Prediction of abnormal proliferation risk of Phaeocystis globosa based on correlation mining of PC concentration indicator and meteorological factors along Qinzhou Bay, Guangxi

Chen, Huaquan; Yao, Huanmei; Liao, Pengren; Wen, Ke; Huang, Yi; Zhong, Wei‐Ping

doi:10.1016/j.seares.2023.102365

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2024

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Cited by 2 publications

References 51 publications

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Advancing Solar Power Forecasting: Integrating Boosting Cascade Forest and Multi-Class-Grained Scanning for Enhanced Precision

BOUTAHIR,

FARHAOUI,

Azrour

2024

Preprint

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Advancing Solar Power Forecasting: Integrating Boosting Cascade Forest and Multi-Class-Grained Scanning for Enhanced Precision

BOUTAHIR,

FARHAOUI,

Azrour

2024

Preprint

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Advancing Solar Power Forecasting: Integrating Boosting Cascade Forest and Multi-Class-Grained Scanning for Enhanced Precision

Boutahir,

Farhaoui,

Azrour

et al. 2024

Sustainability

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Accurate solar power generation forecasting is paramount for optimizing renewable energy systems and ensuring sustainability in our evolving energy landscape. This study introduces a pioneering approach that synergistically integrates Boosting Cascade Forest and multi-class-grained scanning techniques to enhance the precision of solar farm power output predictions significantly. While Boosting Cascade Forest excels in capturing intricate, nonlinear variable interactions through ensemble decision tree learning, multi-class-grained scanning reveals fine-grained patterns within time-series data. Evaluation with real-world solar farm data demonstrates exceptional performance, reflected in low error metrics (mean absolute error, 0.0016; root mean square error 0.0036) and an impressive R-squared score of 99.6% on testing data. This research represents the inaugural application of these advanced techniques to solar generation forecasting, highlighting their potential to revolutionize renewable energy integration, streamline maintenance, and reduce costs. Opportunities for further refinement of ensemble models and exploration of probabilistic forecasting methods are also discussed, underscoring the significance of this work in advancing solar forecasting techniques for a sustainable energy future.

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Prediction of abnormal proliferation risk of Phaeocystis globosa based on correlation mining of PC concentration indicator and meteorological factors along Qinzhou Bay, Guangxi

Cited by 2 publications

References 51 publications

Advancing Solar Power Forecasting: Integrating Boosting Cascade Forest and Multi-Class-Grained Scanning for Enhanced Precision

Advancing Solar Power Forecasting: Integrating Boosting Cascade Forest and Multi-Class-Grained Scanning for Enhanced Precision

Advancing Solar Power Forecasting: Integrating Boosting Cascade Forest and Multi-Class-Grained Scanning for Enhanced Precision

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