Estimating solar irradiance using sky imagers

Abstract

Ground-based whole sky cameras are extensively used for localized monitoring of clouds nowadays. They capture hemispherical images of the sky at regular intervals using a fisheye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards short-term forecasting of solar energy generation based on cloud movement. We derive and validate our model using pyranometers co-located with our whole sky imagers. We achieve a better performance in estimating solar irradiance and in particular its short-term variations as compared to other related methods using ground-based observations. IntroductionClouds have a significant impact on solar energy generation. They intermittently block the sun and significantly reduce the solar irradiance reaching solar panels. A short-term forecast of solar irradiance is needed for grid operators to mitigate the effects of a drop in power generation. With rapid developments in photogrammetric techniques, ground-based sky cameras are now widely used (Dev et al., 2016e). These cameras, known as Whole Sky Imagers (WSIs) are upward looking devices that capture images of the sky at regular intervals of time. These images are subsequently used for automatic cloud coverage computation, cloud tracking, and cloud base height estimation. In our research group, we use these imagers to study the effects of clouds on satellite communication links (Dev et al., 2018b;Yuan et al., 2016;Dev et al., 2017e).Localized and short-term forecasting of cloud movements is an on-going research topic (Shakya et al., 2017;Jiang et al., 2017;Feng et al., 2018). Optical flow techniques can be used to forecast images using anterior frames (Dev et al., 2016d).Similarly, cloud motion vectors are exploited for solar power prediction from satellite images (Jang et al., 2016). Our proposed method for estimating solar irradiance is thus a first step towards solar irradiance forecasting, as the input data used to estimate the irradiance is the same as the one used to forecast the sky condition.The accurate estimation and prediction of solar energy generation is a challenging task, as clouds greatly impact the total irradiance received on the earth's surface. In the event of clouds covering the sun for a short time, there is a sharp decline of the 1 arXiv:1910.04981v1 [eess.IV]

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