Fuzzy logic for short term load forecasting

D. K. Ranaweera; N. F. Hubele; G. G. Karady

doi:10.1016/0142-0615(95)00060-7

Fuzzy logic for short term load forecasting

D. K. Ranaweera, N. F. Hubele, G. G. Karady

Electrical, Computer, and Energy Engineering, School of (IAFSE-ECEE)

Research output: Contribution to journal › Article › peer-review

82 Scopus citations

Abstract

The results of an investigation of a fuzzy logic model for short term load forecasting are presented. The proposed methodology uses fuzzy rules to incorporate historical weather and load data. These fuzzy rules are obtained from the historical data using a learning-type algorithm. Test results from daily peak and total load forecasts for one year of data from a large scale power system indicate that the fuzzy rule bases can produce results similar in accuracy to more complicated statistical and back-propagation neural network methods.

Original language	English (US)
Pages (from-to)	215-222
Number of pages	8
Journal	International Journal of Electrical Power and Energy Systems
Volume	18
Issue number	4
DOIs	https://doi.org/10.1016/0142-0615(95)00060-7
State	Published - May 1996

Keywords

Back-propagation network
Fuzzy logic
Learning algorithm
Short term load forecasting

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1016/0142-0615(95)00060-7

Cite this

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abstract = "The results of an investigation of a fuzzy logic model for short term load forecasting are presented. The proposed methodology uses fuzzy rules to incorporate historical weather and load data. These fuzzy rules are obtained from the historical data using a learning-type algorithm. Test results from daily peak and total load forecasts for one year of data from a large scale power system indicate that the fuzzy rule bases can produce results similar in accuracy to more complicated statistical and back-propagation neural network methods.",

keywords = "Back-propagation network, Fuzzy logic, Learning algorithm, Short term load forecasting",

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year = "1996",

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AU - Hubele, N. F.

AU - Karady, G. G.

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N2 - The results of an investigation of a fuzzy logic model for short term load forecasting are presented. The proposed methodology uses fuzzy rules to incorporate historical weather and load data. These fuzzy rules are obtained from the historical data using a learning-type algorithm. Test results from daily peak and total load forecasts for one year of data from a large scale power system indicate that the fuzzy rule bases can produce results similar in accuracy to more complicated statistical and back-propagation neural network methods.

AB - The results of an investigation of a fuzzy logic model for short term load forecasting are presented. The proposed methodology uses fuzzy rules to incorporate historical weather and load data. These fuzzy rules are obtained from the historical data using a learning-type algorithm. Test results from daily peak and total load forecasts for one year of data from a large scale power system indicate that the fuzzy rule bases can produce results similar in accuracy to more complicated statistical and back-propagation neural network methods.

KW - Back-propagation network

KW - Fuzzy logic

KW - Learning algorithm

KW - Short term load forecasting

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JO - International Journal of Electrical Power and Energy Systems

JF - International Journal of Electrical Power and Energy Systems

IS - 4

ER -

Fuzzy logic for short term load forecasting

Abstract

Keywords

ASJC Scopus subject areas

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