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Solar Energy Forecasting and Resource Assessment

  • Book

  • August 2013
  • Elsevier Science and Technology
  • ID: 2484167

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.

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Table of Contents

Preface

Biography

Chapter 1 Terms and Definitions

Chapter 2 Semi-Empirical Satellite Models

Chapter 3 Physically Based Satellite Methods

Chapter 4 Evaluation of Resource Risk in Solar-Project Financing

Chapter 5 Bankable Solar-Radiation Datasets

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 Solar Anywhere Forecasting

Chapter 11 Satellite-Based Irradiance and Power Forecasting for the German Energy Market

Chapter 12 Forecasting Solar Irradiance with NumericalWeather Prediction Models

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

Index

Authors

Jan Kleissl Associate Professor, Department of Mechanical & Aerospace Engineering; Co-Director, Center for Energy Research, UC San Diego, USA.