Short-term solar power prediction using a support vector machine

By Jianwu Zeng, Wei Qiao. 2013

345 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/3d7fecedc413b71adbcdc27bff835db4e5e589a6

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. F. J. Martínez-de-Pisón
2. Fernando Antoñanzas-Torres
3. Can Wan
4. Zhao Xu
5. Javier Antoñanzas
6. R. Escobar
7. Rubén Urraca
8. Yonghua Song

In Electrical and Electronic Engineering

1. Fuad Un-Noor
2. Can Wan
3. Zhao Xu
4. Yonghua Song
5. Jian Zhao
6. Zechun Hu
7. Jin Lin
8. Eklas Hossain

Citations in the corpus

• Review of photovoltaic power forecasting (2016, methodology): “…et al., 2012; Haque et al., 2013; Soubdhan et al., 2016; De Georgi et al., 2015; Almonacid et al., 2014; Rana et al., 2015; Bouzerdoum et al., 2013; Zeng and Qiao, 2013; Do et al., 2016; De Georgi et al., 2016) or from nearby PV plants (Berdugo et al., 2011; Lonij et al., 2013; Vaz et al., 2016;…”
• Photovoltaic and solar power forecasting for smart grid energy management (2015, background): “A least-square support vector machine (LS-SVM) based model is proposed for short-term solar power prediction [40].”
• Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, background): “different phases of renewable energy power system based SGs, creating a whole new prospect for research [119]–[121].”