研究目的
To develop a novel forecast method for short-term solar irradiance forecasting that addresses the difficulty arising due to rapidly evolving environmental factors over short time periods.
研究成果
The study demonstrates that combining prediction sky images with a Radiative Transfer Model (RTM) can quantitatively forecast DNI and DHI for time horizons shorter than 10 min. The RMSE and MAE errors of the presented forecast model were less than those of the persistence model, validating the use of prediction images combined with the RTM for short-term forecasting horizons.
研究不足
1. The proposed method may generate large errors in the presence of optically thin cloud layers due to their blue-tinting effect.
2. All clouds in images containing the same optical depth values as in the RTM may cause a certain number of DHI errors.
3. The wide shadowband of TSI and the distance between TSI and MFRSR could cause prediction errors.
1：Experimental Design and Method Selection:
The study combines prediction sky images with a Radiative Transfer Model (RTM) for forecasting Global Horizontal Irradiance (GHI). The prediction images are produced by a non-local optical flow method to calculate cloud motion for each pixel.
2：Sample Selection and Data Sources:
The study uses data from the Total Sky Imager (TSI) and Multifilter Rotating Shadowband Radiometer (MFRSR) located at the Southern Great Plains (SGP) atmospheric observatory in Oklahoma, United States.
3：List of Experimental Equipment and Materials:
Equipment includes TSI and MFRSR for capturing sky images and measuring solar irradiance, respectively.
4：Experimental Procedures and Operational Workflow:
The method involves processing TSI images to detect clouds, computing velocity fields by the optical flow method, simulating solar irradiance with RTM, and forecasting solar irradiance using prediction images combined with RTM outputs.
5：Data Analysis Methods:
The solar forecasts are evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE) compared to in-situ measurements and the persistence model.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
