**1. Introduction**

Wind energy, with its vast availability, cleanliness, and renewability, is growing rapidly in the energy share, and plays an increasingly important role in the electric energy sector [1]. However, the intermittent and volatile nature of wind speeds poses a great challenge to the grid-connected transmission of wind power output, threatening the security of the grid

**Citation:** Shi, J.; Liu, Y.; Li, Y.; Liu, Y.; Roux, G.; Shi, L.; Fan, X. Wind Speed Forecasts of a Mesoscale Ensemble for Large-Scale Wind Farms in Northern China: Downscaling Effect of Global Model Forecasts. *Energies* **2022**, *15*, 896. https://doi.org/ 10.3390/en15030896

Academic Editors: Marcin Kami ´nski and Andrés Elías Feijóo Lorenzo

Received: 7 December 2021 Accepted: 25 January 2022 Published: 26 January 2022

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system and sometimes leading to massive wind abandonment [2]. Reliable wind power forecasting is urgently needed for timely and accurate dispatch of power resources [3,4]. Wind speed forecasting methods include statistical approaches, machine learning methods [5–11], and numerical weather prediction [12]. There have been many works on wind prediction reported in the past two decades, especially over the last few years. However, most of these works are on the refinement of statistical and AI approaches [13–18]; there have been very few studies examining and analyzing the errors of numerical weather models. As a matter of fact, for wind forecasts beyond ~1 h, numerical weather prediction models become essential and fundamental. Improving the performance and capability of numerical weather prediction models and machine learning post-processing for wind farm weather prediction is therefore critical.

The performance of numerical weather models relies greatly on model resolutions [19] and regional climates [20], topography [21], underlying land-surface and soil properties [20], weather measurements [22] and data assimilation schemes for model initiation [23], as well as the lateral boundary conditions for limited-area models [24]. For these reasons, many studies and energy forecasting firms use an ensemble of global and regional NWP outputs to reduce forecast errors [25,26].

There are three main error sources in numerical weather forecasting: uncertainties in initial values [27], approximation of the dynamical and physical models [28], and the intrinsic unpredictability of atmospheric motions [29]. Ensemble numerical weather prediction methods [30,31] have been used to improve the accuracy and reliability of weather forecasts through probabilistic forecasts. Probabilistic forecasts and uncertainty quantification are beginning to take the place of single numerical forecasts in the wind energy industry. An ensemble forecast system can simulate the impact of the uncertainties of initial and boundary conditions derived from different global model forecasts, atmospheric physical parameterization schemes, and data assimilation modules. Perturbation members of a mesoscale ensemble forecast system include sub-grid energy stochastic perturbation members, physical parameterization perturbation members, initial and boundary value perturbation members, and some others. Analyzing the error characteristics of ensemble forecast members is important for exploring the value of ensemble forecast outputs and improving the ensemble forecast system.

With respect to model forecast verification, several researchers have explored the effects of model physical processes on wind speed forecasting [32–36]. Different physical parameterization schemes often present different forecast capabilities under different meteorological conditions or regimes [24,37,38], different geographical regions [39–42], and/or different topographic environments [43]. In responding to atmospheric long- and short-wave radiative forcing, model forecast errors often exhibit diurnal and seasonal variations [44–47]. Some other researchers focused on revising model forecast results through post-processing by using statistical and machine learning methods [48]. However, the errors of the model initial and boundary conditions derived from different global model background fields are often large [49,50], but very little attention has been paid to this issue [51]. In fact, we could not find any report investigating the impact of model initial and boundary conditions of the wind farm wind forecasting based on a 2–4 km grid high-resolution ensemble numerical weather prediction model.

The wind energy density in the Inner Mongolia Autonomous Region, China, is outstanding—over 400 W/m<sup>2</sup> in some regions [52]. In 2019, wind power generation in Inner Mongolia was 66.6 billion kWh, accounting for ~16.4% of China's total wind power generation in the same period (China National Energy Administration). In response to the demand for wind power integration in Inner Mongolia, the Inner Mongolia Electric Power Company (IMEPC) has developed a mesoscale ensemble numerical weather prediction system that is composed of 39 perturbed WRF (Weather Research and Forecasting) forecast members. The system is constructed with multiple global models of forcing, multiple physical parameterization schemes, and stochastic kinetic energy perturbations. The 39 forecast members contain three subgroups of 13 physical perturbation members, driven by the forecasts of the GFS (Global Forecast System), GEM (Global Environmental Multiscale Model), and GEOS (Goddard Earth Observing System), respectively.

This paper evaluates the output of the IMEPC mesoscale ensemble prediction system, focusing on its hub-height wind prediction for the wind farms distributed across the Inner Mongolia Autonomous Region during the spring of 2020. The model performance of three sub-ensemble groups driven by the forecasts of the GFS, GEOS, and GEM global models was studied, and the variations in the forecast errors with forecast lead time, wind speed regimes, diurnal forcing, and regional changes were analyzed. The findings of this study provide guidance for the proper use of the ensemble prediction system at the wind farms, and for the development of model forecast post-processing capabilities by the IMEPC. Our results also support modelers to improve the ensemble model system by adjusting the ensemble members according to the error properties of the ensemble members driven by different global model forecasts.

The remainder of this paper is organized as follows: Section 2 describes the observations in the study area and the setup of the ensemble forecast system used for the numerical experiments. Section 3 presents the results of the wind speed forecast error analysis. Section 4 presents the conclusions from these analyses. Finally, Section 5 discusses the limitations of the present work, and describes the outlook for the future.
