Presentation

Improving the accuracy of solar irradiance forecasts based on Numerical Weather Prediction using variables from multiple vertical layers as machine learning inputs

12 Nov 2019
Description

This presentation investigates the value of using forecast variables from multiple vertical layers of NWP as machine learning inputs in improving the accuracy of solar irradiance forecasts. Moreover, the effects of postprocessing on the NWP models with different spatio-temporal resolution – Global Forecast System (GFS), regional (ACCESS-R) and city (ACCESS-C) scale mesoscale models of the Australian Community Climate and Earth-System Simulator (ACCESS) model, are studied across different climatic locations in Australia. Functional Analysis of Variance (FANOVA) shows that the importance of NWP variables varies greatly across different climatic locations. More importantly, it is shown that in addition to the variables from surface level fields, including NWP variables from vertical layers as machine learning inputs provides further improved accuracy of solar irradiance forecasts.Read the full presentation here:  https://bit.ly/2Pj65Jo.

Access Rights Type: 
Open
Language: 
English
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