Book description
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.
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Biography
- Chapter 1. Terms and Definitions
-
Chapter 2. Semi-Empirical Satellite Models
- 2.1 Satellites and Spectral Bands
- 2.2 Basic Principles
- 2.3 Clear-Sky Background
- 2.4 Cloud Attenuation: Cloud Index
- 2.5 Computing Global Irradiance
- 2.6 Computing Direct Normal Irradiance
- 2.7 Downscaling Solar Irradiance with High-Resolution Terrain Information
- 2.8 Sources of Uncertainty
- 2.9 Validation and Accuracy
- 2.10 Calibrating Satellite Bias using Ground Measurements
- 2.11 Future Advancements
- References
- Chapter 3. Physically Based Satellite Methods
- Chapter 4. Evaluation of Resource Risk in Solar-Project Financing
-
Chapter 5. Bankable Solar-Radiation Datasets
- 5.1 Introduction
- 5.2 Solar-Radiation Datasets: Characteristics, Strengths, and Weaknesses
- 5.3 Typical Meteorological Year (TMY) Data Files
- 5.4 Satellite-Derived Solar-Radiation Values
- 5.5 Irradiance Measurements and Uncertainties
- 5.6 Building a Bankable Dataset
- 5.7 Statistical Analysis of a Solar-Radiation Dataset for P50, P90, and P99 Evaluations
- 5.8 Status and Future
- References
- Chapter 6. Solar Resource Variability
- Chapter 7. Quantifying and Simulating Solar-Plant Variability Using Irradiance Data
- Chapter 8. Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation
- Chapter 9. Sky-Imaging Systems for Short-Term Forecasting
- Chapter 10. SolarAnywhere Forecasting
- Chapter 11. Satellite-Based Irradiance and Power Forecasting for the German Energy Market
-
Chapter 12. Forecasting Solar Irradiance with Numerical Weather Prediction Models
- 12.1 Introduction
- 12.2 Steps Required to Produce a NWP Forecast and Grid Resolution
- 12.3 Comparison of Model Configurations for Four Operational Models (ECMWF, NAM, GFS, RAP): Spatial and Temporal Coverage, Deep and Shallow Cumulus, Turbulent Transport, Cloud Fraction, Cloud Overlap, Stratiform Microphysics, Aerosol, Shortwave Radiative Transfer
- 12.4 Possible Sources of Error in Forecasted Irradiance
- 12.5 Present-Day Accuracy of Solar-Irradiance Forecasts
- 12.6 Conclusions
- References
- Chapter 13. Data Assimilation in Numerical Weather Prediction and Sample Applications
- Chapter 14. Case Studies of Solar Forecasting with the Weather Research and Forecasting Model at GL-Garrad Hassan
-
Chapter 15. Stochastic-Learning Methods
- 15.1 Introduction
- 15.2 Baseline Methods for Comparison
- 15.3 Genetic Algorithms
- 15.4 Qualitative Performance Assessment
- 15.5 Performance of Stochastic-Learning Methods with No Exogenous Variables
- 15.6 Sky-Imaging Data as Exogenous Variables for Solar Forecasts
- 15.7 Stochastic-Learning Using Exogenous Variables: The National Digital Forecasting Database
- 15.8 Conclusions
- References
- Color Plates
- Index
Product information
- Title: Solar Energy Forecasting and Resource Assessment
- Author(s):
- Release date: June 2013
- Publisher(s): Academic Press
- ISBN: 9780123977724
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