A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations
Accurate wind power forecasts highly contribute to the integration of wind power into power systems. The focus of the present study is on large-scale offshore wind farms and the complexity of generating accurate probabilistic forecasts of wind power fluctuations at time-scales of a few minutes. Such...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2012-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/5/3/621/ |
| _version_ | 1811186404799021056 |
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| author | Henrik Madsen Pierre Pinson Pierre-Julien Trombe |
| author_facet | Henrik Madsen Pierre Pinson Pierre-Julien Trombe |
| author_sort | Henrik Madsen |
| collection | DOAJ |
| description | Accurate wind power forecasts highly contribute to the integration of wind power into power systems. The focus of the present study is on large-scale offshore wind farms and the complexity of generating accurate probabilistic forecasts of wind power fluctuations at time-scales of a few minutes. Such complexity is addressed from three perspectives: (i) the modeling of a nonlinear and non-stationary stochastic process; (ii) the practical implementation of the model we proposed; (iii) the gap between working on synthetic data and real world observations. At time-scales of a few minutes, offshore fluctuations are characterized by highly volatile dynamics which are difficult to capture and predict. Due to the lack of adequate on-site meteorological observations to relate these dynamics to meteorological phenomena, we propose a general model formulation based on a statistical approach and historical wind power measurements only. We introduce an advanced Markov Chain Monte Carlo (MCMC) estimation method to account for the different features observed in an empirical time series of wind power: autocorrelation, heteroscedasticity and regime-switching. The model we propose is an extension of Markov-Switching Autoregressive (MSAR) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors in each regime to cope with the heteroscedasticity. Then, we analyze the predictive power of our model on a one-step ahead exercise of time series sampled over 10 min intervals. Its performances are compared to state-of-the-art models and highlight the interest of including a GARCH specification for density forecasts. |
| first_indexed | 2024-04-11T13:46:00Z |
| format | Article |
| id | doaj.art-2bed9684e3384f7d98993c5871606228 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T13:46:00Z |
| publishDate | 2012-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2bed9684e3384f7d98993c58716062282022-12-22T04:21:05ZengMDPI AGEnergies1996-10732012-03-015362165710.3390/en5030621A General Probabilistic Forecasting Framework for Offshore Wind Power FluctuationsHenrik MadsenPierre PinsonPierre-Julien TrombeAccurate wind power forecasts highly contribute to the integration of wind power into power systems. The focus of the present study is on large-scale offshore wind farms and the complexity of generating accurate probabilistic forecasts of wind power fluctuations at time-scales of a few minutes. Such complexity is addressed from three perspectives: (i) the modeling of a nonlinear and non-stationary stochastic process; (ii) the practical implementation of the model we proposed; (iii) the gap between working on synthetic data and real world observations. At time-scales of a few minutes, offshore fluctuations are characterized by highly volatile dynamics which are difficult to capture and predict. Due to the lack of adequate on-site meteorological observations to relate these dynamics to meteorological phenomena, we propose a general model formulation based on a statistical approach and historical wind power measurements only. We introduce an advanced Markov Chain Monte Carlo (MCMC) estimation method to account for the different features observed in an empirical time series of wind power: autocorrelation, heteroscedasticity and regime-switching. The model we propose is an extension of Markov-Switching Autoregressive (MSAR) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors in each regime to cope with the heteroscedasticity. Then, we analyze the predictive power of our model on a one-step ahead exercise of time series sampled over 10 min intervals. Its performances are compared to state-of-the-art models and highlight the interest of including a GARCH specification for density forecasts.http://www.mdpi.com/1996-1073/5/3/621/wind energyoffshoreforecastingMarkov-SwitchingGARCHprobabilistic forecastingMCMCGriddy-Gibbs |
| spellingShingle | Henrik Madsen Pierre Pinson Pierre-Julien Trombe A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations Energies wind energy offshore forecasting Markov-Switching GARCH probabilistic forecasting MCMC Griddy-Gibbs |
| title | A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations |
| title_full | A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations |
| title_fullStr | A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations |
| title_full_unstemmed | A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations |
| title_short | A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations |
| title_sort | general probabilistic forecasting framework for offshore wind power fluctuations |
| topic | wind energy offshore forecasting Markov-Switching GARCH probabilistic forecasting MCMC Griddy-Gibbs |
| url | http://www.mdpi.com/1996-1073/5/3/621/ |
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