Reprint

Ensemble Forecasting Applied to Power Systems

Edited by
March 2020
134 pages
  • ISBN978-3-03928-312-5 (Paperback)
  • ISBN978-3-03928-313-2 (PDF)

This book is a reprint of the Special Issue Ensemble Forecasting Applied to Power Systems that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
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.
Format
  • Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
forecast combination; photovoltaic power; probabilistic forecasting; distributed energy resources; energy management; microgrid; deep learning; weather station combination; electric load forecasting; hierarchical load forecasting; electricity price forecasting; predictive distribution; combining forecasts; average probability forecast; calibration window; autoregression; pinball score; conditional predictive ability; solar power prediction; interval prediction; lower and upper bound estimation; extreme learning machine; heuristic algorithm; forecasting; ensemble methods; kernel density estimation; smart grids; distributed generation; solar PV; solar energy; solar farm; clearness index; clear sky index; Fourier series; forecasting