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dc.contributor.authorBracale, Antonio*
dc.contributor.authorFalco, Pasquale De*
dc.date.accessioned2021-02-11T12:37:31Z
dc.date.available2021-02-11T12:37:31Z
dc.date.issued2020*
dc.date.submitted2020-04-07 23:07:09*
dc.identifier44795*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/46473
dc.description.abstractModern 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.*
dc.languageEnglish*
dc.subjectTA1-2040*
dc.subjectT1-995*
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technologyen_US
dc.subject.otherforecast combination*
dc.subject.othersolar energy*
dc.subject.otherelectricity price forecasting*
dc.subject.othercalibration window*
dc.subject.otherheuristic algorithm*
dc.subject.otherdeep learning*
dc.subject.otherelectric load forecasting*
dc.subject.othersmart grids*
dc.subject.otherhierarchical load forecasting*
dc.subject.otherpredictive distribution*
dc.subject.othersolar PV*
dc.subject.othersolar farm*
dc.subject.othermicrogrid*
dc.subject.otherenergy management*
dc.subject.otherlower and upper bound estimation*
dc.subject.othersolar power prediction*
dc.subject.otherinterval prediction*
dc.subject.otherkernel density estimation*
dc.subject.otheraverage probability forecast*
dc.subject.otherprobabilistic forecasting*
dc.subject.otherforecasting*
dc.subject.otherdistributed energy resources*
dc.subject.otherphotovoltaic power*
dc.subject.otherconditional predictive ability*
dc.subject.otherclearness index*
dc.subject.otherFourier series*
dc.subject.othercombining forecasts*
dc.subject.otherweather station combination*
dc.subject.otherdistributed generation*
dc.subject.otherclear sky index*
dc.subject.otherextreme learning machine*
dc.subject.otherensemble methods*
dc.subject.otherpinball score*
dc.subject.otherautoregression*
dc.titleEnsemble Forecasting Applied to Power Systems*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03928-313-2*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
oapen.relation.isbn9783039283132*
oapen.relation.isbn9783039283125*
oapen.pages134*
oapen.edition1st*


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