Short-Term Load Forecasting Using Fuzzy Logic

Jordan Blancas; Julien Noel

doi:10.1109/TDC-LA.2018.8511751

Short-Term Load Forecasting Using Fuzzy Logic

Jordan Blancas, Julien Noel

Research output: Chapter in Book/Report › Conference contribution › peer-review

6 Scopus citations

Abstract

In this paper, fuzzy logic (FL) is applied to the problem of short-Term load forecasting (next day) in electrical power systems. To achieve this, it is necessary to select the historical data to be used and pre-process them using the c-means method, grouping them according to power levels (MW) to define the number of membership functions (MFs) to the fuzzy system, which is very important for the calculation of the lowest forecast error; finally, the historical data are entered into the fuzzy system implemented in MATLAB. This methodology is applied to predict the daily electrical load (demand) of the Peruvian Electrical System using the historical data of the actual demand executed for the study period and by calculating the MAPE error. It is shown that the FL offers better results than the conventional methodology for the forecast of the electrical load.

Original language	American English
Title of host publication	Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538658444
DOIs	https://doi.org/10.1109/TDC-LA.2018.8511751
State	Indexed - 26 Oct 2018
Externally published	Yes
Event	2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA - Lima, Peru Duration: 18 Sep 2018 → 21 Sep 2018

Publication series

Name	Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018

Conference

Conference	2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA
Country/Territory	Peru
City	Lima
Period	18/09/18 → 21/09/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Clustering
fuzzy logic
Peruvian interconnected electrical system.
short-Term load forecasting
time series

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/TDC-LA.2018.8511751

Cite this

Blancas, J., & Noel, J. (2018). Short-Term Load Forecasting Using Fuzzy Logic. In Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018 Article 8511751 (Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TDC-LA.2018.8511751

Blancas, Jordan ; Noel, Julien. / Short-Term Load Forecasting Using Fuzzy Logic. Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018).

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title = "Short-Term Load Forecasting Using Fuzzy Logic",

abstract = "In this paper, fuzzy logic (FL) is applied to the problem of short-Term load forecasting (next day) in electrical power systems. To achieve this, it is necessary to select the historical data to be used and pre-process them using the c-means method, grouping them according to power levels (MW) to define the number of membership functions (MFs) to the fuzzy system, which is very important for the calculation of the lowest forecast error; finally, the historical data are entered into the fuzzy system implemented in MATLAB. This methodology is applied to predict the daily electrical load (demand) of the Peruvian Electrical System using the historical data of the actual demand executed for the study period and by calculating the MAPE error. It is shown that the FL offers better results than the conventional methodology for the forecast of the electrical load.",

keywords = "Clustering, fuzzy logic, Peruvian interconnected electrical system., short-Term load forecasting, time series",

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Blancas, J & Noel, J 2018, Short-Term Load Forecasting Using Fuzzy Logic. in Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018., 8511751, Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA, Lima, Peru, 18/09/18. https://doi.org/10.1109/TDC-LA.2018.8511751

Short-Term Load Forecasting Using Fuzzy Logic. / Blancas, Jordan; Noel, Julien.
Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8511751 (Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018).

Research output: Chapter in Book/Report › Conference contribution › peer-review

TY - GEN

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AU - Blancas, Jordan

AU - Noel, Julien

PY - 2018/10/26

Y1 - 2018/10/26

N2 - In this paper, fuzzy logic (FL) is applied to the problem of short-Term load forecasting (next day) in electrical power systems. To achieve this, it is necessary to select the historical data to be used and pre-process them using the c-means method, grouping them according to power levels (MW) to define the number of membership functions (MFs) to the fuzzy system, which is very important for the calculation of the lowest forecast error; finally, the historical data are entered into the fuzzy system implemented in MATLAB. This methodology is applied to predict the daily electrical load (demand) of the Peruvian Electrical System using the historical data of the actual demand executed for the study period and by calculating the MAPE error. It is shown that the FL offers better results than the conventional methodology for the forecast of the electrical load.

AB - In this paper, fuzzy logic (FL) is applied to the problem of short-Term load forecasting (next day) in electrical power systems. To achieve this, it is necessary to select the historical data to be used and pre-process them using the c-means method, grouping them according to power levels (MW) to define the number of membership functions (MFs) to the fuzzy system, which is very important for the calculation of the lowest forecast error; finally, the historical data are entered into the fuzzy system implemented in MATLAB. This methodology is applied to predict the daily electrical load (demand) of the Peruvian Electrical System using the historical data of the actual demand executed for the study period and by calculating the MAPE error. It is shown that the FL offers better results than the conventional methodology for the forecast of the electrical load.

KW - Clustering

KW - fuzzy logic

KW - Peruvian interconnected electrical system.

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KW - time series

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BT - Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018

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Blancas J, Noel J. Short-Term Load Forecasting Using Fuzzy Logic. In Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8511751. (Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018). doi: 10.1109/TDC-LA.2018.8511751

Short-Term Load Forecasting Using Fuzzy Logic

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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Cite this