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

Abstract

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.

Benchmarking of solar irradiance nowcast performance derived from all-sky imagers

“…Similarly, Chu et al [57] used as inputs to ANN the time-lagged GHI, total sky cloud coverage and cloud indices. ANNs have been widely used to nowcast either GHI [58][59][60][61] or DNI [62]. Recently, Kamadinata et al [59] directly utilized the color information captured by an ASI providing adequately good nowcasts (up to 5 min) with less computational effort.…”

A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting

“…The physical methods are based on the principle of PV power generation and combine numerical weather prediction (NWP) data with the PV system's parameters to build a physical model to directly calculate the PV power prediction results. Its modeling relies on detailed system geographical information, system parameters., and accurate meteorological data, and the forecasting accuracy depends heavily on the accuracy of the NWP data, which is still limited [5], and few studies have applied them to PV power forecasting tasks for shorter time scales [6]. Statistical methods are used to establish and optimize the mapping relationship between the historical samples and actual PV power using curve fitting and parameter estimation with the objective of error minimization.…”

Advanced selection materials in solar cell efficiency and their properties - A comprehensive review

“…Recently, research into more efficient and effective research in photovoltaics has resulted in the existing of emerging solar cells and enhanced conventional efficiency baseline designed CIGS solar cells [3,4]. New chalcopyrite materials accomplished greater than 24% efficiency from expanding thin-film solar cells with efficiencies larger than 10% [5][6][7]. However, the electricity price is approximately one order of magnitude higher when related to commercial prices from current solar cells [8,9].…”