What question did this study set out to answer?

To develop a high-precision day-ahead photovoltaic power forecasting framework by combining multi-scale spatial meteorological data, numerical weather predictions, and optimized machine learning.

August 19, 2026Scientific ReportsOpen Access

Hybrid multi-scale feature extraction and parameter optimization for day-ahead photovoltaic power forecasting

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Authors

ZQZheng QueBeijing Jiaotong University CDChang DanBeijing Jiaotong University

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Implication

Computational modeling study demonstrates enhanced day-ahead solar forecasting via optimized XGBoost and spatial meteorological data, indicating improved renewable grid reliability.

Key Points

To develop a high-precision day-ahead photovoltaic power forecasting framework by combining multi-scale spatial meteorological data, numerical weather predictions, and optimized machine learning.
Integrated historical power generation data with numerical weather prediction (NWP) inputs and high-resolution spatial meteorological variables.
Developed a hybrid framework using an XGBoost model optimized via a genetic algorithm for hyperparameter tuning and feature selection.
The genetic algorithm-optimized XGBoost model consistently reduced prediction errors relative to conventional machine learning baseline approaches.
Incorporating high-resolution spatial meteorological features and targeted NWP variables markedly improved day-ahead solar power forecasting accuracy.

Cite This Study

Que et al. (2026) studied this question.

synapsesocial.com/papers/6a8563eb03308d306e2d7523 https://doi.org/10.1038/s41598-026-67336-w

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Implication

Computational modeling study demonstrates enhanced day-ahead solar forecasting via optimized XGBoost and spatial meteorological data, indicating improved renewable grid reliability.

Hybrid multi-scale feature extraction and parameter optimization for day-ahead photovoltaic power forecasting

Discussion

Member takes

Implication

Key Points

Cite This Study

Hybrid multi-scale feature extraction and parameter optimization for day-ahead photovoltaic power forecasting

Discussion

Member takes

Implication

Key Points

Cite This Study

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