IEA Wind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions: (Wind Energy Engineering)

Published as an Open Access book available on Science Direct, IEA Wind Recommended Practices for the Implementation of Renewable Energy Forecasting Solutions translates decades of academic knowledge and standard requirements into applicable procedures and decision support tools for the energy industry. Designed specifically for practitioners in the energy industry, readers will find the tools to maximize the value of renewable energy forecast information in operational decision-making applications and significantly reduce the costs of integrating large amounts of wind and solar generation assets into grid systems through more efficient management of the renewable generation variability. Authored by a group of international experts as part of the IEA Wind Task 36 (Wind Energy Forecasting), the book addresses the issue that many current operational forecast solutions are not properly optimized for their intended applications. It provides detailed guidelines and recommended practices on forecast solution selection processes, designing and executing forecasting benchmarks and trials, forecast solution evaluation, verification, and validation, and meteorological and power data requirements for real-time forecasting applications. In addition, the guidelines integrate probabilistic forecasting, integrate wind and solar forecasting, offer improved IT data exchange and data format standards, and have a dedicated section to dealing with the requirements for SCADA and meteorological measurements. A unique and comprehensive reference, IEA Wind Recommended Practices for the Implementation of Renewable Energy Forecasting Solutions is an essential guide for all practitioners involved in wind and solar energy generation forecasting from forecast vendors to end-users of renewable forecasting solutions.

Table of Contents:
1. Forecast Solution Selection Process 2. Designing and Executing Forecasting Benchmarks and Trials 3. Forecast Solution Evaluation 4. Meteorological and Power Data Requirements for Real-Time Forecasting Applications

About the Author :
Dr. Corinna Möhrlen earned a Masters degree in Civil Engineering from Ruhr-University Bochum, Germany and a Masters and PhD degree from University College Cork, Ireland, where she started her career in the wind energy area in 2000, being responsible for the development of wind energy forecasting in Ireland in collaboration with the Danish Meteorological Institute. She is co-founder and managing director of WEPROG. Founded in 2003, WEPROG provides world-wide operational weather ensemble and energy forecasting services and is highly specialised in the energy industry's renewables integration. Over the past 20 years Corinna gained experience in integrating renewables into real-time operations, served as coordinator and participant in R&D projects, actively writes and reviews journal articles, organises and participates in workshops and advices on application of ensemble forecasting to deal with uncertainties related to renewable energy generation. Corinna is a board member and workpackage 3 leader of the IEA Wind Task 36 "Wind Energy Forecasting" and received an ESIG excellence award for contributions to advances in the use of probabilistic forecasting in 2020. Dr. John W. Zack earned a BS degree in meteorology and oceanography from New York University and a Ph.D. in atmospheric sciences from Cornell University. He is the President, Chief Scientist and co-founder of MESO, Inc., a 35-year-old small business that specializes in the development and application of physics-based and statistical geophysical models. He is also a Senior Advisor at UL Services Group after leaving his full-time position as the head of the company’s Grid Solutions division in 2018. In 1998, he was one of the founding partners of AWS Truepower, a global leader in renewable energy consulting services, and served on its Board of Directors until its acquisition by UL, Inc in 2016. John is a board member and workpackage 2 leader of the IEA Wind Task 36 "Wind Energy Forecasting" and received an ESIG excellence award for contributions to advances in the use of probabilistic forecasting in 2020. Dr. Gregor Giebel has worked for over 25 years with short-term forecasting of wind power, large-scale integration of wind power into electricity grids, wind farm flow control and condition monitoring for wind turbines including standardisation within the IEC. He is Operating Agent of IEA Wind Task 36 Forecasting for Wind Energy, and heads the FarmConners EU Coordination Action on wind farm control (windfarmcontrol.info) and the EU Marie Curie Initial Training Network Train2Wind (train2wind.eu). His main claim to fame is the standard report on the state of the art in short-term forecasting, with over 950 citations. He also is an accomplished writer of research proposals, with a funding quota of over 50%.