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Consensus and Divergence in Explainable AI (XAI): Evaluating Global Feature-Ranking Consistency with Empirical Evidence from Solar Energy Forecasting

Year: 2026

Type: article

Abstract: The growing reliance on solar energy necessitates robust and interpretable forecasting models for stable grid management. Current research frequently employs Explainable AI (XAI) to glean insights from complex black-box models, yet the reliability and consistency of these explanations remain largely unvalidated. Inconsistent feature attributions can mislead grid operators by incorrectly identifying the dominant drivers of solar generation, thereby affecting operational planning, reserve allocation, and trust in AI-assisted decision-making. This study addresses this critical gap by conducting a systematic statistical evaluation of feature rankings generated by multiple XAI methods, including model-agnostic (SHAP, PDP, PFI, ALE) and model-specific (Split- and Gain-based) techniques, within a time-series regression context. Using a LightGBM model for one-day-ahead solar power forecasting across four sites in Calgary, Canada, we evaluate consensus and divergence using the Friedman test, Kendall’s W, and Spearman’s rank correlation. To ensure the generalizability of our findings, we further validate the results using a CatBoost model. Our results show a strong overall agreement across methods (Kendall’s W: 0.90–0.94), with no statistically significant difference in ranking (p > 0.05). However, pairwise analysis reveals that the “Split” method frequently diverges from other techniques, exhibiting lower correlation scores. These findings suggest that while XAI consensus is high, relying on a single method—particularly the split count—poses risks. We recommend employing multi-method XAI and using agreement as an explicit diagnostic to ensure transparent and reliable solar energy predictions.

Source: Mathematics

DOI: 10.3390/math14020297

Authors: Kay Thari Thinn, Waddah Saeed

Institution: De Montfort University

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,137