Wind Power Forecasting techniques in complex terrain: ANN vs. ANN-CFD hybrid approach

The Science of Making Torque from Wind

Castellani, Francesco^1 ; Astolfi, Davide^1 ; Mana, Matteo^1 ; Burlando, Massimiliano^2 ; Meißner, Cathérine^3 ; Piccioni, Emanuele^1

University of Perugia, Department of Engineering, Via G. Duranti 93, Perugia

06125, Italy^1

University of Genoa, Department of Civil, Chemical and Environmental Engineering, Via Montallegro 1, Genoa

16145, Italy^2

WindSim AS, Fjordgaten 15, Tønsberg

N-3125, Norway^3

关键词: Physical methods;    Power forecasting;    Renewable energies;    Technology development;    Very complex terrain;    Wind power distribution;    Wind power forecast;    Wind power forecasting;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/753/8/082002/pdf
DOI  :  10.1088/1742-6596/753/8/082002

来源: IOP

【 摘 要 】

Due to technology developments, renewable energies are becoming competitive against fossil sources and the number of wind farms is growing, which have to be integrated into power grids. Therefore, accurate power forecast is needed and often operators are charged with penalties in case of imbalance. Yet, wind is a stochastic and very local phenomenon, and therefore hard to predict. It has a high variability in space and time and wind power forecast is challenging. Statistical methods, as Artificial Neural Networks (ANN), are often employed for power forecasting, but they have some shortcomings: they require data sets over several years and are not able to capture tails of wind power distributions. In this work a pure ANN power forecast is compared against a hybrid method, based on the combination of ANN and a physical method using computational fluid dynamics (CFD). The validation case is a wind farm sited in southern Italy in a very complex terrain, with a wide spread turbine layout.