Feature Papers of Forecasting 2021

doi:10.3390/books978-3-0365-5572-0

Download Url(s)

https://mdpi.com/books/pdfview/book/6266

Contributor(s)

Leva, Sonia (editor)

Language

English

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Abstract

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

URI

https://directory.doabooks.org/handle/20.500.12854/93837

Keywords

renewable energy sources; load forecasting; frequency regulation; artificial neural network; model predictive control; building energy management; forecast; neural network; SCADA; user comfort; tobacco endgame; policy; simulation model; tobacco tax revenue; information; combination; uncertainty; theta; temporal aggregation; bagging; sub-seasonal series; power outages; machine learning; thunderstorms; numerical weather prediction; battery energy storage system; battery sizing; photovoltaic power production; performance ratio; electrical load; decision tree; k-means clustering; load curve; unevenly spaced time series; long short-term memory (LSTM); back-propagation neural network (BPNN); water consumption; Holt method; subsampling bootstrapped; harmony search algorithm; forecasting; ARCH-GARCH; model-free; aggregated forecasting; deep learning; Loop Current; ocean current forecasting; LSTM; ocean measurements; COVID-19; probabilistic graphical models; interpretable machine learning; n/a