Solar Energy Forecasting and Resource Assessment

Solar Energy Forecasting and Resource Assessment

1st Edition - June 25, 2013

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  • Author: Jan Kleissl
  • Hardcover ISBN: 9780123971777
  • eBook ISBN: 9780123977724

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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

  • Preface


    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


    Color Plates


Product details

  • No. of pages: 462
  • Language: English
  • Copyright: © Academic Press 2013
  • Published: June 25, 2013
  • Imprint: Academic Press
  • Hardcover ISBN: 9780123971777
  • eBook ISBN: 9780123977724

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

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  • Jakub J. Thu Sep 10 2020

    Solar Energy Forecasting and Resource Assessment

    Great book. Very interesting to read and helpful. Good examples and top contributors from the field. Certainly, a followup in the future would be needed.

  • Robert L. Tue Mar 13 2018

    Solar Energy Forecasting and Resource Assessment

    Very helpful in understanding the basic and advanced nuances of solar energy forecasting.