Includes bibliographical references and index.

Online resource; title from PDF title page (EBSCO, viewed June 27, 2017)

Front Cover; Renewable Energy Forecasting; Related titles; Renewable Energy ForecastingWoodhead Publishing Series in EnergyFrom Models to ApplicationsEdited ByGeorge Kariniotakis?; Copyright; Contents; List of contributors; One -- Introduction to meteorology and measurement technologies; 1 -- Principles of meteorology and numerical weather prediction; 1.1 Introduction to meteorology for renewable energy forecasting; 1.1.1 Atmospheric motion; 1.1.2 Prediction across scales; 1.1.3 Atmospheric chaos; 1.2 Observational data and assimilation into numerical weather prediction models

1.2.1 Observational data1.2.2 Data assimilation; 1.2.2.1 Nudging; 1.2.2.2 Variational assimilation; 1.2.2.3 Ensemble Kalman filters; 1.2.2.4 Hybrid approaches; 1.2.3 Coupled models; 1.3 Configuring numerical weather prediction to the needs of the problem; 1.3.1 Fundamentals of numerical weather prediction; 1.3.1.1 Dynamic solver; 1.3.1.2 Parameterizations; 1.3.2 Standard physics available in numerical weather prediction models; 1.3.3 Configuration of numerical weather prediction models for specific applications; 1.3.4 Model development: the WRF-Solar model; 1.4 Postprocessing

1.5 Probabilistic forecasting1.6 Planning for validation; 1.7 Weather forecasting as a Big Data problem; Acknowledgments; References; Further reading; 2 -- Measurement methodologies for wind energy based on ground-level remote sensing; 2.1 Introduction; 2.1.1 Historical background; 2.1.2 Measuring principles for a heterodyne wind lidar; 2.1.3 Wind lidar calibration; 2.1.4 Climatological use of Doppler wind lidar measurements; 2.1.5 Turbulence estimated from wind lidar measurements; 2.1.5.1 Filtering of the signal and its consequence for the estimation of turbulence

2.1.5.2 A numerical turbulence reconstruction method from Doppler lidar measurements2.1.5.3 Turbulent properties from a vertically pointing Doppler lidar; 2.1.5.4 Wind gusts from a lidar; 2.1.6 Boundary layer depth detection from lidars; 2.1.7 Long-range and short-range WindScanner systems; 2.1.7.1 The long-range WindScanner system; 2.1.7.2 The short-range WindScanner system; References; Two -- Methods for renewable energy forecasting; 3 -- Wind power forecasting-a review of the state of the art; 3.1 Introduction; 3.1.1 Forecast timescales; 3.1.2 The typical model chain; 3.2 Time series models

3.2.1 Time series models for very-short-term forecasting3.2.2 An explanation of the time series model improvements; 3.3 Meteorological modeling for wind power predictions; 3.3.1 Improvements in NWP and mesoscale modeling; 3.3.2 Ensemble Kalman filtering; 3.4 Short-term prediction models with NWPs; 3.4.1 Modeling wind speed versus wind power; 3.5 Upscaling models; 3.6 Spatio-temporal forecasting; 3.7 Ramp forecasting; 3.8 Variability forecasting; 3.9 Uncertainty of wind power predictions; 3.9.1 Statistical approaches; 3.9.2 Ensemble forecasts, risk indices, and scenarios

This book provides an overview of the state-of-the-art of renewable energy forecasting technology and its applications. After an introduction to the principles of meteorology and renewable energy generation, groups of chapters address forecasting models, very short-term forecasting, forecasting of extremes, and longer term forecasting.

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