Enhanced support vector regression based forecast engine to predict solar power output-Reference-Cited by-同舟云学术

Enhanced support vector regression based forecast engine to predict solar power output

Published:2018-11 Issue: Volume:127 Page:269-283
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Shang Chuanfu,Wei Pengcheng

Funder

Chongqing Big Data Engineering Laboratory for Children

Chongqing Electronics Engineering Technology Research Center for Interactive Learning

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference34 articles.

1. Short-term photovoltaic solar power forecasting using a hybrid wavelet-PSO-SVM model based on SCADA and meteorological information;Eseye;Renew. Energy,2017

2. Day-ahead resource forecasting for concentrated solar power integration;Nonnenmacher;Renew. Energy,2016

3. Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest;Larson;Renew. Energy,2016

4. Energy management supporting high penetration of solar photovoltaic generation for smart grid using solar forecasts and pumped hydro storage system;Chaudhary;Renew. Energy,2017

5. Characterizing and analyzing ramping events in wind power, solar power, load, and netload;Cui;Renew. Energy,2017

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