A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260603" target="_blank" >RIV/61989100:27240/25:10260603 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.nature.com/articles/s41598-025-97793-8" target="_blank" >https://www.nature.com/articles/s41598-025-97793-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-025-97793-8" target="_blank" >10.1038/s41598-025-97793-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction

Popis výsledku v původním jazyce

Accurate very short-term wind power forecasting is critical for the reliable integration of renewable energy into modern power systems. However, the inherent variability and non-linearity of wind power data pose significant challenges. To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. The model is optimized using an enhanced Artificial Protozoa Optimizer (IAPO) augmented with an Adaptive Environmental Response Mechanism (AERM), which dynamically adjusts exploration and exploitation strategies based on the problem landscape to improve convergence and hyperparameter tuning efficiency. The proposed IAPO-LSTM model was evaluated on four real-world datasets-NREL WIND, EMD WIND, WWSIS, and ERCOT GRID-and benchmarked against six state-of-the-art forecasting models. Results demonstrate that IAPO-LSTM achieved the lowest forecasting errors across all datasets, with Mean Absolute Error (MAE) as low as 2.78, Root Mean Square Error (RMSE) of 4.50, and Theil&apos;s Inequality Coefficient (TIC) of 0.0292 on the ERCOT dataset. Additionally, the model demonstrated faster inference times and better statistical significance (p &lt; 0.005) compared to baseline methods. These outcomes confirm that IAPO-LSTM is not only highly accurate but also efficient and robust for real-time wind power forecasting applications.

Název v anglickém jazyce

A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction

Popis výsledku anglicky

Accurate very short-term wind power forecasting is critical for the reliable integration of renewable energy into modern power systems. However, the inherent variability and non-linearity of wind power data pose significant challenges. To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. The model is optimized using an enhanced Artificial Protozoa Optimizer (IAPO) augmented with an Adaptive Environmental Response Mechanism (AERM), which dynamically adjusts exploration and exploitation strategies based on the problem landscape to improve convergence and hyperparameter tuning efficiency. The proposed IAPO-LSTM model was evaluated on four real-world datasets-NREL WIND, EMD WIND, WWSIS, and ERCOT GRID-and benchmarked against six state-of-the-art forecasting models. Results demonstrate that IAPO-LSTM achieved the lowest forecasting errors across all datasets, with Mean Absolute Error (MAE) as low as 2.78, Root Mean Square Error (RMSE) of 4.50, and Theil&apos;s Inequality Coefficient (TIC) of 0.0292 on the ERCOT dataset. Additionally, the model demonstrated faster inference times and better statistical significance (p &lt; 0.005) compared to baseline methods. These outcomes confirm that IAPO-LSTM is not only highly accurate but also efficient and robust for real-time wind power forecasting applications.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10700 - Other natural sciences

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Svazek periodika

15

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

34

Strana od-do

nestránkováno

Kód UT WoS článku

001498722100026

EID výsledku v databázi Scopus

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