# Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting

arXiv: https://arxiv.org/abs/2312.15741 · HTML: https://mlsanity.com/paper/2312.15741/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 25 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Appropriate Learning Rates of Adaptive Learning Rate Optimization Algorithms for Training Deep Neural Networks](https://mlsanity.com/paper/2002.09647/) (2020)
- [Grouped Feature Importance and Combined Features Effect Plot](https://mlsanity.com/paper/2104.11688/) (2021)
- [Weight and Gradient Centralization in Deep Neural Networks](https://mlsanity.com/paper/2010.00866/) (2020)
- [Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach](https://mlsanity.com/paper/2206.02433/) (2022)
- [An Interpretable Probabilistic Model for Short-Term Solar Power Forecasting Using Natural Gradient Boosting](https://mlsanity.com/paper/2108.04058/) (2021)

## Abstract (arXiv)

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.