Ensemble methods for wind and solar power forecasting—A state-of-the-art review

By Ye Ren, Ponnuthurai Nagaratnam Suganthan, N. Srikanth. 2015

404 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b2e13e8c9b7939d81bf32d75cbcf0f01a1e9a99b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ponnuthurai Nagaratnam Suganthan
2. Ye Ren
3. M. A. Ganaie
4. Mohammad Tanveer
5. Minghui Hu
6. Le Zhang
7. F. J. Martínez-de-Pisón
8. Fernando Antoñanzas-Torres

In Electrical and Electronic Engineering

1. Ponnuthurai Nagaratnam Suganthan
2. M. A. Ganaie
3. Mohammad Tanveer
4. Minghui Hu
5. Daniel Hsu
6. Ibomoiye Domor Mienye
7. Yanxia Sun
8. R. Paurobally

In Management Science and Operations Research

1. M. A. Ganaie
2. Ponnuthurai Nagaratnam Suganthan
3. Mohammad Tanveer
4. Minghui Hu
5. Le Zhang
6. Ye Ren
7. Daniel Hsu

Citations in the corpus

• Review of photovoltaic power forecasting (2016, background): “Other more recent reviews are only focused on a specific aspect of forecasting, such as ensemble forecasting (Ren et al., 2015) or different forecasting techniques (Wan et al.”
• Weather Forecasting Error in Solar Energy Forecasting (2017, background): “In [9], authors provided a survey on using ensemble methods for wind speed/power”
• Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks (2016, methodology): “An overview of various ensemble techniques is shown in [25], a hybrid ensemble technique based on the gradual weighting of weather situations is described in [26].”
• Ensemble deep learning: A review (2021, background): “There exist multiple surveys in the literature which mainly focus on the review of ensemble learning like learning of ensemble models in classification problems [16, 17, 18, 19], regression problems [20, 21] and clustering [22].”
• Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, background): “sion methods [9], [10] in the literature.”