Renewable energy forecasting : from models to applications /
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| Imprint: | Duxford, United Kingdom : Woodhead Publishing, an imprint of Elsevier, [2017] |
|---|---|
| Description: | 1 online resource |
| Language: | English |
| Series: | Woodhead Publishing Series in Energy Woodhead Publishing in energy. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11689857 |
Table of Contents:
- 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