Wind speed forecasting using a combined deep learning model with slime mould optimization DOI
K. Natarajan, Jai Govind Singh

International Journal of Green Energy, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: April 17, 2025

Language: Английский

Generative probabilistic forecasting of wind power: A Denoising-Diffusion-based nonstationary signal modeling approach DOI
Jingxuan Liu, Haixiang Zang, Lilin Cheng

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134576 - 134576

Published: Jan. 1, 2025

Language: Английский

Citations

1
A review of PV power forecasting using machine learning techniques DOI

Manvi Gupta,

Archie Arya,

U. Varshney

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100058 - 100058

Published: Jan. 1, 2025

Language: Английский

Citations

1
A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant DOI Creative Commons
Fabio Famoso, Ludovıca Marıa Olıverı, Sebastian Brusca

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(7), P. 1627 - 1627

Published: March 28, 2024

This paper presents a novel approach to estimating short-term production of wind farms, which are made up numerous turbine generators. It harnesses the power big data through blend data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions output with stochastic model dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order demonstrate applicability ANN non-linear problems energy or forecast estimation. The study leverages innovative cluster analysis group turbines reduce computational effort ANN, dependability that improves accuracy Therefore, main novelty is employment hybrid accounts realistic operational scenarios turbines, including their susceptibility random shutdowns marks significant advancement field, introducing methodology can aid design forecast. research applied case 24 MW farm located south Italy, characterized by 28 turbines. findings integrated significantly enhances wind-energy estimation, achieving 480% improvement over solo-clustering approach.

Language: Английский

Citations

6
A new Takagi–Sugeno–Kang model for time series forecasting DOI
Kaike Sa Teles Rocha Alves,

Caian Dutra de Jesus,

Eduardo Pestana de Aguiar

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108155 - 108155

Published: March 11, 2024

Language: Английский

Citations

4
Application of machine learning and deep learning in geothermal resource development: Trends and perspectives DOI Creative Commons
Abdulrahman Al‐Fakih, Abdulazeez Abdulraheem, SanLinn I. Kaka

et al.

Deep Underground Science and Engineering, Journal Year: 2024, Volume and Issue: 3(3), P. 286 - 301

Published: May 23, 2024

Abstract This study delves into the latest advancements in machine learning and deep applications geothermal resource development, extending analysis up to 2024. It focuses on artificial intelligence's transformative role industry, analyzing recent literature from Scopus Google Scholar identify emerging trends, challenges, future opportunities. The results reveal a marked increase intelligence (AI) applications, particularly reservoir engineering, with significant observed post‐2019. highlights AI's potential enhancing drilling exploration, emphasizing integration of detailed case studies practical applications. also underscores importance ongoing research tailored AI light rapid technological trends field.

Language: Английский

Citations

4
Assessment of ammonia-diesel fuel blends on compression ignition engine performance and emissions using machine learning techniques DOI
Arivalagan Pugazhendhi, Siti Kartom Kamarudin, Sulaiman Ali Alharbi

et al.

Fuel, Journal Year: 2024, Volume and Issue: 373, P. 132135 - 132135

Published: June 22, 2024

Language: Английский

Citations

4
Assessment of green hydrogen production by volatile renewable energy under different SSPs scenarios in China DOI
Bingchun Liu, Mingzhao Lai,

Yajie Wang

et al.

Renewable Energy, Journal Year: 2024, Volume and Issue: 235, P. 121296 - 121296

Published: Sept. 3, 2024

Language: Английский

Citations

4
Time-series forecasting of microbial fuel cell energy generation using deep learning DOI Creative Commons
Adam Hess-Dunlop,

Harshitha Kakani,

S. G. Taylor

et al.

Frontiers in Computer Science, Journal Year: 2025, Volume and Issue: 6

Published: Jan. 21, 2025

Soil microbial fuel cells (SMFCs) are an emerging technology which offer clean and renewable energy in environments where more traditional power sources, such as chemical batteries or solar, not suitable. With further development, SMFCs show great promise for use robust affordable outdoor sensor networks, particularly farmers. One of the greatest challenges development this is understanding predicting fluctuations SMFC generation, electro-generative process yet fully understood. Very little work currently exists attempting to model predict relationship between soil conditions we first machine learning do so. In paper, train Long Short Term Memory (LSTM) models future generation across timescales ranging from 3 min 1 h, with results 2.33 5.71% Mean Average Percent Error (MAPE) median voltage prediction. For each timescale, quantile regression obtain point estimates establish bounds on uncertainty these estimates. When comparing predicted vs. actual values total generated during testing period, magnitude prediction errors ranged 2.29 16.05%. To demonstrate real-world utility research, also simulate how could be used automated environment SMFC-powered devices shut down activate intermittently preserve charge, promising initial results. Our deep learning-based simulation framework would allow a device achieve 100+% increase successful operations, compared naive that schedules operations based average past.

Language: Английский

Citations

0
Time Series Foundation Model for Improved Transformer Load Forecasting and Overload Detection DOI Creative Commons

Yikai Hou,

Chao Ma, Xiang Li

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 660 - 660

Published: Jan. 31, 2025

Simple load forecasting and overload prediction models, such as LSTM XGBoost, are unable to handle the increasing amount of data in power systems. Recently, various foundation models (FMs) for time series analysis have been proposed, which can be scaled up large variables datasets across domains. However, simple pre-training setting makes FMs unsuitable complex downstream tasks. Effectively handling real-world tasks depends on additional data, i.e., covariates, prior knowledge. Incorporating these through structural modifications is not feasible, it would disrupt pre-trained weights. To address this issue, paper proposes a frequency domain mixer, FreqMixer, framework enhancing task-specific analytical capabilities FMs. FreqMixer an auxiliary network backbone that takes covariates input. It has same number layers communicates with at each layer, allowing incorporation knowledge without altering backbone’s structure. Through experiments, demonstrates high efficiency performance, reducing MAPE by 23.65%, recall 87%, precision 72% transformer during Spring Festival while improving 192.09% accuracy 14% corresponding prediction, all processing from over 160 transformers just 1M parameters.

Language: Английский

Citations

0
A novel prediction of the PV system output current based on integration of optimized hyperparameters of multi-layer neural networks and polynomial regression models DOI
Hussein Mohammed Ridha, Hashim Hizam, Seyedali Mirjalili

et al.

Next Energy, Journal Year: 2025, Volume and Issue: 8, P. 100256 - 100256

Published: Feb. 26, 2025

Language: Английский

Citations

0