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 with...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.展开更多
Surface irradiance measurements with high temporal resolution can be used to detect clear skies,which is a critical step for further study,such as aerosol and cloud radiative effects.Twenty-one clear-sky detection(CSD...Surface irradiance measurements with high temporal resolution can be used to detect clear skies,which is a critical step for further study,such as aerosol and cloud radiative effects.Twenty-one clear-sky detection(CSD)methods are assessed based on five years of 1-min surface irradiance data at Xianghe—a heavily polluted station on the North China Plain.Total-sky imager(TSI)discrimination results corrected by manual checks are used as the benchmark for the evaluation.The performance heavily relies on the criteria adopted by the CSD methods.Those with higher cloudy-sky detection accuracy rates produce lower clear-sky accuracy rates,and vice versa.A general tendency in common among all CSD methods is the detection accuracy deteriorates when aerosol loading increases.Nearly all criteria adopted in CSD methods are too strict to detect clear skies under polluted conditions,which is more severe if clear-sky irradiance is not properly estimated.The mean true positive rate(CSD method correctly detects clear sky)decreases from 45%for aerosol optical depth(AOD)≤0.2%to 6%for AOD>0.5.The results clearly indicate that CSD methods in a highly polluted region still need further improvements.展开更多
基金supported by National Natural Science Foundation of China(U1909201,62206062).
文摘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.
基金supported by the National Key R&D Program of China grant number 2017YFA0603504the Strategic Priority Research Program of the Chinese Academy of Sciences grant number XDA17010101the National Natural Science Foundation of Chinagrant number 41875183。
文摘Surface irradiance measurements with high temporal resolution can be used to detect clear skies,which is a critical step for further study,such as aerosol and cloud radiative effects.Twenty-one clear-sky detection(CSD)methods are assessed based on five years of 1-min surface irradiance data at Xianghe—a heavily polluted station on the North China Plain.Total-sky imager(TSI)discrimination results corrected by manual checks are used as the benchmark for the evaluation.The performance heavily relies on the criteria adopted by the CSD methods.Those with higher cloudy-sky detection accuracy rates produce lower clear-sky accuracy rates,and vice versa.A general tendency in common among all CSD methods is the detection accuracy deteriorates when aerosol loading increases.Nearly all criteria adopted in CSD methods are too strict to detect clear skies under polluted conditions,which is more severe if clear-sky irradiance is not properly estimated.The mean true positive rate(CSD method correctly detects clear sky)decreases from 45%for aerosol optical depth(AOD)≤0.2%to 6%for AOD>0.5.The results clearly indicate that CSD methods in a highly polluted region still need further improvements.
