Deep neural networks (DNNs) are receiving increasing attention in wind power
forecasting due to their ability to effectively capture complex patterns in
wind data. However, their forecasted errors are severely limited by the local
optimal weight issue in optimization algorithms, and their forecasted behavior
also lacks interpretability. To address these two challenges, this paper
firstly proposes simple but effective triple optimization strategies (TriOpts)
to accelerate the training process and improve the model performance of DNNs in
wind power forecasting. Then, permutation feature importance (PFI) and local
interpretable model-agnostic explanation (LIME) techniques are innovatively
presented to interpret forecasted behaviors of DNNs, from global and instance
perspectives. Simulation results show that the proposed TriOpts not only
drastically improve the model generalization of DNNs for both the deterministic
and probabilistic wind power forecasting, but also accelerate the training
process. Besides, the proposed PFI and LIME techniques can accurately estimate
the contribution of each feature to wind power forecasting, which helps to
construct feature engineering and understand how to obtain forecasted values
for a given sample.

提出了简单而有效的三重优化策略（TriOpts）来加快训练过程和改善深度神经网络（DNNs）在风力发电预测中的模型性能，并且创新性地提出了排列特征重要性（PFI）和局部可解释的模型不可知解释（LIME）技术来解释 DNN 的预测行为。模拟结果表明，所提出的 TriOpts 不仅大大提高了 DNNs 在确定性和概率性风力发电预测中的模型泛化能力，还加快了训练过程。此外，所提出的 PFI 和 LIME 技术可以准确估计每个特征对风力发电预测的贡献，有助于构建特征工程并理解如何获得给定样本的预测值。