Ensemble Forecasting Applied to Power Systems

preview-18
  • Ensemble Forecasting Applied to Power Systems Book Detail

  • Author : Antonio Bracale
  • Release Date : 2020-03-10
  • Publisher : MDPI
  • Genre : Technology & Engineering
  • Pages : 134
  • ISBN 13 : 303928312X
  • File Size : 87,87 MB

Ensemble Forecasting Applied to Power Systems by Antonio Bracale PDF Summary

Book Description: Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managing modern power systems at any level: from transmission to distribution networks, and in also the new context of smart grids. Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques have recently started to attract the interest of power system practitioners. This book addresses the development of new, advanced, ensemble forecasting methods applied to power systems, collecting recent contributions to the development of accurate forecasts of energy-related variables by some of the most qualified experts in energy forecasting. Typical areas of research (renewable energy forecasting, load forecasting, energy price forecasting) are investigated, with relevant applications to the use of forecasts in energy management systems.

Disclaimer: www.yourbookbest.com does not own Ensemble Forecasting Applied to Power Systems books pdf, neither created or scanned. We just provide the link that is already available on the internet, public domain and in Google Drive. If any way it violates the law or has any issues, then kindly mail us via contact us page to request the removal of the link.

Wind Power Ensemble Forecasting

Wind Power Ensemble Forecasting

File Size : 34,34 MB
Total View : 5982 Views
DOWNLOAD

This thesis describes performance measures and ensemble architectures for deterministic and probabilistic forecasts using the application example of wind power