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Solar Energy Forecasting and Resource Assessment

Name: Solar Energy Forecasting and Resource Assessment
Author: Jan Kleissl
ISBN: 9780123977724

by Jan Kleissl

June 2013

Intermediate to advanced

462 pages

14h 15m

English

Academic Press

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Cover image
Title page
Table of Contents
Copyright
Preface
Biography
Chapter 1. Terms and Definitions
1.1 Introduction1.2 Overview of Solar-Power Conversion Technologies1.3 Solar Power Versus Solar Irradiance1.4 Direct, Diffuse, and Global Solar Radiation and Instrumentation1.5 Atmospheric Properties Affecting Solar IrradianceReferences
Chapter 2. Semi-Empirical Satellite Models
2.1 Satellites and Spectral Bands2.2 Basic Principles2.3 Clear-Sky Background2.4 Cloud Attenuation: Cloud Index2.5 Computing Global Irradiance2.6 Computing Direct Normal Irradiance2.7 Downscaling Solar Irradiance with High-Resolution Terrain Information2.8 Sources of Uncertainty2.9 Validation and Accuracy2.10 Calibrating Satellite Bias using Ground Measurements2.11 Future AdvancementsReferences
Chapter 3. Physically Based Satellite Methods
3.1 Introduction3.2 Satellite Observing Systems3.3 Cloud and Aerosol Detection and Property Characterization3.4 Relating Properties to Surface-Irradiance Parameters3.5 Example Processing and Datasets3.6 Future Satellite Capabilities3.7 Critical Needs for Research3.8 ConclusionsReferences
Chapter 4. Evaluation of Resource Risk in Solar-Project Financing
4.1 Introduction4.2 Perspectives on Resource Risk in Project Financing4.3 Data Sources, Quality, and Uncertainty4.4 Commercial Implications of Resource Variability4.5 Techniques for Quantifying and Managing Resource Risk4.6 ConclusionsReferences

Chapter 5. Bankable Solar-Radiation Datasets
5.1 Introduction5.2 Solar-Radiation Datasets: Characteristics, Strengths, and Weaknesses5.3 Typical Meteorological Year (TMY) Data Files5.4 Satellite-Derived Solar-Radiation Values5.5 Irradiance Measurements and Uncertainties5.6 Building a Bankable Dataset5.7 Statistical Analysis of a Solar-Radiation Dataset for P50, P90, and P99 Evaluations5.8 Status and FutureReferences
Chapter 6. Solar Resource Variability
6.1 Introduction6.2 Quantifying Solar-Resource Variability6.3 The Dispersion-Smoothing Effect6.4 The General Case of an Arbitrarily Dispersed Fleet of Solar Generators6.5 Variability Impact on the Distribution and Transmission System6.6 A Final Note on the Smoothing EffectReferences
Chapter 7. Quantifying and Simulating Solar-Plant Variability Using Irradiance Data
7.1 Causes and Impacts of PV Variability7.2 Variability Metrics7.3 Wavelet Variability Model7.4 WVM Validation and Application in Puerto Rico7.5 ConclusionsReferences
Chapter 8. Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation
8.1 Classification of Solar-Forecasting Methods8.2 Deterministic and Stochastic Forecasting Approaches8.3 Metrics for Evaluation of Solar-Forecasting Models8.4 Applying the THI Metric to Evaluate Persistence, and Nonlinear Autoregressive Forecast Models8.5 ConclusionsReferences
Chapter 9. Sky-Imaging Systems for Short-Term Forecasting
9.1 Challenges in Short-Term Solar Forecasting9.2 Applications9.3 Sky-Imaging Hardware9.4 Sky-Imagery Analysis Techniques9.5 Case Study: Copper Mountain9.6 Future ApplicationsReferences
Chapter 10. SolarAnywhere Forecasting
10.1 The SolarAnywhere Solar Resource and Forecast Data Service10.2 Solaranywhere Forecast Models10.3 Model Evaluation: Standard Resolution10.4 Performance Evaluation: 1 km, 1 min ForecastsConcluding RemarksReferences
Chapter 11. Satellite-Based Irradiance and Power Forecasting for the German Energy Market
11.1 Solar Energy Penetration in Germany11.2 Overview of the Satellite Forecast Process11.3 Irradiance from Satellite Data11.4 Cloud-Motion Vectors11.5 Evaluation11.6 Evaluation of CMV Forecasts11.7 PV-Power Forecasting11.8 Summary and OutlookReferences
Chapter 12. Forecasting Solar Irradiance with Numerical Weather Prediction Models
12.1 Introduction12.2 Steps Required to Produce a NWP Forecast and Grid Resolution12.3 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 Transfer12.4 Possible Sources of Error in Forecasted Irradiance12.5 Present-Day Accuracy of Solar-Irradiance Forecasts12.6 ConclusionsReferences
Chapter 13. Data Assimilation in Numerical Weather Prediction and Sample Applications
13.1 Introduction13.2 DA Methods and Their Use13.3 How does DA Work?13.4 Solar-Energy DA Challenges13.5 Future Trends13.6 ConclusionsReferences
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 Uncertainty14.2 Solar Forecasting Using NWP at GL-Garrad Hassan14.3 Case Studies on Meeting Stakeholder Needs14.4 Summary and ConclusionsAcronyms, Symbols, and VariablesReferences
Chapter 15. Stochastic-Learning Methods
15.1 Introduction15.2 Baseline Methods for Comparison15.3 Genetic Algorithms15.4 Qualitative Performance Assessment15.5 Performance of Stochastic-Learning Methods with No Exogenous Variables15.6 Sky-Imaging Data as Exogenous Variables for Solar Forecasts15.7 Stochastic-Learning Using Exogenous Variables: The National Digital Forecasting Database15.8 ConclusionsReferences
Color Plates
Index

Content preview from Solar Energy Forecasting and Resource Assessment

Chapter 15

Stochastic-Learning Methods

Carlos F.M. Coimbra and Hugo T.C. Pedro, Center of Excellence in Renewable Energy Integration, Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, University of California, San Diego

Chapter Outline

15.1. Introduction

15.2. Baseline Methods for Comparison

15.2.1. Persistence Methods

15.2.2. ARIMA Models

15.2.3. kNN and ANN

15.3. Genetic Algorithms

15.3.1. GA/ANN: Scanning the Solution Space

15.3.2. Selection, Crossover, Mutation, and Stopping Criterion

15.4. Qualitative Performance Assessment

15.5. Performance of Stochastic-Learning Methods with No Exogenous Variables

15.5.1. Clear-Sky Model

15.5.2. Quantitative Performance of ARIMA, kNN, ANN, GA/ANN

15.6. Sky-Imaging Data ...

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Solar Energy Forecasting and Resource Assessment

by Jan Kleissl

Stochastic-Learning Methods

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.