Wind power forecasting using random forests
(2023) MVKM01 20232Department of Energy Sciences
- Abstract
- The present thesis investigated using the random forest machine learning algorithm for
wind power forecasting. Meteorological prognoses for wind speed, wind direction, gust
winds, and humidity were used. For historical data, wind minimum and temperature was
also included. The results were evaluated using root means square error (RMSE), mean
absolute error (MAE), normalized mean absolute error (NMAE), and normalized mean
biased error (NMBE). Results from the ’Hans’ storm in 2023 were shown separately.
Historical data covered a year and three months. Meteorological prognoses covered a
month. Historical data was used to show that seasonality impacts forecasting.
The best results showed NMAE decreasing (compared to a simple polynomial... (More) - The present thesis investigated using the random forest machine learning algorithm for
wind power forecasting. Meteorological prognoses for wind speed, wind direction, gust
winds, and humidity were used. For historical data, wind minimum and temperature was
also included. The results were evaluated using root means square error (RMSE), mean
absolute error (MAE), normalized mean absolute error (NMAE), and normalized mean
biased error (NMBE). Results from the ’Hans’ storm in 2023 were shown separately.
Historical data covered a year and three months. Meteorological prognoses covered a
month. Historical data was used to show that seasonality impacts forecasting.
The best results showed NMAE decreasing (compared to a simple polynomial model)
from 8.5 % to 6.6 % using historical data, from 9.5 % to 8.6 % using meteorlogical
prognoses, and negligible improvements under storm conditions. The results indicate
that a random forest model can yield improvements in wind power forecasting. It is
simultaneously shown to be important that the models are based on good data and employ
good meteorological prognoses with high levels of agreement between them, and with
the turbine site. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9140595
- author
- Ahrling, Christoffer LU
- supervisor
- organization
- course
- MVKM01 20232
- year
- 2023
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- machine learning, wind power, forecasting, random forest
- report number
- LUTMDN/TMHP-23/5552-SE
- ISSN
- 0282-1990
- language
- English
- id
- 9140595
- date added to LUP
- 2023-10-30 16:03:15
- date last changed
- 2023-10-30 16:03:15
@misc{9140595, abstract = {{The present thesis investigated using the random forest machine learning algorithm for wind power forecasting. Meteorological prognoses for wind speed, wind direction, gust winds, and humidity were used. For historical data, wind minimum and temperature was also included. The results were evaluated using root means square error (RMSE), mean absolute error (MAE), normalized mean absolute error (NMAE), and normalized mean biased error (NMBE). Results from the ’Hans’ storm in 2023 were shown separately. Historical data covered a year and three months. Meteorological prognoses covered a month. Historical data was used to show that seasonality impacts forecasting. The best results showed NMAE decreasing (compared to a simple polynomial model) from 8.5 % to 6.6 % using historical data, from 9.5 % to 8.6 % using meteorlogical prognoses, and negligible improvements under storm conditions. The results indicate that a random forest model can yield improvements in wind power forecasting. It is simultaneously shown to be important that the models are based on good data and employ good meteorological prognoses with high levels of agreement between them, and with the turbine site.}}, author = {{Ahrling, Christoffer}}, issn = {{0282-1990}}, language = {{eng}}, note = {{Student Paper}}, title = {{Wind power forecasting using random forests}}, year = {{2023}}, }