REGRESSION FITTING IMPROVEMENT USING THE NEURAL NETWORK CLUSTERING ALGORITHM FOR MONTHLY PEAK LOAD FORECASTING

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

SOROUSH A.R.; BARADARAN KAZEMZADEH R.; BAHREYNINEZHAD A.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: INDUSTRIAL ENGINEERING & MANAGEMENT SHARIF (SHARIF: ENGINEERING) Year:2009 | Volume:25 | Issue:49 Page(s): 73-83

Download Full-Text

Persian Verion

View:

1,053

Download:

Cites:

Information Journal Paper

Title

REGRESSION FITTING IMPROVEMENT USING THE NEURAL NETWORK CLUSTERING ALGORITHM FOR MONTHLY PEAK LOAD FORECASTING

Author(s)

SOROUSH A.R. | BARADARAN KAZEMZADEH R. | BAHREYNINEZHAD A. | Issue Writer Certificate

Keywords

FORECASTINGQ2

MONTHLY ELECTRICAL PEAK LOADQ3

SELF-ORGANIZING MAP CLUSTERINGQ3

LINEAR REGRESSIONQ3

DAVIES-BOULDIN INDEXQ2

Abstract

In an electric power system, peak load FORECASTING plays an important role, in terms of economic optimization and fuel management. Accurate FORECASTING methods can be helpful especially for developing countries where the demand is increased with dynamic and high growth rate.This paper presents a new hybrid regression model, which combines a self-organizing map (SOM) and a LINEAR REGRESSION model for monthly peak load FORECASTING (PLF). Peak load data is used to test the model from the Iran electric power network from the last 14 years. Also, a principal component analysis (PCA) has been used to reduce input dimension. To evaluate the effectiveness of the model, FORECASTING has been performed by using a regression that uses the un-clustered data. Analysis and comparison of the results have shown the superiority and accuracy of the model.

Cites

No record.

References

No record.

Cite

APA: Copy

SOROUSH, A.R., BARADARAN KAZEMZADEH, R., & BAHREYNINEZHAD, A.. (2009). REGRESSION FITTING IMPROVEMENT USING THE NEURAL NETWORK CLUSTERING ALGORITHM FOR MONTHLY PEAK LOAD FORECASTING. INDUSTRIAL ENGINEERING & MANAGEMENT SHARIF (SHARIF: ENGINEERING), 25(49), 73-83. SID. https://sid.ir/paper/107579/en

Vancouver: Copy

SOROUSH A.R., BARADARAN KAZEMZADEH R., BAHREYNINEZHAD A.. REGRESSION FITTING IMPROVEMENT USING THE NEURAL NETWORK CLUSTERING ALGORITHM FOR MONTHLY PEAK LOAD FORECASTING. INDUSTRIAL ENGINEERING & MANAGEMENT SHARIF (SHARIF: ENGINEERING)[Internet]. 2009;25(49):73-83. Available from: https://sid.ir/paper/107579/en

IEEE: Copy

A.R. SOROUSH, R. BARADARAN KAZEMZADEH, and A. BAHREYNINEZHAD, “REGRESSION FITTING IMPROVEMENT USING THE NEURAL NETWORK CLUSTERING ALGORITHM FOR MONTHLY PEAK LOAD FORECASTING,” INDUSTRIAL ENGINEERING & MANAGEMENT SHARIF (SHARIF: ENGINEERING), vol. 25, no. 49, pp. 73–83, 2009, [Online]. Available: https://sid.ir/paper/107579/en

Related Journal Papers