Evaluating Model Performance Through a User-Centric Explainable Framework for Probabilistic Load Forecasting Models

Keyphrases

• Evaluation Model 100%
• Model Performance 100%
• User-centric 100%
• Load Forecasting Models 100%
• Probabilistic Load Flow 100%
• Shapely Additive Explanations 66%
• Probabilistic Model 33%
• Prediction Accuracy 33%
• Deterministic Approach 33%
• Machine Learning Based 33%
• Stable Operation 33%
• Holidays 33%
• Load Demand 33%
• Efficient Operation 33%
• Local Interpretation 33%
• Model-driven Development 33%
• Missing Features 33%
• Individual Forecasts 33%
• Probabilistic Forecasting 33%
• Explainable AI 33%
• Daily Operation 33%
• Modern Power System 33%
• Local Explanations 33%
• Targeted Users 33%
• Panama 33%
• Global Interpretation 33%
• Permutation Importance 33%
• Secure Operation 33%
• Strategizing 33%
• AI Framework 33%
• Forecast Intervals 33%
• Gradient Boosting Regressor 33%
• Load Forecast 33%
• Interval Forecast 33%
• Kaggle Dataset 33%
• Explanation Needs 33%
• Explanation Goals 33%

Computer Science

• Performance Model 100%
• Development Stage 100%
• Gradient Boosting 100%
• Related Quantity 100%
• Explainable Artificial Intelligence 100%
• Model Development 100%
• Deterministic Approach 100%
• Machine Learning 100%
• Learning System 100%
• Artificial Intelligence 100%

Engineering

• Load Forecasting 100%
• Artificial Intelligence 66%
• Power Engineering 33%
• End-Users 33%
• Daily Operation 33%
• Regressors 33%
• Learning System 33%