Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting
By Xueheng Qiu, Ye Ren, Ponnuthurai Nagaratnam Suganthan, Gehan A. J. Amaratunga. Applied Soft Computing, 2017
421 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/19adf1880209a37e636947e900e67ba94cf90c65
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Henry Leung
- Ponnuthurai Nagaratnam Suganthan
- Saifur Rahman
- Manisa Pipattanasomporn
- Jun Zhao
- King Fai Ma
- Zhongyang Han
- Wei Wang
In Electrical and Electronic Engineering
- M. A. Ganaie
- Mohammad Tanveer
- Minghui Hu
- Henry Leung
- Ponnuthurai Nagaratnam Suganthan
- Saifur Rahman
- Manisa Pipattanasomporn
- Mengmeng Cai
Citations in the corpus
- A Survey on ensemble learning under the era of deep learning (2021, methodology): “DA2 Electricity load demand forecasting (Qiu et al. 2017) 229 2017”
- Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques (2019, methodology): “With the huge success DL techniques made in solving complex statistical problems, researchers also started searching for the DL-driven solutions for load forecasting applications [29,30].”
- A Review of Deep Learning Models for Time Series Prediction (2019, positive): “…predictive objects are not only chaotic time series [169], [170], but also ranging from traffic flow, energy to drought index, etc. [171]–[176] As for the structure of this method, some scholars have constructed ensemble DBNs which aggregate the outputs so as to obtain better accuracy [177], [178].”
- Ensemble deep learning: A review (2021, background): “2017 Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting [214] Supervised ensemble learning Load demand forecasting”