Abstract
This paper investigates the accuracy of weather research and forecasting by improving coding for solar radiation forecasting for location in Dili Timor Leste. Weather Research and Forecasting (WRF) model version 3.9.1 is used in this study for improvement purposes. The shortwave coding of WRF is used to improve in order to decrease error simulation. The importance of improving WRF coding at a specific region will reduce the bias and root mean square root when comparing to the observed data. This study uses high resolution based on the WRF modeling to stabilize the performance of forecasting. The decrease in error performance will be expected to enhance the value of renewable energy. The results show the root mean square error of the WRF default is 233 W/m2 higher compared to 205 W/m2 from the WRF improvement model. In addition, the Mean Bias Error (MBE) of the WRF default is obtained value 0.06 higher than 0.03 from the WRF improvement in rainy days. Meanwhile, on sunny days, the performance Root Mean Square Error (RMSE) of WRF default is 327 W/m2 higher than 223 W/m2 from the WRF improvement. The MBE of WRF improvement obtained 0.13 lower compared to 0.21 of WRF default coding. Finally, this study concludes that improving the shortwave code under the WRF model can decrease the error performance of the WRF simulation for local weather forecasting.
Highlights
IntroductionIn recent years the implementation of renewable energy has been increasing rapidly, increasing the distribution of renewable energy means increasing the reliable energy generation forecast for implementation in electricity grid connection
This paper investigates the accuracy of weather research and forecasting by improving coding for solar radiation forecasting for location in Dili Timor Leste
An improvement of Weather Research and Forecasting (WRF) coding for shortwave radiation for five days of simulation on sunny and rainy days was conducted in Dili, Timor Leste
Summary
In recent years the implementation of renewable energy has been increasing rapidly, increasing the distribution of renewable energy means increasing the reliable energy generation forecast for implementation in electricity grid connection. Sometimes large errors in solar irradiance forecasting may lead to the performance of reliable energy generation. A study of improvement of forecasting should be taken more carefully in order to perform better and a significant irradiance forecast. The complexity of local terrain, imperfect measured data, and uncertain physics of cloud formation may lead to the performance of forecasting
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