WaveHiTS: Wavelet-Enhanced Hierarchical Time Series Modeling for Wind Direction Nowcasting in Eastern Inner Mongolia (2504.06532v1)

Abstract: 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.

Multi-Step Wind Direction Nowcasting Using WaveHiTS

The paper "WaveHiTS: Wavelet-Enhanced Hierarchical Time Series Modeling for Wind Direction Nowcasting in Eastern Inner Mongolia" addresses the complex challenges associated with predicting wind direction for optimizing wind energy production. Traditional forecasting methods such as ARIMA or machine learning models like SVMs and RFs have struggled to effectively manage the non-linearity and circularity inherent in wind direction data. These limitations present significant issues when deploying these models in real-world settings, particularly when short-term precision is vital for operational efficiency in wind energy applications.

Methodological Innovations

WaveHiTS introduces a sophisticated model architecture that skillfully integrates wavelet transforms with the Neural Hierarchical Interpolation for Time Series (N-HiTS) framework, alongside U-V component decomposition. Each of these components plays a critical role in addressing specific challenges associated with wind direction nowcasting.

1. U-V Decomposition: The transformation of circular wind direction data into linear U (east-west) and V (north-south) components elegantly tackles the discontinuity challenges at the 0°/360° boundary. This decomposition translates the complex forecasting problem into two standard regression tasks, enabling more stable predictions.
2. Wavelet Transform: By decomposing wind data into multi-scale frequency components, the wavelet transform captures variations across different temporal scales, facilitating improved noise reduction and feature extraction. This process is particularly advantageous for managing non-stationary signals, characteristic of meteorological data.
3. Hierarchical Time Series Modeling with N-HiTS: The hierarchical structure is designed to capture temporal dependencies at multiple scales, effectively mitigating error accumulation typically encountered in longer forecasting horizons. This hierarchical approach allows the model to maintain high accuracy across extended prediction periods.

Empirical Evaluation

The empirical validation of WaveHiTS on real-world meteorological data from Inner Mongolia has shown that it significantly outperforms a variety of other models, including RNN, LSTM, GRU, TFT, Informer, iTransformer, and hybrid models like EMD-LSTM. Notable performance metrics include RMSE values of approximately 19.2°-19.4°, which starkly contrast with the 56°-64° error range observed with deep learning recurrent models. Additionally, WaveHiTS achieved vector correlation coefficients of 0.985-0.987 and hit rates of 88.5%-90.1%, demonstrating superior robustness and predictive consistency.

Implications and Future Directions

The practical implications of the WaveHiTS model are significant for enhancing wind farm operational efficiency, especially in turbine yaw control and grid integration. By providing more accurate and reliable wind direction forecasts, this approach could materially improve energy capture and reduce mechanical stresses on turbines. The model's superior forecasting capability underscores the potential for further applications, such as improved scheduling for maintenance and optimization in renewable energy trading markets.

From a theoretical perspective, the integration of wavelet transforms and hierarchical time series modeling opens new pathways for handling complex time series data. Future research might extend the current model by incorporating additional meteorological factors or exploring its applicability in different geographical contexts.

In conclusion, the WaveHiTS model represents a significant advancement in predictive modeling for wind energy applications. Its innovative approach not only demonstrates empirical superiority over existing methods but also offers substantive contributions to both the applied and theoretical domains of time series forecasting.