Short-term wind power prediction based on Markov chain and numerical weather prediction models: A case study of Fakken wind farm

Rising energy demands and a growing focus on sustainable development have made electricity production from wind energy an attractive alternative to fossil fuels. However the natural variability of wind makes it challenging to implement wind energy into the electrical grid. Accurate and reliable wind...

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Bibliographic Details
Main Author: Jacobsen, Morten
Format: Master Thesis
Language:English
Published: UiT The Arctic University of Norway 2014
Subjects:
NWP
Online Access:https://hdl.handle.net/10037/6791
id ftunivtroemsoe:oai:munin.uit.no:10037/6791
record_format openpolar
spelling ftunivtroemsoe:oai:munin.uit.no:10037/6791 2023-05-15T15:25:21+02:00 Short-term wind power prediction based on Markov chain and numerical weather prediction models: A case study of Fakken wind farm Jacobsen, Morten 2014-06-02 https://hdl.handle.net/10037/6791 eng eng UiT The Arctic University of Norway UiT Norges arktiske universitet https://hdl.handle.net/10037/6791 URN:NBN:no-uit_munin_6389 openAccess Copyright 2014 The Author(s) VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 VDP::Technology: 500::Electrotechnical disciplines: 540::Electrical power engineering: 542 VDP::Teknologi: 500::Elektrotekniske fag: 540::Elkraft: 542 Short-term prediction Wind power prediction Wind power forecast Fakken wind farm Markov chain Numerical weather prediction model NWP Wind energy Wind power EOM-3901 Master thesis Mastergradsoppgave 2014 ftunivtroemsoe 2021-06-25T17:54:03Z Rising energy demands and a growing focus on sustainable development have made electricity production from wind energy an attractive alternative to fossil fuels. However the natural variability of wind makes it challenging to implement wind energy into the electrical grid. Accurate and reliable wind power predictions are seen as a key element for an increased penetration of wind energy. This study presents a set of statistical power prediction models using the concept of Markov chains, based on various input parameters, such as wind speed, direction and power output. The models have been trained and tested using numerical weather predictions and historical data obtained from a meteorological station and wind turbine at Fakken wind farm in the time period 2. May 2013 - 31. March 2014. Several of the models were found to have lower NRMSE than the currently used persistent model (19.08 %), with the best performing model having a NRMSE of 16.84 %. This 2.25 % lower NRMSE corresponds to approximately 3 100 000 kWh of the anually electricity production from Fakken wind farm. A statistical analysis of Fakken wind farm showed the majority of winds occurring from the straits between Arnøya and Lenangsøyra to the southeast and between Reinøya and Lenangsøyra to the south. Winds were also commonly seen from southwest and to the northwest, while eastern and northeastern winds were rarely observed. Westerly winds were found to be much more tubulent than other directions, with a generally lower power output observed. This is most likely due to the occurerence of mountain waves for winds crossing the mountain range to the west. Master Thesis Arnøya Lenangsøyra University of Tromsø: Munin Open Research Archive Fakken ENVELOPE(20.114,20.114,70.104,70.104) Lenangsøyra ENVELOPE(19.981,19.981,69.813,69.813) Reinøya ENVELOPE(15.223,15.223,68.304,68.304)
institution Open Polar
collection University of Tromsø: Munin Open Research Archive
op_collection_id ftunivtroemsoe
language English
topic VDP::Mathematics and natural science: 400::Physics: 430
VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430
VDP::Technology: 500::Electrotechnical disciplines: 540::Electrical power engineering: 542
VDP::Teknologi: 500::Elektrotekniske fag: 540::Elkraft: 542
Short-term prediction
Wind power prediction
Wind power forecast
Fakken wind farm
Markov chain
Numerical weather prediction model
NWP
Wind energy
Wind power
EOM-3901
spellingShingle VDP::Mathematics and natural science: 400::Physics: 430
VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430
VDP::Technology: 500::Electrotechnical disciplines: 540::Electrical power engineering: 542
VDP::Teknologi: 500::Elektrotekniske fag: 540::Elkraft: 542
Short-term prediction
Wind power prediction
Wind power forecast
Fakken wind farm
Markov chain
Numerical weather prediction model
NWP
Wind energy
Wind power
EOM-3901
Jacobsen, Morten
Short-term wind power prediction based on Markov chain and numerical weather prediction models: A case study of Fakken wind farm
topic_facet VDP::Mathematics and natural science: 400::Physics: 430
VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430
VDP::Technology: 500::Electrotechnical disciplines: 540::Electrical power engineering: 542
VDP::Teknologi: 500::Elektrotekniske fag: 540::Elkraft: 542
Short-term prediction
Wind power prediction
Wind power forecast
Fakken wind farm
Markov chain
Numerical weather prediction model
NWP
Wind energy
Wind power
EOM-3901
description Rising energy demands and a growing focus on sustainable development have made electricity production from wind energy an attractive alternative to fossil fuels. However the natural variability of wind makes it challenging to implement wind energy into the electrical grid. Accurate and reliable wind power predictions are seen as a key element for an increased penetration of wind energy. This study presents a set of statistical power prediction models using the concept of Markov chains, based on various input parameters, such as wind speed, direction and power output. The models have been trained and tested using numerical weather predictions and historical data obtained from a meteorological station and wind turbine at Fakken wind farm in the time period 2. May 2013 - 31. March 2014. Several of the models were found to have lower NRMSE than the currently used persistent model (19.08 %), with the best performing model having a NRMSE of 16.84 %. This 2.25 % lower NRMSE corresponds to approximately 3 100 000 kWh of the anually electricity production from Fakken wind farm. A statistical analysis of Fakken wind farm showed the majority of winds occurring from the straits between Arnøya and Lenangsøyra to the southeast and between Reinøya and Lenangsøyra to the south. Winds were also commonly seen from southwest and to the northwest, while eastern and northeastern winds were rarely observed. Westerly winds were found to be much more tubulent than other directions, with a generally lower power output observed. This is most likely due to the occurerence of mountain waves for winds crossing the mountain range to the west.
format Master Thesis
author Jacobsen, Morten
author_facet Jacobsen, Morten
author_sort Jacobsen, Morten
title Short-term wind power prediction based on Markov chain and numerical weather prediction models: A case study of Fakken wind farm
title_short Short-term wind power prediction based on Markov chain and numerical weather prediction models: A case study of Fakken wind farm
title_full Short-term wind power prediction based on Markov chain and numerical weather prediction models: A case study of Fakken wind farm
title_fullStr Short-term wind power prediction based on Markov chain and numerical weather prediction models: A case study of Fakken wind farm
title_full_unstemmed Short-term wind power prediction based on Markov chain and numerical weather prediction models: A case study of Fakken wind farm
title_sort short-term wind power prediction based on markov chain and numerical weather prediction models: a case study of fakken wind farm
publisher UiT The Arctic University of Norway
publishDate 2014
url https://hdl.handle.net/10037/6791
long_lat ENVELOPE(20.114,20.114,70.104,70.104)
ENVELOPE(19.981,19.981,69.813,69.813)
ENVELOPE(15.223,15.223,68.304,68.304)
geographic Fakken
Lenangsøyra
Reinøya
geographic_facet Fakken
Lenangsøyra
Reinøya
genre Arnøya
Lenangsøyra
genre_facet Arnøya
Lenangsøyra
op_relation https://hdl.handle.net/10037/6791
URN:NBN:no-uit_munin_6389
op_rights openAccess
Copyright 2014 The Author(s)
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