Ensemble Forecasting Applied to Power Systems

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 managi...

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Detaylı Bibliyografya
Asıl Yazarlar:	Bracale, Antonio, Falco, Pasquale De
Materyal Türü:	Online
Dil:	İngilizce
Baskı/Yayın Bilgisi:	MDPI - Multidisciplinary Digital Publishing Institute 2021
Konular:	TA1-2040 T1-995 forecast combination solar energy electricity price forecasting calibration window heuristic algorithm deep learning electric load forecasting smart grids hierarchical load forecasting predictive distribution solar PV solar farm microgrid energy management lower and upper bound estimation solar power prediction interval prediction kernel density estimation average probability forecast probabilistic forecasting forecasting distributed energy resources photovoltaic power conditional predictive ability clearness index Fourier series combining forecasts weather station combination distributed generation clear sky index extreme learning machine ensemble methods pinball score autoregression thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Online Erişim:	44795
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

Diğer Bilgiler
Özet:	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.

Ensemble Forecasting Applied to Power Systems

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