Combination of WRF Model and LSTM Network for Solar Radiation Forecasting—Timor Leste Case Study  

作　　者：Jose Manuel Soares de Araujo Jose Manuel Soares de Araujo(Electrical and Electronic Engineering Division, Graduate School of Natural Science and Technology, Gifu University, Gifu, Japan)

机构地区：[1]Electrical and Electronic Engineering Division, Graduate School of Natural Science and Technology, Gifu University, Gifu, Japan

出　　处：《Computational Water, Energy, and Environmental Engineering》2020年第4期108-144,共37页水能与环境工程（英文）

摘　　要：A study of a combination of Weather Research and Forecasting (WRF) model and Long Short Term Memory (LSTM) network for location in Dili Timor Leste is introduced in this paper. One calendar year’s results of solar radiation from January to December 2014 are used as input data to estimate future forecasting of solar radiation using the LSTM network for three months period. The WRF model version 3.9.1 is used to simulate one year’s solar radiation in horizontal resolution low scale for nesting domain 1</span><span style="font-family:""> </span><span style="font-family:Verdana;">×</span><span style="font-family:""> </span><span style="font-family:Verdana;">1 km. It is done by applying 6-hourly interval 1</span><span style="font-family:Verdana;">&ordm;</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">×</span><span style="font-family:Verdana;"> 1</span></span><span style="font-family:Verdana;">&ordm;</span><span style="font-family:""><span style="font-family:Verdana;"> NCEP FNL analysis data used as Global Forecast System (GFS). LSTM network is applied for forecasting in numerous learning problems for solar radiation forecasting. LSTM network uses two-layer LSTM architecture of 512 hidden neurons coupled with a dense output layer with linear as the model activation to predict with time steps are configured to 50 and the number of features is 1. The maximum epoch is set to 325 with batch size 300 and the validation split is 0.09. The results demonstrate that the combination of these two methods can successfully predict solar radiation where four error metrics of mean bias error (MBE), root mean square error (RMSE), normalized MBE (nMBE), and normalized RMSE (nRMSE) perform small error distribution and percentage in three months prediction where the error percentage is obtained below the 20% for nMBE and nRMSE. Meanwhile, the error distribution of RMSE is obtained below 200 W/m</span>^{<span style="font-family:Verdana;">2</span>}<span style="font-famiA study of a combination of Weather Research and Forecasting (WRF) model and Long Short Term Memory (LSTM) network for location in Dili Timor Leste is introduced in this paper. One calendar year’s results of solar radiation from January to December 2014 are used as input data to estimate future forecasting of solar radiation using the LSTM network for three months period. The WRF model version 3.9.1 is used to simulate one year’s solar radiation in horizontal resolution low scale for nesting domain 1</span><span style="font-family:""> </span><span style="font-family:Verdana;">×</span><span style="font-family:""> </span><span style="font-family:Verdana;">1 km. It is done by applying 6-hourly interval 1</span><span style="font-family:Verdana;">&ordm;</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">×</span><span style="font-family:Verdana;"> 1</span></span><span style="font-family:Verdana;">&ordm;</span><span style="font-family:""><span style="font-family:Verdana;"> NCEP FNL analysis data used as Global Forecast System (GFS). LSTM network is applied for forecasting in numerous learning problems for solar radiation forecasting. LSTM network uses two-layer LSTM architecture of 512 hidden neurons coupled with a dense output layer with linear as the model activation to predict with time steps are configured to 50 and the number of features is 1. The maximum epoch is set to 325 with batch size 300 and the validation split is 0.09. The results demonstrate that the combination of these two methods can successfully predict solar radiation where four error metrics of mean bias error (MBE), root mean square error (RMSE), normalized MBE (nMBE), and normalized RMSE (nRMSE) perform small error distribution and percentage in three months prediction where the error percentage is obtained below the 20% for nMBE and nRMSE. Meanwhile, the error distribution of RMSE is obtained below 200 W/m</span>^{<span style="font-family:Verdana;">2</span>}<span style="font-fami

关 键 词：COMBINATION LSTM Solar Radiation WRF Timor Leste 

分 类 号：O17[理学—数学]