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首页> 外文期刊>International Journal of Performability Engineering >Short-Term Wind Power Forecasting using Wavelet-based Hybrid Recurrent Dynamic Neural Networks
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Short-Term Wind Power Forecasting using Wavelet-based Hybrid Recurrent Dynamic Neural Networks

机译：基于小波的混合复发动态神经网络的短期风力预测

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摘要

In the recent past, the integration of wind energy generation into smart grids has gained lot of momentum because of its availability. The major hurdle in the integration of wind power in smart electric grids, at present time is the irregularity and unpredictability of wind power. Therefore, in order to deal with these challenges, the superior forecasting tool plays an important role in the planning and execution of the wind energy integration. In the expanding power system, because of increasing wind power penetration, a precise wind power forecasting technique is greatly needed to help system operators and consider wind power production in economic scheduling, unit commitment, and allocation trouble reservation. In this paper, two hybrid recurrent dynamic neural networks have employed hybridizing wavelet transform (WT) for short-term prediction of wind power. The proposed approach consists of wavelet decomposition of wind power and wind speed time series, and NAR and NARX recurrent dynamic neural networks are employed to regress upon each decomposed sub-series. Thereafter, the individual outputs of sub-series are aggregated to achieve final prediction of wind power, with up to 24 hours of forecast horizon. The performance of the proposed method is obtained in terms of MAE, MSE, and MAPE values and compared to the results of the persistence method. The forecast results reveal that WT-NARX model is better in terms of the selected performance criteria as compared to the WT-NAR and persistence models respectively.
机译：在最近的过去，由于其可用性，风能产生进入智能电网的流量增长了很多。在智能电网中整合风电的主要障碍，目前是风力的不规则和不可预测性。因此，为了应对这些挑战，优越的预测工具在风能集成的规划和执行中起着重要作用。在扩展电力系统中，由于风力渗透性增加，大大需要精确的风力预测技术来帮助系统运营商，并考虑经济调度，单位承​​诺和分配故障保留的风力发电。在本文中，两个混合复发动态神经网络采用杂交小波变换（WT），用于风电的短期预测。所提出的方法包括风电和风速时间序列的小波分解，并且NAR和NARX复发性动态神经网络被用于在每个分解的子系列上进行回归。此后，聚集子系列的各个输出以实现风电的最终预测，预测地平线多达24小时。所提出的方法的性能是在MAE，MSE和MAPE值方面获得的，并与持久性方法的结果相比。预测结果表明，与WT-NAR和持久性模型相比，WT-NARX模型效果更好。

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