Modeling temporal lag correlations for wind farm meteorology-power joint forecasting: A universal framework

Yan Chen; Rongbin Liu; Tingxiao Ding; Jin Su; Yuhan Yang; Rongxing Jiang; Yingying Jiang; Yiming Qin; Kui Huang

doi:10.1016/j.ijepes.2026.111947

May 1, 2026International Journal of Electrical Power & Energy SystemsOpen Access

Modeling temporal lag correlations for wind farm meteorology-power joint forecasting: A universal framework

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Key Points

The aim is to create a framework for joint forecasting of meteorology and power by modeling temporal lag correlations.
Developed a unified framework for meteorology-power forecasting.
Identified correlations using temporal lag modeling.
Implemented an adaptive alignment mechanism to address dynamic temporal lags.
Achieved improved forecasting accuracy through the proposed framework.
Demonstrated effective identification of temporal lag correlations in varying conditions.
Provided a systematic approach for adaptive alignment in meteorology and power data.

Implication

Demonstrates unified meteorology-power joint forecasting, indicating improved accuracy across applications.

Abstract

• Proposed framework enables unified meteorology-power joint forecasting. • Identifies temporal lag correlations between on-site meteorology and power. • Adaptive alignment mechanism corrects dynamic physical temporal lags.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/6a1bcfe15783ba022b6fbc33 https://doi.org/10.1016/j.ijepes.2026.111947

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Modeling temporal lag correlations for wind farm meteorology-power joint forecasting: A universal framework

Key Points

Implication

Abstract

Discussion

Cite This Study

Modeling temporal lag correlations for wind farm meteorology-power joint forecasting: A universal framework

Key Points

Implication

Abstract

Discussion

Cite This Study

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