A multi-scale model for day-ahead wind speed forecasting: A case study of the Houhoku wind farm, Japan

Abstract

Accurate day-ahead wind prediction from numerical weather prediction (NWP) models is crucial for integrating wind power into existing grids and wind farm arrangements. Most wind farms in Japan are scattered in mountainous areas susceptible to strong winds and wind patterns that are highly fluctuating and cannot be captured well with mesoscale NWP models. Aiming to provide reliable day-ahead forecasts in rugged zones with complex topography, we introduce a multi-scale model based on coupling the Weather Research Forecasting (WRF) model and the Open Source Field Operation and Manipulation (OpenFOAM) Computational Fluid Dynamics model. The WRF’s predictions are used as initial/boundary conditions for a microscale simulation using OpenFOAM. This coupled model can overcome the shortcomings when employing each component independently. Numerical simulations and validations are carried out at a wind farm in Houhoku, Japan, during summer and winter. The multi-scale model results show a considerable reduction in the bias, mean absolute error, and root mean square error compared to the WRF-alone forecasts. It also has a superior skill against most post-processing algorithms or artificial intelligence techniques to reduce random forecasting errors. In addition, analysis of computational cost demonstrates the promising capability of this multi-scale model being a routine tool.