As an energy resource, solar energy provides a cleaner alternative to the conventional power generation systems and therefore solar energy has the potential to help achieve lower emissions standards as well to help provide domestic energy security. A major challenge, however, is the nondispatchability and variability of the solar resource which makes it necessary to develop forecasting methodologies in order to safely integrate with the electric grid. As dictated by current electricity markets, power generation is dispatched according to day-ahead unit commitment as well as 1-hour ahead and 15-minutes for load-following services. In order to integrate large penetration levels of solar energy into the current systems, forecasting at these time intervals are necessary. In this work, we develop and evaluate several solar irradiance forecast models for multiple-time horizons. The 1-day ahead forecasting models are based on forecasted elements from the National Weather System's (NWS) forecasting database (NDFD). The 1-hour ahead forecasting models are based on sky cover indices derived from ground measurements including solar and infrared radiometers as well as a sky imager and we also develop satellite-based models that utilize neural networks for time-series predictions. For very short-term forecasts of

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.

Solar forecasting solutions provide utility companies with predictions of power output from large-scale solar installations or from distributed solar generation with a time scale ranging from the next few minutes up to several days ahead. These predictions decrease the risk associated with bidding renewable electricity to the regional grid. Increasing solar photovoltaic efficiency and decreasing manufacturing costs have driven solar electricity generation to become the fastest growing form of renewable electricity production. Adding solar generation in large quantities to the aging electricity grids of the world poses a problem due to the variability and intermittency of solar irradiance. The current state-of-the-art in solar forecasting is focused on the hour-ahead and day-ahead time horizons using publicly available satellite imagery or numerical weather prediction models. Conventional intra-hour forecasting methods are based on sky imagery and basic image processing and computer vision techniques. This thesis discusses the architecture of an intra-hour forecasting tool and outlines the steps involved in taking a sky image and outputting a value of irradiance at specified intra-hour intervals. The thesis includes technical discussions on obstruction masking, geometric transformation, cloud-motion detection and ray tracing for irradiance estimation. The goal is to improve and enhance conventional techniques with innovative approaches to intra-hour solar forecasting. The forecasting tool provides predictions of irradiance and the associated uncertainty through the use of a novel irradiance estimation algorithm and a Monte Carlo simulation. The ray tracing procedure allows for multiple irradiance estimations to be made at spatially distributed points, providing a high-fidelity irradiance mapping of the area within the range of the sky imager. This map can be used to accurately estimate power output from large scale solar power plants or distributed solar generation sites.

Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. - Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place - Provides real-world examples of methods used to formulate forecasts - Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner

Accurate solar generation forecasting is critical for ensuring power system reliability, economics, and effectiveness and controlling the supply-demand balance. This research offers novel multi-branch spatio-temporal forecasting models to improve forecasting accuracy and minimize forecasting errors. The first step is to build temporal models employing advanced deep learning architectures, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and GRU with Attention (AttGRU). Next, spatio-temporal solar forecasting models are constructed. A novel multi-branch Attentive Gated Recurrent Residual network (ResAttGRU) consisting of multiple branches of residual networks (ResNet), GRU, and the attention mechanism is introduced. The proposed multi-branch ResAttGRU is capable of modeling data at various resolutions, extracting hierarchical features, and capturing short- and long-term dependencies. Moreover, this network also presents a strong multi-time-scale representative, while GRUs can exploit temporal information at less computational cost than the popular LSTM. The novelty of the developed architecture is in the utilization of multiple convolutional-based branches to learn multi-time-scale features jointly, accelerate the learning process, and reduce overfitting. This dissertation also compares the multi-branch ResAttGRU networks with state-of-the-art deep learning methods using 18 years of NSRDB data at 12 solar sites. The proposed multi-branch ResAttGRU requires 7.1% fewer parameters than multi-branch residual LSTM (ResLSTM) while achieving similar average RMSE, MAE, and R-squared values. Finally, to effectively model spatial correlation among neighboring solar sites as well as to alleviate performance degradation due to overfitting of conventional neural networks, a spatio-temporal framework comprised of concatenated multi-branch Residual network and Transformer (ResTrans) is developed. Numerical results indicate that the multi-branch ResTrans structure achieves the highest forecasting accuracy, with an average RMSE of 0.049 ( W/m^2 ), an average MAE of 0.031 (W/m^2 ), and a R^2 coefficient of 97%.

Solar radiation data is important for a wide range of applications, e.g. in engineering, agriculture, health sector, and in many fields of the natural sciences. A few examples showing the diversity of applications may include: architecture and building design, e.g. air conditioning and cooling systems; solar heating system design and use; solar power generation; evaporation and irrigation; calculation of water requirements for crops; monitoring plant growth and disease control; skin cancer research.