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Wind Offshore Power Forecasting - A comparative analysis of forecasting accuracy between Machine Learning and classical Time Series models

Jankunas, Adomas LU and Poultourtzidou, Zografa LU (2024) DABN01 20241
Department of Economics
Department of Statistics
Abstract
This thesis investigated the prediction accuracy of offshore wind power using machine learning and classical time series methods. We used Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel along the Autoregressive (AR) model. Time series cross-validation with data from the Lillgrund Wind Farm and hyperparameter tuning were used to enhance the performance of both models. Throughout the forecasting horizons that were examined, the SVR model consistently outperformed the AR model as indicated by lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values. This was because offshore wind power data has inherent non-linearities. These results demonstrate how... (More)
This thesis investigated the prediction accuracy of offshore wind power using machine learning and classical time series methods. We used Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel along the Autoregressive (AR) model. Time series cross-validation with data from the Lillgrund Wind Farm and hyperparameter tuning were used to enhance the performance of both models. Throughout the forecasting horizons that were examined, the SVR model consistently outperformed the AR model as indicated by lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values. This was because offshore wind power data has inherent non-linearities. These results demonstrate how well-sophisticated machine learning approaches can identify complex patterns in offshore wind energy data, which might have a big impact on improving the reliability of wind power integration into energy grids. Future research should focus on adding more predictor variables, improving model robustness through various data sources, and investigating other machine learning techniques to further improve prediction accuracy. (Less)
Please use this url to cite or link to this publication:
author
Jankunas, Adomas LU and Poultourtzidou, Zografa LU
supervisor
organization
course
DABN01 20241
year
type
H1 - Master's Degree (One Year)
subject
keywords
Offshore Wind Power Forecasting, Machine Learning, Support Vector Regression, Autoregressive Model, Time Series Analysis
language
English
id
9166111
date added to LUP
2024-09-24 08:34:06
date last changed
2024-09-24 08:34:06
@misc{9166111,
  abstract     = {{This thesis investigated the prediction accuracy of offshore wind power using machine learning and classical time series methods. We used Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel along the Autoregressive (AR) model. Time series cross-validation with data from the Lillgrund Wind Farm and hyperparameter tuning were used to enhance the performance of both models. Throughout the forecasting horizons that were examined, the SVR model consistently outperformed the AR model as indicated by lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values. This was because offshore wind power data has inherent non-linearities. These results demonstrate how well-sophisticated machine learning approaches can identify complex patterns in offshore wind energy data, which might have a big impact on improving the reliability of wind power integration into energy grids. Future research should focus on adding more predictor variables, improving model robustness through various data sources, and investigating other machine learning techniques to further improve prediction accuracy.}},
  author       = {{Jankunas, Adomas and Poultourtzidou, Zografa}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Wind Offshore Power Forecasting - A comparative analysis of forecasting accuracy between Machine Learning and classical Time Series models}},
  year         = {{2024}},
}