Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market
Short-term load forecasting predetermines how power systems operate because electricity production needs to sustain demand at all times and costs. Most load forecasts for the non-residential consumers are empirically done either by a customer’s employee or supplier personnel based on experience and...
Guardado en:
Autores principales: | Stefan Ungureanu, Vasile Topa, Andrei Cristinel Cziker |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/4cfdffaf2ee441729ebd530adcbd8804 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Deep Learning for Short-Term Load Forecasting—Industrial Consumer Case Study
por: Stefan Ungureanu, et al.
Publicado: (2021) -
A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms
por: Tingting Hou, et al.
Publicado: (2021) -
Enhanced Short-Term Load Forecasting Using Artificial Neural Networks
por: Athanasios Ioannis Arvanitidis, et al.
Publicado: (2021) -
Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)
por: Ji-Won Cha, et al.
Publicado: (2021) -
Electrical Load Demand Forecasting Using Feed-Forward Neural Networks
por: Eduardo Machado, et al.
Publicado: (2021)