dor_id: 4134867

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Contaduría y Administración", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Consejo Nacional de Ciencia y Tecnología (CONACyT); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Scientific Electronic Library Online (SciELO); SCOPUS, SCImago Journal Rank (SJR)

561.#.#.u: https://www.fca.unam.mx/

561.#.#.a: no

650.#.4.x: Ciencias Sociales y Económicas

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: http://www.cya.unam.mx/index.php/cya/index

351.#.#.b: Contaduría y Administración

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: http://www.cya.unam.mx/index.php/cya/article/view/3356/1913

100.1.#.a: Peujio Jiotsop Foze, Wellcome; Hernandez Del Valle, Adrián

524.#.#.a: Peujio Jiotsop Foze, Wellcome, et al. (2023). Hours Ahead Automed Long Short-term Memory (lstm) Electricity Load Forecasting At Substation Level: Newcastle Substation. Contaduría y Administración; Vol. 68, Núm. 1; e370. Recuperado de https://repositorio.unam.mx/contenidos/4134867

720.#.#.a: Escuela Superior de Economía, CONACYT

245.1.0.a: Hours Ahead Automed Long Short-term Memory (lstm) Electricity Load Forecasting At Substation Level: Newcastle Substation

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Facultad de Contaduría y Administración, UNAM

264.#.0.c: 2023

264.#.1.c: 2022-10-12

653.#.#.a: Economics, Econometrics, Forecasting.; deep learning; forecasting; electric load; LSTM; substation

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY 4.0 Internacional, https://creativecommons.org/licenses/by/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico revista_cya@fca.unam.mx

884.#.#.k: http://www.cya.unam.mx/index.php/cya/article/view/3356

001.#.#.#: 032.oai:ojs.pkp.sfu.ca:article/3356

041.#.7.h: spa

520.3.#.a: Nowadays, electrical energy is of vital importance in our lives, every country needs this resource to develop its economy, factories, businesses, and homes are the basis of the economic structure of a country. In the city of Newcastle as in other cities are in constant development growing day by day in terms of industries, homes and businesses, these elements are the ones that consume all the electricity produced in Newcastle. Although Australia has strategically located substations that serve the function of supplying all existing loads with quality power, from time to time the load will exceed the capacity of these substations and will not be able to supply the loads that will arise in the future as the city grows. To find a solution to this problem, we use a deep learning model to improve accuracy. In this paper, a Long Short-Term Memory recurrent neural network (LSTM) is tested on a publicly available 30-minute dataset containing measured real power data for individual zone substations in the Ausgrid supply area data. The performance of the model is comprehensively compared with 4 different configurations of the LSTM. The proposed LSTM approach with 2 hidden layers and 50 neurons outperforms the other configurations with a mean absolute error (MAE) of 0.0050 in the short-term load forecasting task for substations.

773.1.#.t: Contaduría y Administración; Vol. 68, Núm. 1; e370

773.1.#.o: http://www.cya.unam.mx/index.php/cya/index

022.#.#.a: ISSN electrónico: 2448-8410; ISSN impreso: 0186-1042

310.#.#.a: Trimestral

264.#.1.b: Facultad de Contaduría y Administración, UNAM

doi: https://doi.org/10.22201/fca.24488410e.2023.3356

handle: 67ea9614350840ac

harvesting_date: 2023-03-22 12:00:00.0

856.#.0.q: application/pdf

file_creation_date: 2022-10-17 22:46:20.0

file_modification_date: 2022-10-17 22:46:20.0

file_creator: HP

file_name: 963f5afbc57258dcee75668b9fa2f27776086c5b77e914ec2366fee7ce2b841c.pdf

file_pages_number: 20

file_format_version: application/pdf; version=1.7

file_size: 553537

last_modified: 2023-03-22 14:00:00

license_url: https://creativecommons.org/licenses/by/4.0/legalcode.es

license_type: by

No entro en nada

No entro en nada 2

Artículo

Hours Ahead Automed Long Short-term Memory (lstm) Electricity Load Forecasting At Substation Level: Newcastle Substation

Peujio Jiotsop Foze, Wellcome; Hernandez Del Valle, Adrián

Facultad de Contaduría y Administración, UNAM, publicado en Contaduría y Administración, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Entidad o dependencia
Facultad de Contaduría y Administración, UNAM
Revista
Contaduría y Administración
Repositorio
Revistas UNAM
Contacto
Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

Cita

Peujio Jiotsop Foze, Wellcome, et al. (2023). Hours Ahead Automed Long Short-term Memory (lstm) Electricity Load Forecasting At Substation Level: Newcastle Substation. Contaduría y Administración; Vol. 68, Núm. 1; e370. Recuperado de https://repositorio.unam.mx/contenidos/4134867

Descripción del recurso

Autor(es)
Peujio Jiotsop Foze, Wellcome; Hernandez Del Valle, Adrián
Colaborador(es)
Escuela Superior de Economía, CONACYT
Tipo
Artículo de Investigación
Área del conocimiento
Ciencias Sociales y Económicas
Título
Hours Ahead Automed Long Short-term Memory (lstm) Electricity Load Forecasting At Substation Level: Newcastle Substation
Fecha
2022-10-12
Resumen
Nowadays, electrical energy is of vital importance in our lives, every country needs this resource to develop its economy, factories, businesses, and homes are the basis of the economic structure of a country. In the city of Newcastle as in other cities are in constant development growing day by day in terms of industries, homes and businesses, these elements are the ones that consume all the electricity produced in Newcastle. Although Australia has strategically located substations that serve the function of supplying all existing loads with quality power, from time to time the load will exceed the capacity of these substations and will not be able to supply the loads that will arise in the future as the city grows. To find a solution to this problem, we use a deep learning model to improve accuracy. In this paper, a Long Short-Term Memory recurrent neural network (LSTM) is tested on a publicly available 30-minute dataset containing measured real power data for individual zone substations in the Ausgrid supply area data. The performance of the model is comprehensively compared with 4 different configurations of the LSTM. The proposed LSTM approach with 2 hidden layers and 50 neurons outperforms the other configurations with a mean absolute error (MAE) of 0.0050 in the short-term load forecasting task for substations.
Tema
Economics, Econometrics, Forecasting.; deep learning; forecasting; electric load; LSTM; substation
Idioma
spa
ISSN
ISSN electrónico: 2448-8410; ISSN impreso: 0186-1042

Enlaces