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As the power generation mode with the lowest carbon emissions, wind power generation plays an indispensable role in achieving the goal of carbon neutralization. To optimize the wind power density (WPD), forecasting is crucial to improve wind power utilization and power system stability. However, because near-surface wind is characterized by notable randomness, diversity, intermittence, and uncontrollability, accurately forecasting the WPD on wind farms remains a challenging task. In this study, we attempted to improve the WPD forecast in the middle- and high-latitude regions of China (wind energy resources are abundant there) by selecting the relatively optimal planetary boundary layer (PBL) scheme, as the PBL processes exert notable effects on the near-surface wind field directly. Based on a whole month in the summer (July 2021), seven PBL schemes were compared quantitatively by using the Weather Research and Forecasting (WRF) model for a total of 70 runs (for each run, the forecast period was 3 days). The results show that no PBL schemes could always show the best performance in forecasting all variables, and the forecast accuracy showed a notable dependence on the evolution of the weather systems. Among the seven PBL schemes, the Medium-Range Forecast (MRF) scheme showed the overall best performance in forecasting the 100 m wind speed, sea level pressure, and 2 m temperature, which ensured that it had the highest forecast skill for the WPD in the middle- and high-latitude regions of China. Further analyses indicate that the background conditions were also well forecasted by the MRF scheme (ranked first or second). This was a crucial reason why the WPD forecast was the best for the MRF scheme. [ABSTRACT FROM AUTHOR]

Wind energy is a type of renewable and clean energy which has attracted more and more attention all over the world. The Northwest China is a region with the most abundant wind energy not only in China, but also in the whole world. To achieve the goal of carbon neutralization, there is an urgent need to make full use of wind energy in Northwest China and to improve the efficiency of wind power generation systems in this region. As forecast accuracy of the near-surface wind is crucial to wind-generated electricity efficiency, improving the near-surface wind forecast is of great importance. This study conducted the first test to incorporate the subgrid surface drag into the near-surface wind forecast under the complex terrain conditions over Northwest China by using two TopoWind models added by newer versions of the Weather Research and Forecasting (WRF) model. Based on three groups (each group had 28 runs) of forecasts (i.e., Control run, Test 01 and Test 02) started at 12:00 UTC of each day (ran for 48 h) during the period of 1–28 October 2020, it was shown that, overall, both TopoWind models could improve the near-surface wind speed forecasts under the complex terrain conditions over Northwest China, particularly for reducing the errors associated with the forecast of the wind-speed's magnitude. In addition to wind forecast, the forecasts of sea level pressure and 2-m temperature were also improved. Different geographical features (wind-farm stations located south of the mountain tended to have more accurate forecast) and weather systems were found to be crucial to forecast accuracy. Good forecasts tended to appear when the simulation domain was mainly controlled by the high-pressure systems with the upper-level jet far from it. [ABSTRACT FROM AUTHOR]

• A novel combined model based on transfer learning is proposed for wind speed forecasting in different data features and scenarios. • MVMD is used to decompose the multivariate series with the intention of considering the internal coupling relationship between wind speed and meteorological series. • TDCN-BiLSTM model is established for alleviating the interference between data, extracting multi-scales characteristics. • The IMOGOA is developed to improve the optimization ability of the algorithm. With the establishment of remote centralized control centers for wind farms, the combined model of multiple deep neural networks used in most wind speed prediction methods can no longer meet the requirements of centralized control centers for efficient and low-cost wind speed prediction in different locations. Thus, a model based on multi-layer feature fusion and transfer learning is proposed in this paper for multi-location wind speed forecasting. Under the idea of "offline training and online prediction", the wind speed characteristics of wind farms at typical locations are learned in the offline stage by fusing multifaceted features through a two-channel dilated convolutional network and a bi-directional long short-term memory network, transferring the well-trained models to any target wind farm to achieve online prediction, and integrating the prediction results of each offline model with an improved multi-objective grasshopper optimization algorithm to further enhance the prediction accuracy. Finally, three comparisons reveal that the proposed model outperforms other baseline models in adaptability and accuracy. [ABSTRACT FROM AUTHOR]