An integrated neural network method for market clearing price prediction and confidence interval estimation
- 25 June 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 3, 2045-2050
- https://doi.org/10.1109/wcica.2002.1021444
Abstract
Market energy clearing prices (MCPs) play an important role in a deregulated power market, and good MCP prediction and interval estimation will help utilities and independent power producers submit effective bids with low risks. MCP is a non-stationary process, and an adaptive algorithm with fast convergence is important. A common method for MCP prediction is neural networks, and the extended Kalman filter (EKF) can be used as an integrated adaptive learning and interval estimation method, with fast convergence and small confidence interval. However, the EKF learning is computationally expensive because it involves high dimensional matrices. This paper presents a modified U-D factorization method within the framework of decoupled EKF. The computation speed is significantly improved and also is the numerical stability. EKF learning can then be used for high dimensional practical problems. Testing results show that the integrated learning and confidence interval algorithm provides better prediction than the back propagation algorithm and the confidence interval is smaller than that of a Bayesian inference-based interval estimation method.Keywords
This publication has 13 references indexed in Scilit:
- Decoupled extended Kalman filter training of feedforward layered networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Confidence regions for cascaded neural network prediction in power marketsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Forecasting loads and prices in competitive power marketsProceedings of the IEEE, 2000
- Confidence intervals for neural network based short-term load forecastingIEEE Transactions on Power Systems, 2000
- Estimations of error bounds for neural-network function approximatorsIEEE Transactions on Neural Networks, 1999
- Bayesian approach to neural-network modeling with input uncertaintyIEEE Transactions on Neural Networks, 1999
- Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networksIEEE Transactions on Neural Networks, 1994
- Optimal filtering algorithms for fast learning in feedforward neural networksNeural Networks, 1992
- Bayesian InterpolationNeural Computation, 1992
- Computational Experience with Confidence Regions and Confidence Intervals for Nonlinear Least SquaresTechnometrics, 1987