Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review
Abstract Wind power is playing a pivotal part in global energy growth as it is clean and pollution‐free. To maximize profits, economic scheduling, dispatching, and planning the unit commitment, there is a great demand for wind forecasting techniques. This drives the researchers and electric utility...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2020-06-01
|
Series: | Engineering Reports |
Subjects: | |
Online Access: | https://doi.org/10.1002/eng2.12178 |
id |
doaj-2eabdd30a0cf4feeaa410fea3195e7db |
---|---|
record_format |
Article |
spelling |
doaj-2eabdd30a0cf4feeaa410fea3195e7db2020-11-25T03:40:41ZengWileyEngineering Reports2577-81962020-06-0126n/an/a10.1002/eng2.12178Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A reviewMadasthu Santhosh0Chintham Venkaiah1D. M. Vinod Kumar2Department of Electrical Engineering National Institute of Technology Warangal IndiaDepartment of Electrical Engineering National Institute of Technology Warangal IndiaDepartment of Electrical Engineering National Institute of Technology Warangal IndiaAbstract Wind power is playing a pivotal part in global energy growth as it is clean and pollution‐free. To maximize profits, economic scheduling, dispatching, and planning the unit commitment, there is a great demand for wind forecasting techniques. This drives the researchers and electric utility planners in the direction of more advanced approaches to forecast over broader time horizons. Key prediction techniques use physical, statistical approaches, artificial intelligence techniques, and hybrid methods. An extensive review of the current forecasting techniques, as well as their performance evaluation, is here presented. The techniques used for improving the prediction accuracy, methods to overcome major forecasting problems, evolving trends, and further advanced applications in future research are explored.https://doi.org/10.1002/eng2.12178artificial intelligencedecomposition‐based modelsdeep learninghybrid predictionnumerical weather predictionwind speed and wind power forecasting |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Madasthu Santhosh Chintham Venkaiah D. M. Vinod Kumar |
spellingShingle |
Madasthu Santhosh Chintham Venkaiah D. M. Vinod Kumar Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review Engineering Reports artificial intelligence decomposition‐based models deep learning hybrid prediction numerical weather prediction wind speed and wind power forecasting |
author_facet |
Madasthu Santhosh Chintham Venkaiah D. M. Vinod Kumar |
author_sort |
Madasthu Santhosh |
title |
Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review |
title_short |
Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review |
title_full |
Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review |
title_fullStr |
Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review |
title_full_unstemmed |
Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review |
title_sort |
current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: a review |
publisher |
Wiley |
series |
Engineering Reports |
issn |
2577-8196 |
publishDate |
2020-06-01 |
description |
Abstract Wind power is playing a pivotal part in global energy growth as it is clean and pollution‐free. To maximize profits, economic scheduling, dispatching, and planning the unit commitment, there is a great demand for wind forecasting techniques. This drives the researchers and electric utility planners in the direction of more advanced approaches to forecast over broader time horizons. Key prediction techniques use physical, statistical approaches, artificial intelligence techniques, and hybrid methods. An extensive review of the current forecasting techniques, as well as their performance evaluation, is here presented. The techniques used for improving the prediction accuracy, methods to overcome major forecasting problems, evolving trends, and further advanced applications in future research are explored. |
topic |
artificial intelligence decomposition‐based models deep learning hybrid prediction numerical weather prediction wind speed and wind power forecasting |
url |
https://doi.org/10.1002/eng2.12178 |
work_keys_str_mv |
AT madasthusanthosh currentadvancesandapproachesinwindspeedandwindpowerforecastingforimprovedrenewableenergyintegrationareview AT chinthamvenkaiah currentadvancesandapproachesinwindspeedandwindpowerforecastingforimprovedrenewableenergyintegrationareview AT dmvinodkumar currentadvancesandapproachesinwindspeedandwindpowerforecastingforimprovedrenewableenergyintegrationareview |
_version_ |
1724533442978250752 |