The power prediction for photovoltaic (PV) power plants has significant importance for their grid connection. Due to PV power’s periodicity and non-stationary characteristics, traditional power prediction methods based on linear or time series models are no longer applicable. This paper presents a method combining the advantages of the wavelet decomposition (WD) and artificial neural network (ANN) to solve this problem. With the ability of ANN to address nonlinear relationships, theoretical solar irradiance and meteorological variables are chosen as the input of the hybrid model based on WD and ANN. The output power of the PV plant is decomposed using WD to separated useful information from disturbances. The ANNs are used to build the models of the decomposed PV output power. Finally, the outputs of the ANN models are reconstructed into the forecasted PV plant power. The presented method is compared with the traditional forecasting method based on ANN. The results shows that the method described in this paper needs less calculation time and has better forecasting precision.

Herein, we employ a simple star-shaped hole-transporting material (HTM) consisting of a carbazole core and triphenylamine side arms, termed LD29, in mesoscopic perovskite solar cells (PSCs). LD29 exhibits a deep HOMO level of −5.24 eV, a high hole mobility of 1.72 × 10⁻⁵ cm² V⁻¹ s⁻¹, and a relatively high glass-transition temperature of 121 °C, as well as good film-forming ability. PSCs using dopant-free LD29 as the HTM layer exhibit a maximum efficiency of 14.29%. Promisingly, when LD29 is doped, a champion efficiency over 18% has been achieved, which is even comparable with that of the conventional doped spiro-OMeTAD. Moreover, the laboratory synthesis cost for LD29 is significantly lower than that for spiro-OMeTAD. Importantly, the PSC performance does not change greatly with the concentration of LD29 regardless of whether the dopants are used, which has the potential to further reduce material usage. The device employing LD29 also shows comparable stability to that employing spiro-OMeTAD. Consequently, our results provide an important step forward for the commercialized application of PSCs.