Short term prediction of wind speed based on long-short term memory networks

Power utilities, developers, and investors are pushing towards larger penetrations of wind and solar energy-based power generation in their existing energy mix. This study, specifically, looks towards wind power deployment in Saudi Arabia. For profitable development of wind power, accurate knowledge...

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Autores principales:	Salman Umar T., Rehman Shafiqur, Alawode Basit, Alhems Luai M.
Formato:	article
Lenguaje:	EN
Publicado:	University of Belgrade - Faculty of Mechanical Engineering, Belgrade 2021
Materias:	ann errors forecasting lstm wind speed wind power Engineering (General). Civil engineering (General) TA1-2040 Mechanics of engineering. Applied mechanics TA349-359
Acceso en línea:	https://doaj.org/article/ec308bb4f4ac4cd795513d6137b2a471
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spelling	oai:doaj.org-article:ec308bb4f4ac4cd795513d6137b2a4712021-12-05T21:01:45ZShort term prediction of wind speed based on long-short term memory networks1451-20922406-128X10.5937/fme2103643Shttps://doaj.org/article/ec308bb4f4ac4cd795513d6137b2a4712021-01-01T00:00:00Zhttps://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2021/1451-20922103643S.pdfhttps://doaj.org/toc/1451-2092https://doaj.org/toc/2406-128XPower utilities, developers, and investors are pushing towards larger penetrations of wind and solar energy-based power generation in their existing energy mix. This study, specifically, looks towards wind power deployment in Saudi Arabia. For profitable development of wind power, accurate knowledge of wind speed both in spatial and time domains is critical. The wind speed is the most fluctuating and intermittent parameter in nature compared to all the meteorological variables. This uncertain nature of wind speed makes wind power more difficult to predict ahead of time. Wind speed is dependent on meteorological factors such as pressure, temperature, and relative humidity and can be predicted using these meteorological parameters. The forecasting of wind speed is critical for grid management, cost of energy, and quality power supply. This study proposes a short-term, multi-dimensional prediction of wind speed based on Long-Short Term Memory Networks (LSTM). Five models are developed by training the networks with measured hourly mean wind speed values from1980 to 2019 including exogenous inputs (temperature and pressure). The study found that LSTM is a powerful tool for a short-term prediction of wind speed. However, the accuracy of LSTM may be compromised with the inclusion of exogenous features in the training sets and the duration of prediction ahead.Salman Umar T.Rehman ShafiqurAlawode BasitAlhems Luai M.University of Belgrade - Faculty of Mechanical Engineering, Belgradearticleannerrorsforecastinglstmwind speedwind powerEngineering (General). Civil engineering (General)TA1-2040Mechanics of engineering. Applied mechanicsTA349-359ENFME Transactions, Vol 49, Iss 3, Pp 643-652 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ann errors forecasting lstm wind speed wind power Engineering (General). Civil engineering (General) TA1-2040 Mechanics of engineering. Applied mechanics TA349-359
spellingShingle	ann errors forecasting lstm wind speed wind power Engineering (General). Civil engineering (General) TA1-2040 Mechanics of engineering. Applied mechanics TA349-359 Salman Umar T. Rehman Shafiqur Alawode Basit Alhems Luai M. Short term prediction of wind speed based on long-short term memory networks
description	Power utilities, developers, and investors are pushing towards larger penetrations of wind and solar energy-based power generation in their existing energy mix. This study, specifically, looks towards wind power deployment in Saudi Arabia. For profitable development of wind power, accurate knowledge of wind speed both in spatial and time domains is critical. The wind speed is the most fluctuating and intermittent parameter in nature compared to all the meteorological variables. This uncertain nature of wind speed makes wind power more difficult to predict ahead of time. Wind speed is dependent on meteorological factors such as pressure, temperature, and relative humidity and can be predicted using these meteorological parameters. The forecasting of wind speed is critical for grid management, cost of energy, and quality power supply. This study proposes a short-term, multi-dimensional prediction of wind speed based on Long-Short Term Memory Networks (LSTM). Five models are developed by training the networks with measured hourly mean wind speed values from1980 to 2019 including exogenous inputs (temperature and pressure). The study found that LSTM is a powerful tool for a short-term prediction of wind speed. However, the accuracy of LSTM may be compromised with the inclusion of exogenous features in the training sets and the duration of prediction ahead.
format	article
author	Salman Umar T. Rehman Shafiqur Alawode Basit Alhems Luai M.
author_facet	Salman Umar T. Rehman Shafiqur Alawode Basit Alhems Luai M.
author_sort	Salman Umar T.
title	Short term prediction of wind speed based on long-short term memory networks
title_short	Short term prediction of wind speed based on long-short term memory networks
title_full	Short term prediction of wind speed based on long-short term memory networks
title_fullStr	Short term prediction of wind speed based on long-short term memory networks
title_full_unstemmed	Short term prediction of wind speed based on long-short term memory networks
title_sort	short term prediction of wind speed based on long-short term memory networks
publisher	University of Belgrade - Faculty of Mechanical Engineering, Belgrade
publishDate	2021
url	https://doaj.org/article/ec308bb4f4ac4cd795513d6137b2a471
work_keys_str_mv	AT salmanumart shorttermpredictionofwindspeedbasedonlongshorttermmemorynetworks AT rehmanshafiqur shorttermpredictionofwindspeedbasedonlongshorttermmemorynetworks AT alawodebasit shorttermpredictionofwindspeedbasedonlongshorttermmemorynetworks AT alhemsluaim shorttermpredictionofwindspeedbasedonlongshorttermmemorynetworks
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Short term prediction of wind speed based on long-short term memory networks

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