東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Neural network based load and price ...

Zhang, Li.

Linked to FindBook

Google Book

Amazon

博客來

Neural network based load and price forecasting and confidence interval estimation in deregulated power markets.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Neural network based load and price forecasting and confidence interval estimation in deregulated power markets./
Author:	Zhang, Li.
Description:	71 p.
Notes:	Adviser: Peter B. Luh.
Contained By:	Dissertation Abstracts International63-10B.
Subject:	Energy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3066268
ISBN:	0493857214

Neural network based load and price forecasting and confidence interval estimation in deregulated power markets.
Zhang, Li.

Neural network based load and price forecasting and confidence interval estimation in deregulated power markets. - 71 p.

Adviser: Peter B. Luh.

Thesis (Ph.D.)--The University of Connecticut, 2002.

The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies.

ISBN: 0493857214Subjects--Topical Terms:

876794
Energy.

Neural network based load and price forecasting and confidence interval estimation in deregulated power markets.
LDR:03453nam 2200325 a 45 001 932881
005 20110505
008 110505s2002 eng d
020 $a 0493857214
035 $a (UnM)AAI3066268
035 $a AAI3066268
040 $a UnM $c UnM
100 1 $a Zhang, Li. $3 729094
245 1 0 $a Neural network based load and price forecasting and confidence interval estimation in deregulated power markets.
300 $a 71 p.
500 $a Adviser: Peter B. Luh.
500 $a Source: Dissertation Abstracts International, Volume: 63-10, Section: B, page: 4838.
502 $a Thesis (Ph.D.)--The University of Connecticut, 2002.
520 $a The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies.
520 $a With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process.
520 $a In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance.
520 $a MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed.
520 $a The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman filter (EKF) can be used as an integrated adaptive learning and confidence interval estimation algorithm for neural networks, with fast convergence and small confidence intervals. However, EKF learning is computationally expensive because it involves high dimensional matrix manipulations. A modified U-D factorization within the decoupled EKF (DEKF-UD) framework is developed in this research. The computational efficiency and numerical stability are significantly improved.
590 $a School code: 0056.
650 4 $a Energy. $3 876794
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0544
690 $a 0791
710 2 0 $a The University of Connecticut. $3 1249323
773 0 $t Dissertation Abstracts International $g 63-10B.
790 $a 0056
790 1 0 $a Luh, Peter B., $e advisor
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3066268