Estimation of hourly global solar radiation using Multivariate Adaptive Regression Spline (MARS) – A case study of Hong Kong. (1st November 2019) - Li, Danny H.W.; Chen, Wenqiang; Li, Shuyang; Lou, Siwei
Abstract: Solar energy is the most popular resource for power generation among the various available renewable energy alternatives. Solar radiation data are important for solar systems and energy-efficient building designs. Due to the unavailability of measurement, solar radiation prediction models are required. Recently, machine learning techniques were successfully used for predicting solar radiation. However, previous works were mainly focusing on monthly average daily or daily solar radiation. In this study, models for predicting hourly global solar radiation on a horizontal surface were developed based on Multivariate Adaptive Regression Spline (MARS) method. Hourly meteorological data measured in 7 years were used for the study. Sensitivity analysis was conducted using MARS algorithm and the most important variables were selected as inputs of the proposed models. 16 MARS models with different combinations of input variables were proposed. Logistic regression and Artificial Neural Networks (ANN) methods were also used to develop models for comparative study. Finally, the proposed models were evaluated against measurements and compared with existing models. The results showed that the proposed MARS models have good performance in both prediction accuracy and interpretability. The proposed models could be used to estimate effectively the hourly solar radiation according to different combinations of measured variables. Highlights: Sensitivity analysis was conducted using MARS, five most important climate parameters were selected. 16 MARS models were proposed using various combinations of most important variables. Models using logistic regression and ANN methods were built for comparative study. The proposed MARS models have good performance in both prediction accuracy and interpretability.