Multi-renewable energy resources parameters prediction through meta-learning models selectivity analysis and parallel fusion approaches DOI
Muhammad Abubakar, Yanbo Che, Muhammad Shoaib Bhutta

и другие.

Electrical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 27, 2025

Язык: Английский

Performance analysis of IoT-enabled hydro-photovoltaic power systems considering electrical power mission chains DOI
Hongyan Dui, Heyuan Li, Shaomin Wu

и другие.

Energy Conversion and Management, Год журнала: 2024, Номер 319, С. 118962 - 118962

Опубликована: Авг. 26, 2024

Язык: Английский

Процитировано

3
Application of three Transformer neural networks for short-term photovoltaic power prediction: A case study DOI Creative Commons
Jiahao Wu,

Yongkai Zhao,

Ruihan Zhang

и другие.

Solar Compass, Год журнала: 2024, Номер 12, С. 100089 - 100089

Опубликована: Сен. 17, 2024

Язык: Английский

Процитировано

3
A novel fractional Bessel grey system model optimized by Salp Swarm Algorithm for renewable energy generation forecasting in developed countries of Europe and North America DOI
Xin Ma, Hong Yuan,

Minda Ma

и другие.

Computational and Applied Mathematics, Год журнала: 2025, Номер 44(1)

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Optimization of solar and wind power plants production through a parallel fusion approach with modified hybrid machine and deep learning models DOI Creative Commons
Muhammad Abubakar, Yanbo Che, Zafar Ahsan

и другие.

Intelligent Data Analysis, Год журнала: 2025, Номер unknown

Опубликована: Фев. 16, 2025

Artificial Intelligence (AI) is becoming increasingly indispensable across diverse domains as technology rapidly advances. As traditional energy sources dwindle, there's a noticeable pivot towards renewable (RES). However, to effectively meet demands, integrating these RES into smart grids bolster efficiency imperative. Despite the transition, ongoing technical challenges persist, specifically in accurately predicting and optimizing grid parameters. To tackle hurdles enhance efficiency, various AI techniques are being harnessed. This study leverages real-time generation data (MWh) from solar wind plants over year, dependent on parameters such POA speed, respectively. Prediction outcomes derived using three machine learning (ML) models (XGBoost, CatBoost, LightGBM) deep (DL) (LSTM, BiLSTM, GRU). From individual models, two hybrid ML DL developed, yielding promising results. Subsequently, further refined through parallel fusion approach (PFA), resulting heightened accuracy reliability. The implementation of this technique notably reduces error rates 15.05% for ML, 19.18% DL, 8.1432% PFA. methodology holds substantial potential future research endeavors, supplementing existing enhanced efficiency.

Язык: Английский

Процитировано

0
Multi-renewable energy resources parameters prediction through meta-learning models selectivity analysis and parallel fusion approaches DOI
Muhammad Abubakar, Yanbo Che, Muhammad Shoaib Bhutta

и другие.

Electrical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 27, 2025

Язык: Английский

Процитировано

0