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

作　　者：Mao Yang Chuanyu Xu Yuying Bai Miaomiao Ma Xin Su 

机构地区：[1]Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology,Ministry of Education,Northeast Electric Power University,Jilin,132012,China [2]Daqing Power Industry Bureau,Daqing 163000,China

出　　处：《CSEE Journal of Power and Energy Systems》2025年第1期227-242,共16页中国电机工程学会电力与能源系统学报(英文)

基　　金：supported by the National Key R&D Program of China(Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption)under Grant(2018YFB0904200).

摘　　要：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.

关 键 词：Black-box model correlation analysis feature trust index local interpretability local interpretable modelagnostic explanations(LIME) wind power forecasting 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]