Wind power forecasting (WPF) is important for safe

stable

and reliable integration of new energy technologies into power systems. Machine learning (ML) algorithms have recently attracted increasing attention in the field of WPF. However

opaque decisions and lack of trustworthiness of black-box models for WPF could cause scheduling risks. This study develops a method for identifying risky models in practical applications and avoiding the risks. First

a local interpretable model-agnostic explanations algorithm is introduced and improved for WPF model analysis. On that basis

a novel index is presented to quantify the level at which neural networks or other black-box models can trust features involved in training. Then

by revealing the operational mechanism for local samples

human interpretability of the black-box model is examined under different accuracies

time horizons

and seasons. This interpretability provides a basis for several technical routes for WPF from the viewpoint of the forecasting model. Moreover

further improvements in accuracy of WPF are explored by evaluating possibilities of using interpretable ML models that use multi-horizons global trust modeling and multi-seasons interpretable feature selection methods. Experimental results from a wind farm in China show that error can be robustly reduced.