Improving the Accuracy and Interpretability of Neural Networks for Wind
  Power Forecasting
Wenlong Liao Fernando Porte-Agel Jiannong Fang Birgitte Bak-Jensen Zhe Yang Gonghao Zhang
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.