Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. Our previous study introduced a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposed the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle were accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL). A cloud retrieval technique was used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction were predicted for future time steps using a two-stream approximation and a 5-minute exponential weighted moving average, respectively. The model evaluation indicated the estimation and forecast of cloud fraction mostly contributed to the uncertainty of the PSPI though it overcame the persistence and smart persistence models in all forecast time horizons between 5 and 60 minutes. This study aims to enhance the PSPI by ingesting surface-based observations of cloud fraction from a total sky imager (TSI). The estimation and forecast of cloud albedo is correspondingly improved by utilizing the cloud fraction observations and thus leads to more accurate GHI forecast. Various time-series analysis methods are also investigated on the forecasting of cloud fraction and cloud albedo for further improving the GHI forecast. These improvements are valuable for many applications, such as forecasting energy use for buildings, grid operations, and ultimately bringing down the cost of solar energy.

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.

The total sky imager (TSI) provides time series of hemispheric sky images during daylight hours and retrievals of fractional sky cover for periods when the solar elevation is greater than 10 degrees.

As the nation's economic activities, security concerns, and stewardship of natural resources become increasingly complex and globally interrelated, they become ever more sensitive to adverse impacts from weather, climate, and other natural phenomena. For several decades, forecasts with lead times of a few days for weather and other environmental phenomena have yielded valuable information to improve decision-making across all sectors of society. Developing the capability to forecast environmental conditions and disruptive events several weeks and months in advance could dramatically increase the value and benefit of environmental predictions, saving lives, protecting property, increasing economic vitality, protecting the environment, and informing policy choices. Over the past decade, the ability to forecast weather and climate conditions on subseasonal to seasonal (S2S) timescales, i.e., two to fifty-two weeks in advance, has improved substantially. Although significant progress has been made, much work remains to make S2S predictions skillful enough, as well as optimally tailored and communicated, to enable widespread use. Next Generation Earth System Predictions presents a ten-year U.S. research agenda that increases the nation's S2S research and modeling capability, advances S2S forecasting, and aids in decision making at medium and extended lead times.

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

Solar photovoltaic (PV) capacity is rapidly growing across the world. However, the volatility of cloud movement introduces significant uncertainty in short-term solar PV output, which can complicate the operation of modern power systems. Cloudy days remain challenging for modern short-term solar forecasting algorithm. An improved short-term forecast benefits all participants of the sub-hourly power market. This work proposes a specialized convolutional neural network (CNN) "SUNSET" for short-term solar PV output forecasting. Its suitability is first tested on now-casting, i.e. inferring contemporaneous PV output from sky images. On a system with a rated capacity of 30.1 kW, the baseline SUNSET model achieved an RMSE of 1.01 kW on the sunny test set, 3.30 kW on the cloudy test set, and 2.40 kW overall. This validates the sky images' close correlation with PV panel outputs and that a CNN is suitable to extract this correlation. Extensive experiments are done to optimize the structure of SUNSET. In terms of depth, having three convolutional layers and one fully-connected layer produces the best result. Both types of neural nets are found to be crucial for model performance. In terms of width, 48 filters in the convolutional layers and 2048 neurons in the fully-connected layers provide the best performance. In terms of image resolution, 64 x 64 is the optimal point, as either finer or coarser resolution results in worse RMSE. Two further techniques are also found to be useful: drop-out increases the robustness for generalization while ensemble modeling decreases forecast error. For forecast, the SUNSET model is augmented in two key aspects, the usage of hybrid input and temporal history. PV output history is injected mid-way in the model to be joined with the processed image features. The temporal history of sky images are included by concatenating the images in the color channel. On a 1-year database, the "baseline'' model achieves a 15.7% forecast skill in all weather conditions, and a 16.3% forecast skill in the more demanding cloudy conditions, relative to a smart persistence forecast. Optimal input and output configurations for forecast are also explored. In terms of input, both sky images and PV output history are found to be crucial. Output-wise, training against PV output significantly out-performs training against clear sky indices (CSI). Careful down-sampling can reduce the training time by as much as 83% without affecting accuracy. For lag term configurations, using the same length of history as the forecast horizon is a good heuristic, while using slightly shorter history yields a modest 0.5% - 0.9% improvement. Last but not least, a two-stage optimization framework is proposed to quantify the value of short-term solar forecast. Design optimization in the first stage solves for resource capacity, while the receding horizon control (RHC) in the second stage simulates a power system's operation for a year. Within this framework, we consider a microgrid scenario which have battery as an option, and a demand charge scenario which allows grid import. In the settling process of the RHC stage, batteries can be utilized and redesigned to address forecast error in the microgrid scenario, while grid import is incurred in the demand charge scenario for the same purpose. For the microgrid scenario, a perfect forecast can reduce overall cost by 3.6% to 12.5% comparing to a persistence forecast. The largest cost savings are achieved with medium solar penetration of 30% to 60%. For the demand charge scenario, a perfect forecast can reduce total cost by 8.9% to 25.6%. For SUNSET with a forecast skill of 15.7%, we can expect a cost saving of 1-2% or 2-5% respectively in these two scenarios.