Context
A fuzzy adaptive correction scheme for short term load forecasting using fuzzy layered neural network
Title:
A fuzzy adaptive correction scheme for short term load forecasting using fuzzy layered neural network
Archive:
Dspace@nitr
Author(s):
Dash, P K
Dash, S
Rahman, S
Dash, S
Rahman, S
Date:
2005-06-29
Abstract:
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A hybrid neural network-fuzzy expert system is developed to forecast one hour to forty-eight hour ahead electric load accurately. The fuzzy membership values of load and other weather variables are the inputs to the neural network and the output comprises the membership value of the predicted load. An adaptive fuzzy correction scheme is used to forecast the final load by using a fuzzy rule base and fuzzy inference mechanism. The paper also presents a fuzzy pattern classification approach for identifying the day-type from the historical load database to be used for training the neural network. Extensive studies have been performed for all seasons, although the results for a typical winter day are given in the paper to demonstrate the powerfulness of this technique
A hybrid neural network-fuzzy expert system is developed to forecast one hour to forty-eight hour ahead electric load accurately. The fuzzy membership values of load and other weather variables are the inputs to the neural network and the output comprises the membership value of the predicted load. An adaptive fuzzy correction scheme is used to forecast the final load by using a fuzzy rule base and fuzzy inference mechanism. The paper also presents a fuzzy pattern classification approach for identifying the day-type from the historical load database to be used for training the neural network. Extensive studies have been performed for all seasons, although the results for a typical winter day are given in the paper to demonstrate the powerfulness of this technique
Index terms:
Discipline(s):
expert systems
Subject(s):
inference mechanisms; load forecasting; neural nets
Method/Approach:
Coverage:
Publisher:
IEEE
Contributors:
Source:
Language:
en
Relation:
Type:
Article
Format:
498524 bytes
application/pdf
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