Forecasting solar still performance from conventional weather data variation by machine learning method
Wenjie Gao, Leshan Shen, Senshan Sun, Guilong Peng, Zhen Shen, Yunpeng Wang, A.W. Kandeal, Zhouyang Luo, A.E. Kabeel, Jianqun Zhang, Hua Bao, Nuo Yang
Abstract
Solar stills are considered an effective method to solve the scarcity of drinkable water. However, it is still missing a way to forecast its production. Herein, it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data. The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm. The required data to train the model are obtained from daily measurements lasting 9 months. To validate the accuracy model, the determination coefficients of two types of solar stills are calculated as 0.935 and 0.929, respectively, which are much higher than the value of both multiple linear regression (0.767) and the traditional models (0.829 and 0.847). Moreover, by applying the model, we predicted the freshwater production of four cities in China. The predicted production is approved to be reliable by a high value of correlation (0.868) between the predicted production and the solar insolation. With the help of the forecasting model, it would greatly promote the global application of solar stills.