Investigating Black-Box Model for Wind Power Forecasting Using Local Interpretable Model-Agnostic Explanations Algorithm

Investigating Black-Box Model for Wind Power Forecasting Using Local Interpretable Model-Agnostic Explanations Algorithm

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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 klaire labs ashwagandha 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 caruso rhodiola 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.