Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/64105

Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast

File Size Format  
0292.pdf 1.23 MB Adobe PDF View/Open

Item Summary

Title:Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast
Authors:Alqudah, Mohammad
Djokic, Tatjana
Kezunovic, Mladen
Obradovic, Zoran
Keywords:Distributed, Renewable, and Mobile Resources
forecast
hour-ahead
solar generation
spatiotemporal
Date Issued:07 Jan 2020
Abstract:A novel method is proposed for real-time solar generation forecast using weather data, while exploiting both spatial and temporal structural dependencies. The network observed over time is projected to a lower-dimensional representation where a variety of weather measurements are used to train a structured regression model while weather forecast is used at the inference stage. Experiments were conducted at 288 locations in the San Antonio, TX area on obtained from the National Solar Radiation Database. The model predicts solar irradiance with a good accuracy (R2 0.91 for the summer, 0.85 for the winter, and 0.89 for the global model). The best accuracy was obtained by the Random Forest Regressor. Multiple additional experiments were conducted to characterize influence of missing data and different time horizons providing evidence that the new algorithm is robust for data missing not only completely at random but also when the mechanism is spatial, and temporal.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/64105
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.363
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Integrating Distributed or Renewable Resources


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons