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

Producción científica: Libro o Capítulo del libro › Contribución a la conferencia › revisión exhaustiva

6 Citas (Scopus)

Resumen

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.

Idioma original	Inglés estadounidense
Título de la publicación alojada	Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781538658444
DOI	https://doi.org/10.1109/TDC-LA.2018.8511751
Estado	Indizado - 26 oct. 2018
Publicado de forma externa	Sí
Evento	2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA - Lima, Perú Duración: 18 set. 2018 → 21 set. 2018

Serie de la publicación

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

Conferencia

Conferencia	2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA
País/Territorio	Perú
Ciudad	Lima
Período	18/09/18 → 21/09/18

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© 2018 IEEE.

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Blancas, J., & Noel, J. (2018). Short-Term Load Forecasting Using Fuzzy Logic. En Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018 Artículo 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",

author = "Jordan Blancas and Julien Noel",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA ; Conference date: 18-09-2018 Through 21-09-2018",

year = "2018",

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Blancas, J & Noel, J 2018, Short-Term Load Forecasting Using Fuzzy Logic. En 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, Perú, 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).

Producción científica: Libro o Capítulo del libro › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Short-Term Load Forecasting Using Fuzzy Logic

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.

KW - short-Term load forecasting

KW - time series

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T3 - Proceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA

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ER -

Blancas J, Noel J. Short-Term Load Forecasting Using Fuzzy Logic. En 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

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