Predicting short-term electricity demand through artificial neural network
Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the fore...
- Autores:
-
Viloria, Amelec
García Guliany, Jesús
Varela Izquierdo, Noel
Pineda, Omar
Hernández Palma, Hugo
Valero, Lesbia
Marín-González, Freddy
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7752
- Acceso en línea:
- https://hdl.handle.net/11323/7752
https://doi.org/10.1007/978-981-15-2612-1_14
https://repositorio.cuc.edu.co/
- Palabra clave:
- Primary feeder
Demand short-term electricity prognosis
Neural networks
Forecast accuracy
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
| Summary: | Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the forecasting error, which makes this topic as an integral part of planning in many companies of various kinds and sizes, ranging from generation, transmission, and distribution to consumption, by requiring reliable forecasting systems. |
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