Solar Energy Forecasting and Resource Assessment - 1st Edition - ISBN: 9780123971777, 9780123977724

Solar Energy Forecasting and Resource Assessment

1st Edition

Authors: Jan Kleissl
eBook ISBN: 9780123977724
Hardcover ISBN: 9780123971777
Imprint: Academic Press
Published Date: 16th July 2013
Page Count: 462
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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.

Key Features

  • 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.


Scientists and engineers working within the power utility, renewable energy industry, or other related energy fields, as well as within the areas of atmospheric science and meteorology.  Solar energy professionals particularly, including research scientists, project developers, system operators, planners and engineers.

Table of Contents



Chapter 1. Terms and Definitions

1.1 Introduction

1.2 Overview of Solar-Power Conversion Technologies

1.3 Solar Power Versus Solar Irradiance

1.4 Direct, Diffuse, and Global Solar Radiation and Instrumentation

1.5 Atmospheric Properties Affecting Solar Irradiance


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


Chapter 3. Physically Based Satellite Methods

3.1 Introduction

3.2 Satellite Observing Systems

3.3 Cloud and Aerosol Detection and Property Characterization

3.4 Relating Properties to Surface-Irradiance Parameters

3.5 Example Processing and Datasets

3.6 Future Satellite Capabilities

3.7 Critical Needs for Research

3.8 Conclusions


Chapter 4. Evaluation of Resource Risk in Solar-Project Financing

4.1 Introduction

4.2 Perspectives on Resource Risk in Project Financing

4.3 Data Sources, Quality, and Uncertainty

4.4 Commercial Implications of Resource Variability

4.5 Techniques for Quantifying and Managing Resource Risk

4.6 Conclusions


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


Chapter 6. Solar Resource Variability

6.1 Introduction

6.2 Quantifying Solar-Resource Variability

6.3 The Dispersion-Smoothing Effect

6.4 The General Case of an Arbitrarily Dispersed Fleet of Solar Generators

6.5 Variability Impact on the Distribution and Transmission System

6.6 A Final Note on the Smoothing Effect


Chapter 7. Quantifying and Simulating Solar-Plant Variability Using Irradiance Data

7.1 Causes and Impacts of PV Variability

7.2 Variability Metrics

7.3 Wavelet Variability Model

7.4 WVM Validation and Application in Puerto Rico

7.5 Conclusions


Chapter 8. Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation

8.1 Classification of Solar-Forecasting Methods

8.2 Deterministic and Stochastic Forecasting Approaches

8.3 Metrics for Evaluation of Solar-Forecasting Models

8.4 Applying the THI Metric to Evaluate Persistence, and Nonlinear Autoregressive Forecast Models

8.5 Conclusions


Chapter 9. Sky-Imaging Systems for Short-Term Forecasting

9.1 Challenges in Short-Term Solar Forecasting

9.2 Applications

9.3 Sky-Imaging Hardware

9.4 Sky-Imagery Analysis Techniques

9.5 Case Study: Copper Mountain

9.6 Future Applications


Chapter 10. SolarAnywhere Forecasting

10.1 The SolarAnywhere Solar Resource and Forecast Data Service

10.2 Solaranywhere Forecast Models

10.3 Model Evaluation: Standard Resolution

10.4 Performance Evaluation: 1 km, 1 min Forecasts

Concluding Remarks


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

11.1 Solar Energy Penetration in Germany

11.2 Overview of the Satellite Forecast Process

11.3 Irradiance from Satellite Data

11.4 Cloud-Motion Vectors

11.5 Evaluation

11.6 Evaluation of CMV Forecasts

11.7 PV-Power Forecasting

11.8 Summary and Outlook


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


Chapter 13. Data Assimilation in Numerical Weather Prediction and Sample Applications

13.1 Introduction

13.2 DA Methods and Their Use

13.3 How does DA Work?

13.4 Solar-Energy DA Challenges

13.5 Future Trends

13.6 Conclusions


Chapter 14. Case Studies of Solar Forecasting with the Weather Research and Forecasting Model at GL-Garrad Hassan

14.1 Motivation: Forecasts of Irradiance, Variability, and Uncertainty

14.2 Solar Forecasting Using NWP at GL-Garrad Hassan

14.3 Case Studies on Meeting Stakeholder Needs

14.4 Summary and Conclusions

Acronyms, Symbols, and Variables


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


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About the Author

Jan Kleissl

Affiliations and Expertise

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