WaveHiTS: Wavelet-Enhanced Hierarchical Time Series Modeling for Wind Direction Nowcasting in Eastern Inner Mongolia

Wind direction forecasting plays a crucial role in optimizing wind energy production, but faces significant challenges due to the circular nature of directional data, error accumulation in multi-step forecasting, and complex meteorological interactions. This paper presents a novel model, WaveHiTS, which integrates wavelet transform with Neural Hierarchical Interpolation for Time Series to address these challenges. Our approach decomposes wind direction into U-V components, applies wavelet transform to capture multi-scale frequency patterns, and utilizes a hierarchical structure to model temporal dependencies at multiple scales, effectively mitigating error propagation. Experiments conducted on real-world meteorological data from Inner Mongolia, China demonstrate that WaveHiTS significantly outperforms deep learning models (RNN, LSTM, GRU), transformer-based approaches (TFT, Informer, iTransformer), and hybrid models (EMD-LSTM). The proposed model achieves RMSE values of approximately 19.2{\deg}-19.4{\deg} compared to 56{\deg}-64{\deg} for deep learning recurrent models, maintaining consistent accuracy across all forecasting steps up to 60 minutes ahead. Moreover, WaveHiTS demonstrates superior robustness with vector correlation coefficients (VCC) of 0.985-0.987 and hit rates of 88.5%-90.1%, substantially outperforming baseline models. Ablation studies confirm that each component-wavelet transform, hierarchical structure, and U-V decomposition-contributes meaningfully to overall performance. These improvements in wind direction nowcasting have significant implications for enhancing wind turbine yaw control efficiency and grid integration of wind energy.

Forecasting the Future with Future Technologies: Advancements in Large Meteorological Models

The field of meteorological forecasting has undergone a significant transformation with the integration of large models, especially those employing deep learning techniques. This paper reviews the advancements and applications of these models in weather prediction, emphasizing their role in transforming traditional forecasting methods. Models like FourCastNet, Pangu-Weather, GraphCast, ClimaX, and FengWu have made notable contributions by providing accurate, high-resolution forecasts, surpassing the capabilities of traditional Numerical Weather Prediction (NWP) models. These models utilize advanced neural network architectures, such as Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Transformers, to process diverse meteorological data, enhancing predictive accuracy across various time scales and spatial resolutions. The paper addresses challenges in this domain, including data acquisition and computational demands, and explores future opportunities for model optimization and hardware advancements. It underscores the integration of artificial intelligence with conventional meteorological techniques, promising improved weather prediction accuracy and a significant contribution to addressing climate-related challenges. This synergy positions large models as pivotal in the evolving landscape of meteorological forecasting.

Multistep prediction for short-term wind speed based on the MLP and LSTM method with rankpooling

The actual wind speed data suffers from the intermittent and fluctuating property, which implies that it is very difficult to forecast wind speed with high accuracy by applying single or shallow models. Hence, with the purpose of improving the forecasting accuracy and obtain better forecasting results, in this paper, a novel hybrid deep learning model is proposed for multistep forecasting of wind speed, which is intuitively abbreviated as LR-FFT-RP-LSTM and LR-FFT-RP-LSTM. Under these formulated model, the rankpooling method is firstly presented to extract local features of the raw meteorological data, and the Fast Fourier Transformation (FFT) is adopted to extract local and global features of the raw meteorological data to obtain pre-processed data, and the data obtained is then integrated with the original data using the two procedures to produce two input datasets. Then, deep learning model named multi-layer perceptron method (MLP) and long short-term memory (LSTM) are adopted to predict the wind speed dataset. The target prediction results are then obtained by integrating the preliminary prediction findings using the linear regression method.Practical wind speed data from 2010 to 2020 are exploited to evaluate the performance of the proposed model. Case study results indicate that the proposed model for wind speed has a superior forecasting capability. Moreover, the proposed hybrid model is very competitive compared to the state-of-the-art single model and other hybrid models involved in this paper.