An Empirical Study on IDTBN Applied to the Integration of Expert Opinions -- The Case of Long-term Electric Load Prediction
Date Issued
2008
Date
2008
Author(s)
Yang, Hsiu-Shan
Abstract
The enterprise rely on a lot of information to lead production, research and development activities; therefore, how to transfer the information into knowledge which assists enterprises in making decision, becomes one of the most important issue in the age of information overload. Usually, extracting and integrating expert opinions of specific fields are the common rule to acquire knowledge.ffectively converging expert opinions and showing the structure of the knowledge model can be used for analysis and application in the future and the decision-making of related areas. This thesis aims to extract the extraneous variables and their impact degree toward predicting value. The goal is to integrate Decision Tree analysis and Bayesian Network and construct a complete knowledge profile of experts with those variables and data. Not only will the predicting value of single analyst’s view but also an inferring structure including multiple views of experts be acquired.ecause the integration of demand and supply side for electric power could enhance the utility performance of electric power resource, the prediction of power load becomes more and more important. With the aid of Decision Support System (DSS), the power load could be predicted by making use of historical records and experts’ opinions and slightly adjusting the predicting value of DSS by experts, but the great knowledge structure used for judgment was not kept. In this study, we select the power industry as our research target, and use IDTBN (Integrated Decision Tree and Bayesian Network) method presented in this paper, to integrate the opinions given in the expert meeting and build an analysis model. As a result, it can improve the prediction of electric power load and cope with the changing trend of the future.
Subjects
Bayesian Network
Decision Tree
Data Mining
Integration of Expert Opinions
Long-term Electric Load Prediction
File(s)![Thumbnail Image]()
Loading...
Name
ntu-97-R95725004-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):26b795259b5d4d8db46c2d03f1a1fc2f