Electricity demand forecast model based on meteorological and historical demand data using artificial neural networks

Accurate forecasting of electricity demand is crucial for improving transmission system operation through optimized use of resources, operation planning, and minimized outages. The dynamic of electricity demand depends on exogenous factors (e.g., meteorological conditions), but the relationships bet...

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Autores principales: Uhrig, Mariela N., Vignolo, Leandro D., Müller, Omar V.
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2024
Materias:
Ciencias Informáticas
electricity demand forecast
weather conditions
deep learning
artificial neural network
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/177174
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-177174
record_format dspace
spelling I19-R120-10915-1771742025-03-07T20:07:01Z http://sedici.unlp.edu.ar/handle/10915/177174 Electricity demand forecast model based on meteorological and historical demand data using artificial neural networks Uhrig, Mariela N. Vignolo, Leandro D. Müller, Omar V. 2024-08 2024 2025-03-07T16:36:53Z en Ciencias Informáticas electricity demand forecast weather conditions deep learning artificial neural network Accurate forecasting of electricity demand is crucial for improving transmission system operation through optimized use of resources, operation planning, and minimized outages. The dynamic of electricity demand depends on exogenous factors (e.g., meteorological conditions), but the relationships between demand and factors are complex and nonlinear, posing a challenge for accurate prediction. With the aim of predicting electricity demand, this work explores the relationship with meteorological conditions for the province of Entre Ríos (Argentina). We propose a recurrent neural network model based on long short-term memories, which receives the raw input data without feature extraction. We evaluate its performance and compare it with a state-of-the-art method. The exploratory analysis of the data shows that temperature extremes present a strong influence on consumption patterns. The proposed models achieve a performance of 0.77 in determination coefficient when comparing predicted electricity demand with observations. This indicates the potential as a powerful tool for optimizing the system operation in Entre Ríos. Sociedad Argentina de Informática e Investigación Operativa Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 106-118
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
electricity demand forecast
weather conditions
deep learning
artificial neural network
spellingShingle Ciencias Informáticas
electricity demand forecast
weather conditions
deep learning
artificial neural network
Uhrig, Mariela N.
Vignolo, Leandro D.
Müller, Omar V.
Electricity demand forecast model based on meteorological and historical demand data using artificial neural networks
topic_facet Ciencias Informáticas
electricity demand forecast
weather conditions
deep learning
artificial neural network
description Accurate forecasting of electricity demand is crucial for improving transmission system operation through optimized use of resources, operation planning, and minimized outages. The dynamic of electricity demand depends on exogenous factors (e.g., meteorological conditions), but the relationships between demand and factors are complex and nonlinear, posing a challenge for accurate prediction. With the aim of predicting electricity demand, this work explores the relationship with meteorological conditions for the province of Entre Ríos (Argentina). We propose a recurrent neural network model based on long short-term memories, which receives the raw input data without feature extraction. We evaluate its performance and compare it with a state-of-the-art method. The exploratory analysis of the data shows that temperature extremes present a strong influence on consumption patterns. The proposed models achieve a performance of 0.77 in determination coefficient when comparing predicted electricity demand with observations. This indicates the potential as a powerful tool for optimizing the system operation in Entre Ríos.
format Objeto de conferencia
Objeto de conferencia
author Uhrig, Mariela N.
Vignolo, Leandro D.
Müller, Omar V.
author_facet Uhrig, Mariela N.
Vignolo, Leandro D.
Müller, Omar V.
author_sort Uhrig, Mariela N.
title Electricity demand forecast model based on meteorological and historical demand data using artificial neural networks
title_short Electricity demand forecast model based on meteorological and historical demand data using artificial neural networks
title_full Electricity demand forecast model based on meteorological and historical demand data using artificial neural networks
title_fullStr Electricity demand forecast model based on meteorological and historical demand data using artificial neural networks
title_full_unstemmed Electricity demand forecast model based on meteorological and historical demand data using artificial neural networks
title_sort electricity demand forecast model based on meteorological and historical demand data using artificial neural networks
publishDate 2024
url http://sedici.unlp.edu.ar/handle/10915/177174
work_keys_str_mv AT uhrigmarielan electricitydemandforecastmodelbasedonmeteorologicalandhistoricaldemanddatausingartificialneuralnetworks
AT vignololeandrod electricitydemandforecastmodelbasedonmeteorologicalandhistoricaldemanddatausingartificialneuralnetworks
AT mulleromarv electricitydemandforecastmodelbasedonmeteorologicalandhistoricaldemanddatausingartificialneuralnetworks
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