Forecasting and Assessing Risk of Individual Electricity Peaks
License: CC By 4.0
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| Ngôn ngữ: | English |
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Springer Nature
2023
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| Truy cập trực tuyến: | https://dlib.phenikaa-uni.edu.vn/handle/PNK/6640 https://library.oapen.org/handle/20.500.12657/23132 |
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oai:localhost:PNK-66402023-01-12T02:48:46Z Forecasting and Assessing Risk of Individual Electricity Peaks Maria, Jacob Cláudia, Neves Danica, Vukadinović Greetham Mathematics Statistics Energy efficiency Algorithms Energy systems Toán học Số liệu thống kê Thuật toán hệ thống năng lượng Hiệu suất năng lượng License: CC By 4.0 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. 2023-01-12T02:46:05Z 2023-01-12T02:46:05Z 2020 Book 978-3-030-28669-9 https://dlib.phenikaa-uni.edu.vn/handle/PNK/6640 https://library.oapen.org/handle/20.500.12657/23132 en application/pdf Springer Nature |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| topic |
Mathematics Statistics Energy efficiency Algorithms Energy systems Toán học Số liệu thống kê Thuật toán hệ thống năng lượng Hiệu suất năng lượng |
| spellingShingle |
Mathematics Statistics Energy efficiency Algorithms Energy systems Toán học Số liệu thống kê Thuật toán hệ thống năng lượng Hiệu suất năng lượng Maria, Jacob Cláudia, Neves Danica, Vukadinović Greetham Forecasting and Assessing Risk of Individual Electricity Peaks |
| description |
License: CC By 4.0 |
| format |
Book |
| author |
Maria, Jacob Cláudia, Neves Danica, Vukadinović Greetham |
| author_facet |
Maria, Jacob Cláudia, Neves Danica, Vukadinović Greetham |
| author_sort |
Maria, Jacob |
| title |
Forecasting and Assessing Risk of Individual Electricity Peaks |
| title_short |
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_sort |
forecasting and assessing risk of individual electricity peaks |
| publisher |
Springer Nature |
| publishDate |
2023 |
| url |
https://dlib.phenikaa-uni.edu.vn/handle/PNK/6640 https://library.oapen.org/handle/20.500.12657/23132 |
| _version_ |
1754845947029880832 |
| score |
8.891145 |