Assessment of the high-resolution estimations of global and diffuse solar radiation using WRF-Solar

Compared with physical models,WRF-Solar,as an excellent numerical forecasting model,includes abundant novel cloud physical and dynamical processes,which enablesenable the high-frequency output of radiation components which are urgently needed by the solar energy industry.However,the popularisation of WRF-Solar in a wide range of applications,such as the estimation of diffuse radiation,suffers from unpredictable influences of cloud and aerosol optical property parameters.This study assessed the accuracy of the improved numerical weather prediction(WRF-Solar)model in simulating global and diffuse radiation.Aerosol optical properties at 550 nm,which were provided by a moderate resolution imaging spectroradiometer,were used as input to analyse the differences in accuracies obtained by the model with/without aerosol input.The sensitivity of WRF-Solar to aerosol and cloud optical properties and solar zenith angle(SZA)was analysed.The results show the superiority of WRF-Solar to WRF-Dudhia in terms of their root mean square error(RMSE)and mean absolute error(MAE).The coefficients of determination between WRF-Solar and WRF-Dudhia revealed no statistically significant difference,with values greater than 0.9 for the parent and nested domains.In addition,the relative RMSE(RRMSE%)reached 46.60%.The experiment on WRF-Solar and WRF-Dudhia revealed a negative bias for global radiation,but WRF-Solar attained a slightly lower RMSE and higher correlation coefficient than WRF-Dudhia.The WRF-Solar-simulated results on diffuse radiation under clear sky conditions were slightly poorer,with RMSE,RRMSE,mean percentage error and MAE of 181.93 W m^(−2),170.52%,93.04%and 138 W m^(−2),respectively.Based on Himawari-8 cloud data,statistical results on cloud optical thickness(COT)for cloudy days revealed that WRF-Solar overestimated diffuse radiation at COTs greater than 20.Moreover,when the aerosol optical depth was greater than or equal to 0.8,WRF-Solar also overestimated the diffuse radiation,with a mean difference of 58.57 W m^(−2).The errors of WRF-Solar simulations in global and diffuse radiation exhibited a significant dependence on the SZA.The dispersion degree of deviation increased gradually with the decrease in the SZA.Thus,WRF-Solar serves as an improved numerical tool that can provide high temporal and high-spatial-resolution solar radiation data for the prediction of photovoltaic power.Studies should explore the improvement of cumulus parameterisation schemes to enhance the accuracy of solar radiation component estimation and prediction under cloudy conditions.

CoolVox:Advanced 3D convolutional neural network models for predicting solar radiation on building facades

Data-driven models have become increasingly prominent in the building,architecture,and construction industries.One area ideally suited to exploit this powerful new technology is building performance simulation.Physics-based models have traditionally been used to estimate the energy flow,air movement,and heat balance of buildings.However,physics-based models require many assumptions,significant computational power,and a considerable amount of time to output predictions.Artificial neural networks(ANNs)with prefabricated or simulated data are likely to be a more feasible option for environmental analysis conducted by designers during the early design phase.Because ANNs require fewer inputs and shorter computation times and offer superior performance and potential for data augmentation,they have received increased attention for predicting the surface solar radiation on buildings.Furthermore,ANNs can provide innovative and quick design solutions,enabling designers to receive instantaneous feedback on the effects of a proposed change to a building's design.This research introduces deep learning methods as a means of simulating the annual radiation intensities and exposure level of buildings without the need for physics-based engines.We propose the CoolVox model to demonstrate the feasibility of using 3D convolutional neural networks to predict the surface radiation on building facades.The CoolVox model accurately predicted the radiation intensities of building facades under different boundary conditions and performed better than ARINet(with average mean square errors of 0.01 and 0.036,respectively)in predicting the radiation intensity both with(validation error=0.0165)and without(validation error=0.0066)the presence of boundary buildings.