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GradientSHAP adapted for explainable AI in wind power forecasting

By PulseAugur Editorial · [1 sources] ·

A new paper introduces an adapted GradientSHAP approach to enhance the explainability of AI models used in wind power forecasting. This method aims to address the 'black box' problem, which has hindered AI adoption in the renewable energy sector. The research presents novel visualizations for both local and global explanations, revealing complex interactions between environmental factors, site conditions, and temporal data. While demonstrating the potential for valuable insights, the paper acknowledges the complexity of GradientSHAP and calls for future research to simplify its implementation for practical deployment in critical energy applications. AI

IMPACT Enhances trust and adoption of AI in critical energy infrastructure by improving model transparency.

RANK_REASON The item is a research paper published on arXiv detailing a new methodology for AI explainability in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GradientSHAP adapted for explainable AI in wind power forecasting

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The item is a research paper published on arXiv detailing a new methodology for AI explainability in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Robbie Southam, Zhong Fan, Ke Li ·

    Multistep and Multivariate Time Series Explainability with GradientSHAP: A Wind Power Case Study

    arXiv:2610.40330v1 Announce Type: new Abstract: As the effects of climate change become increasingly apparent, the demand for renewable energy sources such as wind power continues to grow. However, renewables' variable and intermittent nature presents significant challenges in th…

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