Accurate photovoltaic power probability forecasting assists dispatch departments in making rational decisions. Joint probability distributions constructed using Copula functions can flexibly characterize complex nonlinear correlations and tail dependencies among random variables. However, existing research has not thoroughly explored the multivariate dynamic coupling characteristics related to forecasting errors, nor has it sufficiently considered the complementary advantages among different Copula functions. To address this, we propose a method for forecasting photovoltaic power output probabilities days in advance, integrating multivariate dynamic Copula functions with reinforcement learning. First, to capture the time-varying structure of photovoltaic power-related variables, we introduce a sliding time window for segmented modeling of historical data, fitting marginal probability distributions for predicted irradiance, forecasting power, and forecasting error. Second, a joint probability distribution of dynamic Gaussian Copula and t-Copula is constructed based on historical samples within the time window, generating a probabilistic prediction interval for the target time. Finally, reinforcement learning is employed to adaptively combine the probability prediction intervals derived from both Copula types, yielding the final photovoltaic power probability forecast. Simulations using actual operational data from a photovoltaic power plant in Shanxi Province validate the effectiveness of the proposed method.
Zhao et al. (Thu,) studied this question.