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Leveraging Large Language Models for Wind Energy Assessment

AAAI 2025 · Large language models › AI and society · approach: Critiques of LLM claims and judging · 3 citations (OpenAlex)

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Proposes using BERT-based LLMs to predict wind speed probabilities, power densities, wind speed extrapolation, and available energy at 10/50/100 m in Cameroon, with results only planned (no experiments reported).

Problem

Wind energy assessment is hard because wind is stochastic with multiple regimes that classical distributions such as Weibull fit poorly, producing poor estimates especially in developing countries.

Approach

Applies large language models (BERT) to wind energy potential assessment as a regression/prediction task on site wind data, instead of fitting parametric statistical distributions, and releases a wind dataset collected in Cameroon.

Key results

• No experimental results are reported; the paper states only expected outcomes (LLM performance better than Weibull and lower error than prior work).

Limitations

• No experiments or quantitative results are presented; the work is a proposal of planned methodology.
• Rationale for using BERT for numeric time-series regression is not justified.
• Data limited to one region (Western Cameroon) at 10 m height, with 50 m and 100 m values derived via power law.

Experimental setup

Datasets: Wind speed data collected in Western Cameroon at 10-minute frequency at 10 m height

Metrics: F1-score precision recall correlation coefficient mean absolute error

Baselines: Weibull distribution Gamma distribution Nakagami distribution Normal distribution

Builds on: Weibull distribution Energy Pattern Factor method Open Research Knowledge Graph

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