Permutation test and bootstrap methods for unsupervised detection and estimation of behind‐the‐meter photovoltaic generation

Liu, Chao; Shi, Jing; Chen, Hongkun; Chen, Lei; Li, Guocheng

doi:10.1049/rpg2.12067

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…According to the IEA (International Renewable Energy Agency) report, global solar PV power generation increased by 22% to 720 TWh in 2019 [4]. However, the ability to forecast PV power remains a key technical challenge for the integration of large-scale PV into the electrical grid due to the variability and uncertainty of solar irradiance, which affects PV power most directly [5][6][7][8][9]. Moreover, accurate forecasting of solar irradiance is crucial to the dispatch and management of power systems [5].…”

Section: Introductionmentioning

confidence: 99%

The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images

Jiang

Gao

2020

Remote Sensing

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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.

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