The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images
Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/21/3671 |
| _version_ | 1797548389042225152 |
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| author | Junxia Jiang Qingquan Lv Xiaoqing Gao |
| author_facet | Junxia Jiang Qingquan Lv Xiaoqing Gao |
| author_sort | Junxia Jiang |
| collection | DOAJ |
| description | Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time. |
| first_indexed | 2024-03-10T14:58:40Z |
| format | Article |
| id | doaj.art-3f8f41388ff74b56913725a759970199 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:58:40Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3f8f41388ff74b56913725a7599701992023-11-20T20:19:17ZengMDPI AGRemote Sensing2072-42922020-11-011221367110.3390/rs12213671The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky ImagesJunxia Jiang0Qingquan Lv1Xiaoqing Gao2Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaKey Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSolar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time.https://www.mdpi.com/2072-4292/12/21/3671solar radiationsolar energyirradiationforecastingimage retrievalTotal Sky Imager |
| spellingShingle | Junxia Jiang Qingquan Lv Xiaoqing Gao The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images Remote Sensing solar radiation solar energy irradiation forecasting image retrieval Total Sky Imager |
| title | The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images |
| title_full | The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images |
| title_fullStr | The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images |
| title_full_unstemmed | The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images |
| title_short | The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images |
| title_sort | ultra short term forecasting of global horizonal irradiance based on total sky images |
| topic | solar radiation solar energy irradiation forecasting image retrieval Total Sky Imager |
| url | https://www.mdpi.com/2072-4292/12/21/3671 |
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