Photovoltaic (PV) output prediction can provide an important basis for the economical and safe operation of power system. The traditional prediction methods mostly belong to deterministic point predictions

the results of which generally have different degrees of error. The probability interval prediction method is gradually adopted because of its ability to effectively describe the uncertainty of PV output. For the interval prediction problem of ultra-short-term PV output

a prediction model based on particle swarm optimization (PSO) and lower upper bound estimation (LUBE) is proposed for PV output interval prediction. The upper and lower limits of interval prediction could be optimized quickly and directly by using PSO-LUBE

thus the problems of the large computational complexity and the assumption of data distribution in the traditional interval prediction scheme are solved. Case studies based on real PV power station data from the University of Queensland are conducted

the interval prediction performance of the model under different confidence levels is evaluated and compared with the traditional point prediction scheme. The results show that the proposed model can generate high-quality ultra-short-term PV output interval prediction

which can provide better decision support for safe and stable operation of grid-connected PV.