Forecasting and Assessing Risk of Individual Electricity Peaks
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 valu...
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| Huvudupphov: | , , |
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| Materialtyp: | Online |
| Språk: | engelska |
| Utgiven: |
Springer Nature
2021
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| Ämnen: | |
| Länkar: | 1007022 |
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Inga taggar, Lägg till första taggen!
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| _version_ | 1869525567561269248 |
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| author | Jacob, Maria Neves, Cláudia Vukadinović Greetham, Danica |
| author_browse | Jacob, Maria Neves, Cláudia Vukadinović Greetham, Danica |
| author_facet | Jacob, Maria Neves, Cláudia Vukadinović Greetham, Danica |
| author_sort | Jacob, Maria |
| collection | Directory of Open Access Books |
| description | 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, academics, engineers and professionals that are interested in short term load prediction, energy data analytics, battery control, demand side response and data science in general. |
| format | Online |
| id | doab-20.500.12854ir-38099 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-380992025-03-23T12:51:04Z Forecasting and Assessing Risk of Individual Electricity Peaks Jacob, Maria Neves, Cláudia Vukadinović Greetham, Danica Mathematics Mathematics Statistics Energy efficiency Algorithms Energy systems thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering 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, academics, engineers and professionals that are interested in short term load prediction, energy data analytics, battery control, demand side response and data science in general. 2021-02-10T14:52:02Z 2021-02-10T14:52:02Z 2020-03-18 13:36:15 2020-04-01T09:04:57Z 2020 book 1007022 OCN: 1121273246 http://library.oapen.org/handle/20.500.12657/23132 https://directory.doabooks.org/handle/20.500.12854/38099 eng Mathematics of Planet Earth open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/23132/1/1007022.pdf https://library.oapen.org/bitstream/20.500.12657/23132/1/1007022.pdf https://library.oapen.org/bitstream/20.500.12657/23132/1/1007022.pdf Springer Nature 10.1007/978-3-030-28669-9 10.1007/978-3-030-28669-9 9fa3421d-f917-4153-b9ab-fc337c396b5a 97 Cham open access |
| spellingShingle | Mathematics Mathematics Statistics Energy efficiency Algorithms Energy systems thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering Jacob, Maria Neves, Cláudia Vukadinović Greetham, Danica Forecasting and Assessing Risk of Individual Electricity Peaks |
| title | Forecasting and Assessing Risk of Individual Electricity Peaks |
| title_full | Forecasting and Assessing Risk of Individual Electricity Peaks |
| title_fullStr | Forecasting and Assessing Risk of Individual Electricity Peaks |
| title_full_unstemmed | Forecasting and Assessing Risk of Individual Electricity Peaks |
| title_short | Forecasting and Assessing Risk of Individual Electricity Peaks |
| title_sort | forecasting and assessing risk of individual electricity peaks |
| topic | Mathematics Mathematics Statistics Energy efficiency Algorithms Energy systems thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering |
| topic_facet | Mathematics Mathematics Statistics Energy efficiency Algorithms Energy systems thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering |
| url | 1007022 |
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