NDLI: Power market clearing price prediction and confidence interval estimation with fast neural network learning

Content Provider	IEEE Xplore Digital Library
Author	Li Zhang Luh, P.B.
Copyright Year	2002
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Connecticut Univ., Storrs, CT, USA (Li Zhang; Luh, P.B.)
Abstract	Market 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 in this uncertain market. Since MCP is a nonstationary process, an adaptive algorithm with fast convergence is important. A common method for MCP prediction is neural networks, and multilayer perceptron networks (MLP) is one of the widely used networks. Backpropagation (BP) is a popular learning method for MLP, while BP suffers from slow convergence. This paper presents an integrated learning and interval estimation algorithm for MCP prediction. In the extended Kalman filter (EKF) framework, confidence interval is a natural by-product of EKF, and is integrated with learning process to improve learning results in addition to fast convergence. Since Kalman filter (KF) is a minimum variance estimator for linear system, EKF framework helps to provide a smaller confidence interval, which is preferred in risk management. Testing results on New England MCP prediction show the integrated learning and confidence interval algorithm provides better prediction than BP algorithm and the confidence interval is smaller with reasonable coverage than a Bayesian inference-based interval estimation method.
Starting Page	268
Ending Page	273
File Size	102521
Page Count	6
File Format	PDF
ISBN	0780373227
DOI	10.1109/PESW.2002.984997
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-01-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power markets Neural networks Convergence Prediction algorithms Inference algorithms Adaptive algorithm Multi-layer neural network Multilayer perceptrons Backpropagation Learning systems
Content Type	Text
Resource Type	Article

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