Real-time prediction intervals for intra-hour DNI forecasts

We develop a hybrid, real-time solar forecasting computational model to construct prediction intervals (PIs) of one-minute averaged direct normal irradiance for four intra-hour forecasting horizons: five, ten, fifteen, and 20 min. This hybrid model, which integrates sky imaging techniques, support v...

Full description

Saved in:
Bibliographic Details
Published in:Renewable energy 2015-11, Vol.83, p.234-244
Main Authors: Chu, Yinghao, Li, Mengying, Pedro, Hugo T.C., Coimbra, Carlos F.M.
Format: Article
Language:English
Subjects:
Artificial neural networks
Prediction intervals
Sky imaging
Solar forecasting
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We develop a hybrid, real-time solar forecasting computational model to construct prediction intervals (PIs) of one-minute averaged direct normal irradiance for four intra-hour forecasting horizons: five, ten, fifteen, and 20 min. This hybrid model, which integrates sky imaging techniques, support vector machine and artificial neural network sub-models, is developed using one year of co-located, high-quality irradiance and sky image recording in Folsom, California. We validate the proposed model using six-month of measured irradiance and sky image data, and apply it to construct operational PI forecasts in real-time at the same observatory. In the real-time scenario, the hybrid model significantly outperforms the reference persistence model and provides high performance PIs regardless of forecast horizon and weather condition. •We train a Hybrid model using lagged DNI and image data.•The Hybrid model first categorizes the variability of DNI.•The Hybrid model adaptively forecasts DNI Prediction Interval (PI) accordingly.•The Hybrid model significantly outperforms the reference model in real time.
ISSN:0960-1481
1879-0682
DOI:10.1016/j.renene.2015.04.022