Open AccessLeveraging stochastic differential equations for probabilistic forecasting of wind power using a dynamic power curve
Author(s) -
Iversen Emil B.,
Morales Juan M.,
Møller Jan K.,
Trombe PierreJulien,
Madsen Henrik
Publication year - 2017
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.1988
Subject(s) - stochastic differential equation , probabilistic logic , wind power , power (physics) , wind power forecasting , differential (mechanical device) , mathematics , computer science , control theory (sociology) , mathematical optimization , electric power system , econometrics , engineering , physics , statistics , aerospace engineering , artificial intelligence , electrical engineering , quantum mechanics , control (management)
Short‐term (hours to days) probabilistic forecasts of wind power generation provide useful information about the associated uncertainty of these forecasts. Standard probabilistic forecasts are usually issued on a per‐horizon‐basis, meaning that they lack information about the development of the uncertainty over time or the inter‐temporal correlation of forecast errors for different horizons. This information is very important for forecast end‐users optimizing time‐dependent variables or dealing with multi‐period decision‐making problems, such as the management and operation of power systems with a high penetration of renewable generation. This paper provides input to these problems by proposing a model based on stochastic differential equations that allows generating predictive densities as well as scenarios for wind power. We build upon a probabilistic model for wind speed and introduce a dynamic power curve. The model thus decomposes the dynamics of wind power prediction errors into wind speed forecast errors and errors related to the conversion from wind speed to wind power. We test the proposed model on an out‐of‐sample period of 1year for a wind farm with a rated capacity of 21MW. The model outperforms simple as well as advanced benchmarks on horizons ranging from 1 to 24h. Copyright © 2016 John Wiley & Sons, Ltd.