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Titlebook: Forecasting and Assessing Risk of Individual Electricity Peaks; Maria Jacob,Cláudia Neves,Danica Vukadinovi? Greet Book‘‘‘‘‘‘‘‘ 2020 The E

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書目名稱Forecasting and Assessing Risk of Individual Electricity Peaks
編輯Maria Jacob,Cláudia Neves,Danica Vukadinovi? Greet
視頻videohttp://file.papertrans.cn/346/345295/345295.mp4
概述Presents a self-contained theory and algorithms for individual energy load peak prediction.Implementations are available in Python in R.Uses case studies on publicly available data and has accessible
叢書名稱Mathematics of Planet Earth
圖書封面Titlebook: Forecasting and Assessing Risk of Individual Electricity Peaks;  Maria Jacob,Cláudia Neves,Danica Vukadinovi? Greet Book‘‘‘‘‘‘‘‘ 2020 The E
描述.The overarching aim of this open access book is to present self-contained theory and algorithms for investigation and prediction of electric demand peaks. A cross-section of popular demand forecasting algorithms from statistics, machine learning and mathematics is presented, followed by extreme value theory techniques with examples..In order to achieve carbon targets, good forecasts of peaks are essential. For instance, shifting demand or charging battery depends on correct demand predictions in time. Majority of forecasting algorithms historically were focused on average load prediction. In order to model the peaks, methods from extreme value theory are applied. This allows us to study extremes without making any assumption on the central parts of demand distribution and to predict beyond the range of available data. . .While applied on individual loads, the techniques described in this book can be extended naturally to substations, or to commercial settings.Extreme value theory techniques presented can be also used across other disciplines, for example for predicting heavy rainfalls, wind speed, solar radiation and extreme weather events. The book is intended for students, acade
出版日期Book‘‘‘‘‘‘‘‘ 2020
關(guān)鍵詞60G70, 05C85 , 62M10, 68T05; electricity forecasting; extreme value theory; scedasis; heteroscedasticity
版次1
doihttps://doi.org/10.1007/978-3-030-28669-9
isbn_softcover978-3-030-28668-2
isbn_ebook978-3-030-28669-9Series ISSN 2524-4264 Series E-ISSN 2524-4272
issn_series 2524-4264
copyrightThe Editor(s) (if applicable) and The Author(s) 2020
The information of publication is updating

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