated power markets are volatile. Good MCP prediction and its confidence interval estimation will help utilities and independent power producers submit effective bids with low risks. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and various uncertainties interact in an intricate way. Furthermore, MCP predictors usually have a cascaded structure, as several key input factors need to be predicted first. Cascaded structures are widely used, however, they have not been adequately investigated. This paper analyzes the uncertainties involved in a cascaded neural-network (NN) structure for MCP prediction, and develops the prediction distribution under the Bayesian framework. A computationally efficient algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. Testing results on a classroom problem and on New England MCP prediction show that the method is computationally efficient and provides accurate prediction and confidence coverage. The scheme is generic, and can be applied to various networks, such as multilayer perceptrons and radial basis function networks.