Next Article in Journal
Measurement and Evaluation of Heating Performance of Heat Pump Systems Using Wasted Heat from Electric Devices for an Electric Bus
Previous Article in Journal
Life Cycle Assessment of Environmental and Economic Impacts of Advanced Vehicles
Energies 2012, 5(3), 621-657; doi:10.3390/en5030621
Article

A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations

* ,
 and
Received: 9 January 2012; in revised form: 28 February 2012 / Accepted: 29 February 2012 / Published: 7 March 2012
Download PDF [1144 KB, uploaded 7 March 2012]
Abstract: 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.
Keywords: wind energy; offshore; forecasting; Markov-Switching; GARCH; probabilistic forecasting; MCMC; Griddy-Gibbs wind energy; offshore; forecasting; Markov-Switching; GARCH; probabilistic forecasting; MCMC; Griddy-Gibbs
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Trombe, P.-J.; Pinson, P.; Madsen, H. A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations. Energies 2012, 5, 621-657.

AMA Style

Trombe P-J, Pinson P, Madsen H. A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations. Energies. 2012; 5(3):621-657.

Chicago/Turabian Style

Trombe, Pierre-Julien; Pinson, Pierre; Madsen, Henrik. 2012. "A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations." Energies 5, no. 3: 621-657.


Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert