Journal ArticleDOI
Current methods and advances in forecasting of wind power generation
TLDR
A review of the current methods and advances in wind power forecasting and prediction can be found in this article, where numerical wind power prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed.About:
This article is published in Renewable Energy.The article was published on 2012-01-01. It has received 1017 citations till now. The article focuses on the topics: Wind power forecasting & Wind power.read more
Citations
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Journal ArticleDOI
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
David R. Roberts,Volker Bahn,Simone Ciuti,Mark S. Boyce,Jane Elith,Gurutzeta Guillera-Arroita,Severin Hauenstein,José J. Lahoz-Monfort,Boris Schröder,Wilfried Thuiller,David I. Warton,Brendan A. Wintle,Florian Hartig,Florian Hartig,Carsten F. Dormann +14 more
TL;DR: It is recommended that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.
Journal ArticleDOI
Energy harvesting in wireless sensor networks: A comprehensive review
TL;DR: A comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs is presented and some of the challenges still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment are identified.
Journal ArticleDOI
A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges.
Andreas F. Prein,Andreas F. Prein,Wolfgang Langhans,Giorgia Fosser,Andrew Ferrone,Nikolina Ban,Klaus Goergen,Michael Keller,Merja Tölle,Oliver Gutjahr,Frauke Feser,Erwan Brisson,Stefan Kollet,Juerg Schmidli,Nicole Van Lipzig,Ruby Leung +15 more
TL;DR: This study aims to provide a common basis for CPM climate simulations by giving a holistic review of the topic, and presents the consolidated outcome of studies that addressed the added value of CPMClimate simulations compared to LSMs.
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Big data driven smart energy management: From big data to big insights
Kaile Zhou,Chao Fu,Shanlin Yang +2 more
TL;DR: A systematic review of big data analytics for smart energy management from four major aspects, namely power generation side management, microgrid and renewable energy management, asset management and collaborative operation, as well as demand side management (DSM).
Journal ArticleDOI
Current status and future advances for wind speed and power forecasting
Jaesung Jung,Robert Broadwater +1 more
TL;DR: An overview of existing research on wind speed and power forecasting can be found in this article, where state-of-the-art approaches for wind power and wind speed forecasting are discussed.
References
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A Description of the Advanced Research WRF Version 3
C. Skamarock,B. Klemp,Jimy Dudhia,O. Gill,Dale Barker,G. Duda,Xiang-Yu Huang,Wei Wang,G. Powers +8 more
TL;DR: The Technical Note series provides an outlet for a variety of NCAR manuscripts that contribute in specialized ways to the body of scientific knowledge but which are not suitable for journal, monograph, or book publication.
A Description of the Advanced Research WRF Version 2
William C. Skamarock,Joseph B. Klemp,Jimy Dudhia,David O. Gill,Dale Barker,Wei Wang,Jordan G. Powers +6 more
TL;DR: The Weather Research and Forecasting (WRF) model as mentioned in this paper was developed as a collaborative effort among the NCAR Mesoscale and Microscale Meteorology (MMM) Division, the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Prediction (NCEP) and Forecast System Laboratory (FSL), the Department of Defense's Air Force Weather Agency (AFWA) and Naval Research Laboratory (NRL), the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma, and the Federal Aviation Administration (F
Journal ArticleDOI
The Use of Model Output Statistics (MOS) in Objective Weather Forecasting
Harry R. Glahn,Dale A. Lowry +1 more
TL;DR: Model Output Statistics (MOS) as mentioned in this paper is an objective weather forecasting technique which consists of determining a statistical relationship between a predictand and variables forecast by a numerical model at some projection time(s).