Cargando…
Improved Neural Networks with Random Weights for Short-Term Load Forecasting
An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique...
Autores principales: | Lang, Kun, Zhang, Mingyuan, Yuan, Yongbo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4667993/ https://www.ncbi.nlm.nih.gov/pubmed/26629825 http://dx.doi.org/10.1371/journal.pone.0143175 |
Ejemplares similares
-
Forecasting short-term data center network traffic load with convolutional neural networks
por: Mozo, Alberto, et al.
Publicado: (2018) -
Recurrent neural networks for short-term load forecasting: an overview and comparative analysis
por: Bianchi, Filippo Maria, et al.
Publicado: (2017) -
Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks
por: Xie, Mei-Quan, et al.
Publicado: (2014) -
Research on Impulse Power Load Forecasting Based on Improved Recurrent Neural Networks
por: Feng, Chenyang, et al.
Publicado: (2022) -
Additive Ensemble Neural Network with Constrained Weighted Quantile Loss for Probabilistic Electric-Load Forecasting
por: Lopez-Martin, Manuel, et al.
Publicado: (2021)