Statistical learning for wind power : a modeling and stability study towards forecasting
We focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Ma{\"i}a Eolis, that parametric models, even following closely the physical equation relating wind production to wind speed are outperformed by intelligent learning algorithms. In particular, the CART-Bagging algorithm gives very stable and promising results. Besides, as a step towards forecast, we quantify the impact of using deteriorated wind measures on the performances. We show also on this application that the default methodology to select a subset of predictors provided in the standard random forest package can be refined, especially when there exists among the predictors one variable which has a major impact.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic
For the integration of renewable energy sources, power grid operators need realistic information about the effects of energy production and consumption to assess grid stability. Recently, research in scenario planning be…
Dynamic Modeling and Stability Analysis for Repeated LVRT Process of Wind Turbine Based on Switched System Theory
The significant electrical distance between wind power collection points and the main grid poses challenges for weak grid-connected wind power systems. A new type of voltage oscillation phenomenon induced by repeated low…
Analysis of the Implication of Current Limits in Grid Forming Wind Farm
There is an ongoing trend of reduction in short circuit power at the grid connection point due to decommissioning of synchronous generation plants causing system strength issues in wind power plants. Whereas wind power p…
Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network
Generating wind power scenarios is very important for studying the impacts of multiple wind farms that are interconnected to the grid. We develop a graph convolutional generative adversarial network (GCGAN) approach by l…
Generative Adversarial NetworkProbabilistic Wind Power Forecasting via Non-Stationary Gaussian Processes
Accurate probabilistic forecasting of wind power is essential for maintaining grid stability and enabling efficient integration of renewable energy sources. Gaussian Process (GP) models offer a principled framework for q…
Gaussian Processes