Mid-Term Residential Load Forecasting Based on Feature Selection Using Neighborhood Component Analysis

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.

Bahadornejad Iman; MOAZZAMI MAJID; SHAHGHOLIAN GHAZANFAR; FANI BAHADOR; Hashemi mahnaz

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

Journal Paper

Paper Information

Journal: COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING) Year:2022 | Volume:13 | Issue:2 Page(s): 103-114

Download Full-Text

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

Persian Verion

View:

200

Download:

Cites:

Information Journal Paper

Title

Mid-Term Residential Load Forecasting Based on Feature Selection Using Neighborhood Component Analysis

Author(s)

Keywords

Mid-Term Forecasting

Neighborhood Component Analysis

LBFGS Algorithm

Mean Squared Error

Mean Absolute Percentage Error

Abstract

Residential load forecasting plays an important role in management and planning of modern smart grids. Accurate residential load forecasting is needed in planning to keep demand and supply balanced. This paper presents a mid-term residential load forecasting method based on feature selection to solve the linear regression problem. In this way, for feature selection to perform the regression, the Neighborhood Component Analysis method is used. For this purpose, an optimization problem is designed, and the problem is solved using the LBFGS Algorithm. The AMPds2 dataset is used to implement the proposed method, and the results were compared with the results of the other six forecasting methods. Comparisons were made through Mean Squared Error, root Mean Squared Error, and Mean Absolute Percentage Error. The simulation results confirm the effectiveness of the proposed method for accurate residential load forecasting.

Cites

No record.

References

No record.

Cite

APA: Copy

Bahadornejad, Iman, MOAZZAMI, MAJID, SHAHGHOLIAN, GHAZANFAR, FANI, BAHADOR, & Hashemi, mahnaz. (2022). Mid-Term Residential Load Forecasting Based on Feature Selection Using Neighborhood Component Analysis. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), 13(2 ), 103-114. SID. https://sid.ir/paper/960722/en

Vancouver: Copy

Bahadornejad Iman, MOAZZAMI MAJID, SHAHGHOLIAN GHAZANFAR, FANI BAHADOR, Hashemi mahnaz. Mid-Term Residential Load Forecasting Based on Feature Selection Using Neighborhood Component Analysis. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)[Internet]. 2022;13(2 ):103-114. Available from: https://sid.ir/paper/960722/en

IEEE: Copy

Iman Bahadornejad, MAJID MOAZZAMI, GHAZANFAR SHAHGHOLIAN, BAHADOR FANI, and mahnaz Hashemi, “Mid-Term Residential Load Forecasting Based on Feature Selection Using Neighborhood Component Analysis,” COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), vol. 13, no. 2 , pp. 103–114, 2022, [Online]. Available: https://sid.ir/paper/960722/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops