Study of Photovoltaic Power Generation Output Predicting Model Based on Nonlinear Time Series

To solve the problem of the variance of the photovoltaic power when photovoltaic power station connect with the power grid, a photovoltaic power predicting model of photovoltaic power station based on double ANNs is proposed in the paper. Wind velocity and wind direction on photovoltaic power station are the key of photovoltaic power predicting, and other circumstance conditions such as temperature, humidity, atmospheric pressure, are also great influence on it. The observed values of these five circumstance conditions can be treated as a nonlinear time series and be analyzed by the nonlinear time series ANNs model. The photovoltaic power predicting model consists of double artificial neural networks. The first is consisted of five artificial neural networks which is used to prediction the circumstance conditions time series, the second is employed to prediction the power of photovoltaic power station use predicting value of the five conditions. A series of simulation show that the results of the predicting model is acceptable in engineering application.