A very short term solar power forecasting technology which uses ground-based visible wavelength imagery is presented. A sky camera system suitable for use as a solar power forecasting tool is described. Relevant imaging considerations are discussed, including the need for high dynamic range imaging of the daytime sky and an associated stray light assessment. To photogrammetrically calibrate this sky camera system, a general camera model applicable to a fixed focal length photo objective lens with significant radially symmetric distortion is developed, and an accurate calibration technique for a stationary, skyward pointing daytime camera using the sun's position is given. Remote sensing algorithms used in the solar forecasting process are detailed, including clear sky characterization, cloud detection, cloud velocity estimation, and cloud height estimation using stereography. A cloud stereo photogrammetry method which provides dense 3D cloud position is presented. Correspondence is automatically determined using intra-scanline dynamic programming applied to a normalized cross correlation matching metric; an ordering constraint is implicit in the approach used. Using the described remote sensing tools and methods, a complete solar power forecasting framework is detailed. The method is based on the estimation of cloud shadow position via ray tracing, and the forecast position of the cloud shadows relative to solar collectors. A ray tracing procedure that works with a planar mapping of cloud position is used to compute shadow position. Cloud transmissivity is characterized using past observations and applied to forecast cloud positions. The application of the procedure to two case studies: the UCSD DEMROES weather station network, and a 48MW solar photovoltaic power plant is presented. A comparison of the forecasting performance using a common Total Sky Imager is compared to the UCSD Sky Imager, where it is shown that the UCSD Sky Imager performs better overall.

Solar Energy Forecasting and Resource Assessment is a vital text for solar energy professionals, addressing a critical gap in the core literature of the field. As major barriers to solar energy implementation, such as materials cost and low conversion efficiency, continue to fall, issues of intermittency and reliability have come to the fore. Scrutiny from solar project developers and their financiers on the accuracy of long-term resource projections and grid operators' concerns about variable short-term power generation have made the field of solar forecasting and resource assessment pivotally important. This volume provides an authoritative voice on the topic, incorporating contributions from an internationally recognized group of top authors from both industry and academia, focused on providing information from underlying scientific fundamentals to practical applications and emphasizing the latest technological developments driving this discipline forward. - The only reference dedicated to forecasting and assessing solar resources enables a complete understanding of the state of the art from the world's most renowned experts. - Demonstrates how to derive reliable data on solar resource availability and variability at specific locations to support accurate prediction of solar plant performance and attendant financial analysis. - Provides cutting-edge information on recent advances in solar forecasting through monitoring, satellite and ground remote sensing, and numerical weather prediction.

Accurate solar radiation knowledge and its characterization on the Earth’s surface are of high interest in many aspects of environmental and engineering sciences. Modeling of solar irradiance from satellite imagery has become the most widely used method for retrieving solar irradiance information under total sky conditions, particularly in the solar energy community. Solar radiation modeling, forecasting, and characterization continue to be broad areas of study, research, and development in the scientific community. This Special Issue contains a small sample of the current activities in this field. Both the environmental and climatology community, as the solar energy world, share a great interest in improving modeling tools and capabilities for obtaining more reliable and accurate knowledge of solar irradiance components worldwide. The work presented in this Special Issue also remarks on the significant role that remote sensing technologies play in retrieving and forecasting solar radiation information.

Very short-term solar forecasts based on sky images of ground-based cameras introduce a new forecasting methodology for solar energy applications which covers forecast horizons up to 30 minutes. In this thesis, a newly developed image-based forecasting model for the usage in different applications is presented. The core components of the model are cloud detection, cloud motion tracking, cloud shadow projection and irradiance modelling. The model takes raw camera images, local solar irradiance measurements and cloud base height estimations as input data and provides estimations of near-future surface solar irradiance distribution. Large data sets comprising sky images, pyranometer measurements and in some cases cloud base height and PV power measurements are the basis for an in-depth analysis of its forecast performance. Model forecasts are compared with persistence forecasts to evaluate its skill. engl.

The Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University (CSU) proposes to continue its research partnership with the NREL in Golden, CO. The partnership resonates with the goals outlined in a Memorandum of Understanding between the Department of Energy and the Department of Commerce (with the National Oceanic and Atmospheric Administration (NOAA)) signed in 2011. This CRADA will sustain the research partnership and provide an avenue and resources for ongoing collaborations, including the maintenance of NREL-furnished computer and storage equipment at CSU/CIRA. There is no formal exchange of funds in this CRADA; support above and beyond the in-kind items outlined in this document will come on a best-effort basis and/or in association with formal sponsored-research enabled collaborations. The research conducted under this CRADA continues to work toward enabling and improving short-term solar energy forecasting as developed under previous CIRA/CSU and NREL collaborations, with specific focus on point-based (e.g., solar farm) forecasting on the 1-3 hour timeframe. Under the best-effort actions during the CRADA period, technical maintenance of CIRA's existing satellite-based solar forecasting algorithm ("CIRACast"; Miller et al., 2017) was continued to maintain operability with evolving datasets. These efforts benefit the public by maintaining national capability for point solar forecasting using geostationary observation platforms, and laying the groundwork for future technical improvements as opportunities arise.

Solar PV installation is growing fast in recent decades across the world but high variability of solar power hinders its further penetration to the energy market. This variability mainly comes from cloud coverage, water vapor content and aerosol loadings, and has the greatest effect in short-term solar power prediction. This high volatility nature of solar insolation makes it difficult to integrate PV output to electricity grid. A more accurate short-term solar power prediction helps to develop bidding strategies for real-time markets or to determine the need for operating reserves. This work aims to tackle this problem by employing comprehensive spectral radiative models to calculate longwave and shortwave radiation through the atmosphere, estimating cloud properties from remote sensing data with the atmospheric model and building convolutional neural network model to model and forecast solar radiation. First, a Line-by-Line (LBL) spectral radiative model is built to capture details of the highly wavenumber-dependent nature of the irradiance fluxes. Then the broadband empirical model serves as a benchmark to validate the LBL model. For longwave spectrum that is emitted and absorbed by gases, aerosols, clouds and the ground, a high-resolution two-flux model with a recursive scattering method is developed. For the shortwave (solar) part of the spectrum, which includes scattering from atmospheric constituents and the ground, 3D comprehensive Monte-Carlo simulations are used. Beyond the basic model, some corrections or calibrations are made. Comprehensive Monte Carlo simulations are used for correcting deviations on the atmospheric downwelling longwave (DLW) flux caused by isotropic scattering assumptions in high aerosol loading regimes.The [delta]-M approximation input-based scaling rule is validated for a wide range of aerosol loading values except for very high aerosol loading conditions. This proposed scaling rules minimize substantially the computational effort of calculating anisotropic downwelling radiation from diverse types of aerosols under these extreme conditions. Earth curvature effect (air mass correction) is also tested. Although for solar zenith angles larger than 75°, the attenuation of the direct solar beam is overestimated in a plane-parallel atmosphere comparing to in a real spherical atmosphere, for most solar rays, a plane-parallel atmosphere approximation is accurate enough for modeling. A Spectral Cloud Optical Property Estimation (SCOPE) method that integrates the high-resolution imagery from GOES-R satellite and a two-stream, spectrally-resolved longwave radiative model was proposed, for the estimation of cloud optical depth and cloud bottom height. An improved model SCOPE 2.0 is also proposed which considers multi-layer clouds, clouds with ice crystals and aerosol corrections. A shortwave Monte Carlo simulation is developed and used to validate the derived cloud optical properties. With this comprehensive cloud cover estimate model, a convolutional neural networks (CNN) model is developed to correlate global horizontal irradiance (GHI) to the satellite-derived cloud cover (a "now-cast"). The performance of SCOPE method as well as CNN+SCOPE model is evaluated using one year (2018) of downwelling longwave (DLW) radiation and GHI measurements from the Surface Radiation Budget Network, which consists of seven sites spread across climatically diverse regions of the contiguous United States. CNN+SCOPE model achieves test-set root-mean-square error (RMSE) of 30.5 - 62.6 W[subscript m]−2 with an average of 47.2 W[subscript m]−2, which is better then the National Solar Radiation Database (NSRDB) model (average RMSE is 66.9 W[subscript m]−2). A reference CNN model is also tested which directly use satellite ABI data that the SCOPE model uses with an average error equal to 69.4 W[subscript m]−2. This success at CNN+SCOPE "now-cast" model points to possible future uses for short-term forecast.

Like wind power and most other renewable energy sources besides reservoir hydroelectricity, solar photovoltaic (PV) power generation has the disadvantage of unintended variability. Residential PV generators in electric distribution systems pose a unique challenge to voltage control and general maintenance of stable operation within specific tolerances, because of the large number of distributed generators, combined with limited visibility and control. Short-term forecasting of solar irradiance for the predictive benefit of PV operators and other concerned parties is an active and developing field. However, short-term forecasting of the effects of fair-weather cumulus clouds is immature, and there has been little or no modeling of the spatiotemporal evolution of such clouds for use in forecasting. This dissertation describes research work performed toward this goal, including three key contributions to the forecasting art for PV production. The primary contribution is a method to produce computationally cost-effective reduced-order dynamical models of cumulus cloud evolution and forecasts therefrom that can potentially be performed within the computing platform of PV site's energy management system or inverter. Specifically, the method is a novel application of dynamic mode decomposition to clear-sky index forecasting of shadowing effects of convective fair-weather cumulus clouds. In the method, cloud dynamics are captured by sequences of visible-light photographic video frames. The method can be more easily applied to the modeling of cloud evolution than traditional fluid-based methods, and has decreased forecasting error compared to existing frozen-cloud advection methods. Its use is demonstrated for several actual fair-weather cumulus cloud image sequences and compared to advection-only forecasts. Second, a novel method for mapping color sky images of convective fair-weather cumuli to a scalar irradiance metric is presented. This method exploits the special structure of sky images in three-dimensional red-green-blue cartesian color space. The proposed metric is shown, by comparison with experimentally measured irradiance time series, to produce a more accurate clear-sky index in comparison with other methods. Third, a method for efficiently removing the bright glare of the solar disk from an entire image sequence in one operation, using proper orthogonal decomposition, is presented and its use is demonstrated. These contributions are presented in the greater context of irradiance forecasting as a fundamentally fluid-dynamical problem, and appendices are provided with detailed examinations into the current state of the art