Application of functional deep belief network for estimating daily global solar radiation: A case study in China. (15th January 2020) - Zang, Haixiang; Cheng, Lilin; Ding, Tao; Cheung, Kwok W.; Wang, Miaomiao; Wei, Zhinong; Sun, Guoqiang
Abstract: Solar energy plays an essential role in environment governance and resource protection, as it is totally pollution-free and extensively accessed. An accurate knowledge of solar radiation is beneficial to the deployments of solar energy constructions, photovoltaic and thermal solar systems. In this study, a deep learning method is proposed for estimating daily global solar radiation, which is constituted by embedding clustering (EC) and functional deep belief network (DBN). Based on the curve shapes of daily solar radiation, EC divides the overall dataset into different subsets, which can be modeled separately. Knowledge from empirical radiation models is also merged as the input of functional DBN. The model can be directly applied to solar estimation in various stations due to its strong nonlinear representation. The case study in China is adopted that involves radiation data from a total of 30 stations to validate the practicability and accuracy of the proposed method. From the results, the method obtains better estimation precision with empirical knowledge, achieving 1.706 MJ/m 2 of mean absolute error (MAE), 2.352 MJ/m 2 of root mean square error (RMSE) and 13.71% of mean absolute percentage error (MAPE) according to the average values at the 30 stations. Highlights: One sole deep model can be applied at multiple sites to estimate solar radiation. The pre-training of RBM reduces the optimization difficulty of deep networks. The knowledge from empirical equations is involved by means of functional neurons. The embedding clustering method provides initial cluster centers for k-means. The proposed method is feasible for both daily and hourly estimation.