An assessment of high-resolution wind speeds downscaled with the Weather Research and Forecasting Model for coastal areas in Ghana

Abstract

Ghana produces over 50% of its electrical energy demands from fossil-fuelled thermal plants. To increase the proportion of renewable energy in the national energy generation, a Renewable Energy Master Plan (REMP) which seeks, among others, to shift the country's national energy generation capacity towards more renewable energy sources has been developed. The REMP noted that inadequate data on renewable energy sources such as wind is one of the challenges to achieving this target. In this regard, this paper assessed the open-source Weather Research and Forecasting Model, as a tool for generating wind resource data. The WRF model is often used to downscale meteorological datasets for wind resources assessments. However, due to diverse model options, performance assessments are required to establish the accuracy and suitability of a model configuration for an application in an area. This paper assessed the performance of a Weather Research and Forecasting Model configuration that is based on previous verification studies. In evaluation, data accuracy benchmarks were generally met by the downscaled wind data. A wind map that was generated was observed to be generally accurate and better than the previous 2001 wind map for Ghana. It is presumed that the configuration is suitable for wind mapping activities for the coastal areas in Ghana, and probably neighbouring countries. However, for downscaling time-series data, further studies are recommended.

An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi

“…To accurately simulate the wind resource in the study area, we carefully selected specific combinations of WRF parameters. These choices were based on valuable insights gathered from previous research conducted in multiple regions within SSA (Details are depicted in Table 1) [55][56][57]. Twelve simulations were conducted to evaluate the applicability of the WRF model over Burundi (see Table 2).…”

“…The WRF's ability to generate precise wind data is influenced by the specific topography and climate of different regions, resulting in variations in accuracy between low and high-altitude areas [55]. Furthermore, as stated earlier, the data collected on-site from the Gisozi, Gitega, and Mpota stations has been extrapolated from a height of 2 m to 12 m. This extrapolation may have implications for the accuracy of the WRF outputs.…”

A novel Bayesian ensembling model for wind power forecasting

“…Physical methods mainly adopt topographic features and meteorological factors as input variables to predict the trend of future wind power, including the fifth-generation mesoscale model (MM5) [ 3 ], numerical weather prediction (NWP) model [ 4 , 5 , 6 ] and computational fluid dynamics (CFD) model [ 7 ], etc. For the reason that there is no need for massive historical data, these methods have an advantage in long-term prediction.…”