Evaluating parallelized support vector regression and nearest neighbor regression with different input variations for estimating daily global solar radiation of the humid subtropical region in China

Indirectly estimating global solar radiation is strongly nonlinear and needs to be addressed by machine learning. Sequentially developing a machine learning model can be very time consuming. Moreover, whether and how the exogenous meteorological, geographical and temporal variables affect regression accuracy still has not been well understood. This paper evaluates parallelized support vector regression (SVR) and nearest neighbor regression (NNR) models for estimating daily global solar radiation of the humid subtropical region in China using existing Python libraries on a multi-core central processing unit (CPU) and a graphical processing unit (GPU). Seven input variations are studied. Two variations are commonly adopted in literature, four variations contain meteorological, geographical and/or temporal features with bounded Pearson correlation coefficients (PCCs), and the other variation simply include all the available features. Experimental results demonstrate that: SVR and NNR are equally powerful for nonlinear regression, and the variation comprising features with absolute PCCs no less than 0.3 (i.e. just all the meteorological features) is able to achieve most accurate estimation; the GPU-parallelized SVR model can accelerate parameter calibration and prediction; compared with the CPU-parallelized and GPU-parallelized SVR models, the GPU-parallelized NNR model is much more efficient and rather more scalable with the increment of the number of data samples; and the CPU-parallelized NNR model consumes quite less parameter calibration time than the GPU-parallelized NNR model, owing to different methods adopted for determining distances and significant time wasted by the GPU-parallelized NNR model on repeatedly calculating required information during cross-validation.