Statistical Learning Tools for Electricity Load Forecasting - Jean-Michel Poggi,Jairo Cugliari,Matteo Fasiolo,Yannig Goude,Anestis Antoniadis
-40% ar kodu BOOKS
Piegāde 17-23 darba dienu laikā
30 dienu atgriešanas politika
This monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the authors guide readers through several modern forecasting methods and tools from both industrial and applied perspe ... Pilns apraksts
Jums varētu patikt arī
TOP
If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All
Eliezer Yudkowsky, Nate Soares
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
How To Think About AI: A Guide For The Perplexed
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Gödel, Escher, Bach: An Eternal Golden Braid
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
AI Engineering: Building Applications with Foundation Models
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Python Crash Course: A Hands-On, Project-Based Introduction to Programming
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
The Rust Programming Language
Steve Klabnik, Carol Nichols, Chris Krycho
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
How to Talk to AI: (And How Not To)
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
The Pragmatic Programmer: journey to mastery, 20th Anniversary Edition, 2/e: your journey to mastery, 20th Anniversary Edition
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Hackers. 25th Anniversary Edition: Heroes of the Computer Revolution
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
The Art of Game Design: A Book of Lenses
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
How Linux Works: What Every Superuser Should Know
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Hands-On Large Language Models: Language Understanding and Generation
Maarten Grootendorst, Jay Alammar
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Fundamentals of Software Architecture: A Modern Engineering Approach
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Speak Data: Artists, Scientists, Thinkers, and Dreamers on How We Live Our Lives in Numbers
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Clean Architecture: A Craftsman's Guide to Software Structure and Design
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
World of Warcraft Chronicle, Volume 2
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Linux Basics for Hackers, 2nd Edition: Getting Started with Networking, Scripting, and Security in Kali
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Deep Learning: Foundations and Concepts
Christopher M. Bishop, Hugh Bishop
-40% ar kodu BOOKS
Piegādātāja noliktavā
Aprašymas
This monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the authors guide readers through several modern forecasting methods and tools from both industrial and applied perspectives ¿ generalized additive models (GAMs), probabilistic GAMs, functional time series and wavelets, random forests, aggregation of experts, and mixed effects models. A collection of case studies based on sizable high-resolution datasets, together with relevant R packages, then illustrate the implementation of these techniques. Five real datasets at three different levels of aggregation (nation-wide, region-wide, or individual) from four different countries (UK, France, Ireland, and the USA) are utilized to study five problems: short-term point-wise forecasting, selection of relevant variables for prediction, construction of prediction bands, peak demand prediction, and use of individual consumer data. This text is intended for practitioners, researchers, and post-graduate students working on electricity load forecasting; it may also be of interest to applied academics or scientists wanting to learn about cutting-edge forecasting tools for application in other areas. Readers are assumed to be familiar with standard statistical concepts such as random variables, probability density functions, and expected values, and to possess some minimal modeling experience.
Vairāk informācijas
| Autors | Jean-Michel Poggi, Jairo Cugliari, Matteo Fasiolo, Yannig Goude, Anestis Antoniadis |
|---|---|
| Izdevējs | Springer International Publishing |
| Series | Statistics for Industry, Technology, and Engineering |
| Izlaides gads | 2024 |
| Vāka tips | Cietais vāks |
| EAN | 9783031603389 |