Modelado y predicción de la demanda eléctrica: comparación de enfoques estadísticos y de aprendizaje automático
Reliable forecasting of electricity demand is essential for enhancing distribution system management by optimizing resource utilization, streamlining operational planning, and reducing service disruptions. The fluctuation of electricity demand is influenced by various external factors, such as weat...
Guardado en:
| Autores principales: | , , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/177986 |
| Aporte de: |
| Sumario: | Reliable forecasting of electricity demand is essential for enhancing distribution system management by optimizing resource utilization, streamlining operational planning, and reducing service disruptions.
The fluctuation of electricity demand is influenced by various external factors, such as weather conditions, yet the intricate and non-linear associations between demand and these influences present significant prediction challenges.
In this study, we aim to forecast electricity demand by examining its relationship with weather variables in the province of Entre Ríos, Argentina.
A recurrent neural network, specifically using long short-term memory (LSTM) architecture, is employed to model this complex relationship directly from raw input data without prior feature engineering.
We assess and compare the performance of this model with a baseline method.
Preliminary data analysis reveals that extreme temperatures exert a notable effect on energy consumption behaviors. Our proposed model achieves a coefficient of determination of 0.77 when comparing predicted demand to actual observations, underscoring its effectiveness as a potential solution for optimizing system operations in Entre Ríos. |
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