Intra-hour PV Power Forecasting Technique Based on Total-sky Images

Clouds are one of the leading causes of sun shading, which reduces the direct horizontal irradiance and curtails the photovoltaic (PV) power. It is critical to estimate cloud cover to accurately predict PV generation within a very short horizon (second/minute). To achieve the precise forecasting of cloud cover, an image preprocessing method based on total-sky images is proposed to remove the interference and address the image edge distortion issue. An optimal threshold estimation method is further designed to achieve higher cloud identification precision. Considering the cloud's meteorological properties, a random hypersurface model (RHM) based on the Gaussian mixture probability hypothesis density (GM-PHD) filter is applied to track the cloud. The GM-PHD can track the rotation and diffusion of clouds, which helps to estimate sun-cloud collision. Furthermore, a hybrid autoregressive integrated moving average (ARIMA) and backpropagation (BP) neural network-based model is applied for intra-hour PV power forecasting. The experiment results demonstrate that the proposed cloud-tracking-based PV power forecasting model can capture the ramp behavior of PV power, improving forecasting precision.