bom_analysis

This repository is published together with the article entitled "Analyzing the Impact of Design Factors on Solar Module Thermomechanical Durability Using Interpretable Machine Learning Techniques". The data set used in this study is available at DuraMat Datahub.

public_bom_tc_modeling.ipynb records the machine learning modeling and SHAP interpretation.
public_bom_tc_stats_validation.ipynb records the post-hoc statistical validation.
bom_tc_pkg.py provides the functions used in the two notebooks.

model_publish contains the pretrained weights of random forest model. rf_train.pkl is the weight trained on the training set and to be tested on the testing set. rf_shap.pkl shares the same model structure with rf_train.pkl but trained on the whole data set, which was used for SHAP interpretation.

⚠️ Some of the values in the published data set are redacted due to non-disclosure agreement. The redacted values are marked in the data set. It should be noted that new models trained on the redacted data set may not yield the same results as models mentioned in the article that are trained on the original data set.