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230202 ||| eng |
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|a 9783036555713
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|a 9783036555720
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|a books978-3-0365-5572-0
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| 100 |
1 |
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|a Leva, Sonia
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| 245 |
0 |
0 |
|a Feature Papers of Forecasting 2021
|h Elektronische Ressource
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| 260 |
|
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
|
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|a 1 electronic resource (196 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a Holt method
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| 653 |
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|a renewable energy sources
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| 653 |
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|a forecast
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| 653 |
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|a model-free
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| 653 |
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|a water consumption
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| 653 |
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|a combination
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| 653 |
|
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|a forecasting
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| 653 |
|
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|a k-means clustering
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| 653 |
|
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|a frequency regulation
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| 653 |
|
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|a n/a
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| 653 |
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|a numerical weather prediction
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| 653 |
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|a power outages
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| 653 |
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|a photovoltaic power production
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| 653 |
|
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|a deep learning
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| 653 |
|
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|a uncertainty
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| 653 |
|
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|a History of engineering and technology / bicssc
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| 653 |
|
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|a user comfort
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| 653 |
|
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|a LSTM
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| 653 |
|
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|a harmony search algorithm
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| 653 |
|
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|a simulation model
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| 653 |
|
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|a theta
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| 653 |
|
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|a ARCH-GARCH
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| 653 |
|
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|a Technology: general issues / bicssc
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| 653 |
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|a building energy management
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| 653 |
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|a artificial neural network
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| 653 |
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|a COVID-19
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| 653 |
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|a load forecasting
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| 653 |
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|a tobacco tax revenue
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| 653 |
|
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|a model predictive control
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| 653 |
|
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|a long short-term memory (LSTM)
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| 653 |
|
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|a probabilistic graphical models
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| 653 |
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|a policy
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| 653 |
|
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|a back-propagation neural network (BPNN)
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| 653 |
|
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|a bagging
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| 653 |
|
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|a Environmental science, engineering and technology / bicssc
|
| 653 |
|
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|a ocean measurements
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| 653 |
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|a subsampling bootstrapped
|
| 653 |
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|a unevenly spaced time series
|
| 653 |
|
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|a aggregated forecasting
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| 653 |
|
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|a performance ratio
|
| 653 |
|
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|a sub-seasonal series
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| 653 |
|
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|a battery energy storage system
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| 653 |
|
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|a load curve
|
| 653 |
|
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|a Loop Current
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| 653 |
|
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|a interpretable machine learning
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| 653 |
|
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|a battery sizing
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| 653 |
|
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|a thunderstorms
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| 653 |
|
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|a decision tree
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| 653 |
|
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|a ocean current forecasting
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| 653 |
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|a tobacco endgame
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| 653 |
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|a SCADA
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| 653 |
|
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|a electrical load
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| 653 |
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|a neural network
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| 653 |
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|a temporal aggregation
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| 653 |
|
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|a information
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| 700 |
1 |
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|a Leva, Sonia
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
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|b DOAB
|a Directory of Open Access Books
|
| 500 |
|
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
|
| 028 |
5 |
0 |
|a 10.3390/books978-3-0365-5572-0
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/93837
|z DOAB: description of the publication
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/6266
|7 0
|x Verlag
|3 Volltext
|
| 082 |
0 |
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|a 900
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| 082 |
0 |
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|a 363
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| 082 |
0 |
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|a 000
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| 082 |
0 |
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|a 333
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| 082 |
0 |
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|a 700
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| 082 |
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|a 600
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| 082 |
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|a 620
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| 520 |
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|a This book focuses on fundamental and applied research on forecasting methods and analyses on how forecasting can affect a great number of fields, spanning from Computer Science, Engineering, and Economics and Business to natural sciences. Forecasting applications are increasingly important because they allow for improving decision-making processes by providing useful insights about the future. Scientific research is giving unprecedented attention to forecasting applications, with a continuously growing number of articles about novel forecast approaches being published
|