Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms
The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 24 h play an importan...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/4/1252 |
| _version_ | 1797480789401665536 |
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| author | Paweł Piotrowski Dariusz Baczyński Marcin Kopyt Tomasz Gulczyński |
| author_facet | Paweł Piotrowski Dariusz Baczyński Marcin Kopyt Tomasz Gulczyński |
| author_sort | Paweł Piotrowski |
| collection | DOAJ |
| description | The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 24 h play an important role in this process. They are also used in energy market transactions. Even a small improvement in the quality of these forecasts translates into more security of the system and savings for the economy. Using two wind farms for statistical analyses and forecasting considerably increases credibility of newly created effective prediction methods and formulated conclusions. In the first part of our study, we have analysed the available data to identify potentially useful explanatory variables for forecasting models with additional development of new input data based on the basic data set. We demonstrate that it is better to use Numerical Weather Prediction (NWP) point forecasts for hourly lags: −3, 2, −1, 0, 1, 2, 3 (original contribution) as input data than lags 0, 1 that are typically used. Also, we prove that it is better to use forecasts from two NWP models as input data. Ensemble, hybrid and single methods are used for predictions, including machine learning (ML) solutions like Gradient-Boosted Trees (GBT), Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), K-Nearest Neighbours Regression (KNNR) and Support Vector Regression (SVR). Original ensemble methods, developed for researching specific implementations, have reduced errors of forecast energy generation for both wind farms as compared to single methods. Predictions by the original ensemble forecasting method, called “Ensemble Averaging Without Extremes” have the lowest normalized mean absolute error (nMAE) among all tested methods. A new, original “Additional Expert Correction” additionally reduces errors of energy generation forecasts for both wind farms. The proposed ensemble methods are also applicable to short-time generation forecasting for other renewable energy sources (RES), e.g., hydropower or photovoltaic (PV) systems. |
| first_indexed | 2024-03-09T22:05:09Z |
| format | Article |
| id | doaj.art-0b82d4d9161d4af5861e321d0689d649 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T22:05:09Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0b82d4d9161d4af5861e321d0689d6492023-11-23T19:41:06ZengMDPI AGEnergies1996-10732022-02-01154125210.3390/en15041252Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind FarmsPaweł Piotrowski0Dariusz Baczyński1Marcin Kopyt2Tomasz Gulczyński3Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, PolandElectrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, PolandElectrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, PolandGlobema Sp. z o. o., Wita Stwosza 22 Street, 02-661 Warsaw, PolandThe ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 24 h play an important role in this process. They are also used in energy market transactions. Even a small improvement in the quality of these forecasts translates into more security of the system and savings for the economy. Using two wind farms for statistical analyses and forecasting considerably increases credibility of newly created effective prediction methods and formulated conclusions. In the first part of our study, we have analysed the available data to identify potentially useful explanatory variables for forecasting models with additional development of new input data based on the basic data set. We demonstrate that it is better to use Numerical Weather Prediction (NWP) point forecasts for hourly lags: −3, 2, −1, 0, 1, 2, 3 (original contribution) as input data than lags 0, 1 that are typically used. Also, we prove that it is better to use forecasts from two NWP models as input data. Ensemble, hybrid and single methods are used for predictions, including machine learning (ML) solutions like Gradient-Boosted Trees (GBT), Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), K-Nearest Neighbours Regression (KNNR) and Support Vector Regression (SVR). Original ensemble methods, developed for researching specific implementations, have reduced errors of forecast energy generation for both wind farms as compared to single methods. Predictions by the original ensemble forecasting method, called “Ensemble Averaging Without Extremes” have the lowest normalized mean absolute error (nMAE) among all tested methods. A new, original “Additional Expert Correction” additionally reduces errors of energy generation forecasts for both wind farms. The proposed ensemble methods are also applicable to short-time generation forecasting for other renewable energy sources (RES), e.g., hydropower or photovoltaic (PV) systems.https://www.mdpi.com/1996-1073/15/4/1252wind energywind farmensemble methodsshort-term forecastingelectric energy productionmachine learning |
| spellingShingle | Paweł Piotrowski Dariusz Baczyński Marcin Kopyt Tomasz Gulczyński Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms Energies wind energy wind farm ensemble methods short-term forecasting electric energy production machine learning |
| title | Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms |
| title_full | Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms |
| title_fullStr | Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms |
| title_full_unstemmed | Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms |
| title_short | Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms |
| title_sort | advanced ensemble methods using machine learning and deep learning for one day ahead forecasts of electric energy production in wind farms |
| topic | wind energy wind farm ensemble methods short-term forecasting electric energy production machine learning |
| url | https://www.mdpi.com/1996-1073/15/4/1252 |
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