Leveraging microbial synergy: Predicting the optimal consortium to enhance the performance of microbial fuel cell using Subspace-kNN DOI
Jimil Mehta, Soumesh Chatterjee, M. T. Shah

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 369, P. 122252 - 122252

Published: Sept. 1, 2024

Language: Английский

Predicting green hydrogen production using electrolyzers driven by photovoltaic panels and wind turbines based on machine learning techniques: A pathway to on-site hydrogen refuelling stations DOI
Baki Barış Urhan, Ayşe Erdoğmuş, Ahmet Şakir Dokuz

et al.

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 101, P. 1421 - 1438

Published: Jan. 8, 2025

Language: Английский

Citations

3
A review on machine learning applications in hydrogen energy systems DOI Creative Commons

Zaid Allal,

Hassan Noura, Ola Salman

et al.

International Journal of Thermofluids, Journal Year: 2025, Volume and Issue: unknown, P. 101119 - 101119

Published: Feb. 1, 2025

Language: Английский

Citations

3
Adaptive energy management strategy for optimal integration of wind/PV system with hybrid gravity/battery energy storage using forecast models DOI Creative Commons
Anisa Emrani, Youssef Achour, M. J. Sanjari

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 96, P. 112613 - 112613

Published: June 24, 2024

Language: Английский

Citations

13
Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model DOI Creative Commons

Venkatachalam Mohanasundaram,

R. Balamurugan

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 3, 2025

As the global energy demand continues to produce, photovoltaic (PV) solar has emerged as a key Renewable Energy Source (RES) due its sustainability and potential reduce dependence on fossil fuels. However, accurate forecasting of Solar (SE) output remains significant challenge inherent variability intermittency irradiance (SI), which is affected by factors such weather conditions, geographic location, seasonal patterns. Reliable prediction models are crucial for optimizing management, ensuring grid stability, minimizing operational costs. To address these challenges, this research introduces an innovative method that integrates Robust Seasonal-Trend Decomposition (RSTL) with Adaptive Seagull Optimisation Algorithm (ASOA)-optimized Long Short-Term Memory (LSTM) neural network. Using RSTL differentiate between time series data into development, in nature, residual factors, methodology addresses SI's unpredictable nature intermittent operation provides basis predictions. ASOA improves LSTM features constantly finding exploiting resources adopting motivation from seagulls' collecting migration behaviours. Parameter standardization employing ASOA, decomposition approach, conceptual model networks all presented work. The proposed been contrasted conventional methods applying testing environment incorporating essential Meteorological Factors (MF) historical SE datasets. study performance measurements (RMSE, MAE, R2) demonstrates improvements accuracy results highlight implications regarding subsequent studies real-world uses prediction, accentuating positive impacts adaptive optimized performance.

Language: Английский

Citations

1
Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review DOI
Mir Mohammad Ali, Md. Arif Hossen, Azrina Abd Aziz

et al.

Next Materials, Journal Year: 2025, Volume and Issue: 8, P. 100522 - 100522

Published: Feb. 10, 2025

Language: Английский

Citations

1
Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy DOI Creative Commons

Tien Han Nguyen,

Prabhu Paramasivam,

Van Huong Dong

et al.

JOIV International Journal on Informatics Visualization, Journal Year: 2024, Volume and Issue: 8(1), P. 55 - 55

Published: March 16, 2024

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, solar power, can revolutionize the industry. Biomass biofuels have benefited significantly from implementing AI ML algorithms that optimize feedstock, enhance resource management, facilitate biofuel production. By applying insight derived data analysis, stakeholders improve entire supply chain - biomass conversion, fuel synthesis, agricultural growth, harvesting to mitigate environmental impacts accelerate transition a low-carbon economy. Furthermore, in combustion systems engines has yielded substantial improvements efficiency, emissions reduction, overall performance. Enhancing engine design control techniques produces cleaner, more efficient minimal impact. This contributes sustainability of power generation transportation. are employed analyze vast quantities photovoltaic systems' design, operation, maintenance. The ultimate goal is increase output system efficiency. Collaboration among academia, industry, policymakers imperative expedite sustainable future harness potential energy. these technologies, it possible establish ecosystem, which would benefit generations.

Language: Английский

Citations

6
Wind turbine fault detection and identification using a two-tier machine learning framework DOI Creative Commons

Zaid Allal,

Hassan Noura, Flavien Vernier

et al.

Intelligent Systems with Applications, Journal Year: 2024, Volume and Issue: 22, P. 200372 - 200372

Published: April 26, 2024

Language: Английский

Citations

6
Overview of emerging electronics technologies for artificial intelligence: A review DOI Creative Commons
Peng Gao, Muhammad Adnan

Materials Today Electronics, Journal Year: 2025, Volume and Issue: unknown, P. 100136 - 100136

Published: Jan. 1, 2025

Language: Английский

Citations

0
Hydroelectric Power Potentiality Analysis for the Future Aspect of Trends with R2 Score Estimation by XGBoost and Random Forest Regressor Time Series Models DOI Open Access
Suman Chowdhury, Apurba Kumar Saha,

Dilip Kumar Das

et al.

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 252, P. 450 - 456

Published: Jan. 1, 2025

Language: Английский

Citations

0
Time Series Analysis of Solar Power Generation Based on Machine Learning for Efficient Monitoring DOI Creative Commons
Umer Farooq, Muhammad Faheem Mushtaq, Zahid Ullah

et al.

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(2)

Published: Feb. 1, 2025

ABSTRACT Solar energy, a renewable resource, is essential for the efficiency of solar photovoltaic (PV) panels. However, meteorological factors, such as irradiation, weather patterns, precipitation, and overall climate conditions, pose challenges to seamless integration energy production into power grid. Accurate prediction PV system output necessary enhance The study focuses on utilizing machine learning (ML) methodologies accurate forecasting generation, addressing related integrating By analyzing generation data employing advanced ML models, research aims predictability systems. significance this lies in its potential optimize production, improve grid stability, contribute transition towards sustainable sources. This assesses appropriateness approaches accurately projecting half‐hourly cycles next day. consists many analytical phases, including exploratory analysis, inverter which are carried out two separate plants. following step conduct comparative analyses. analyzed using models like gradient boosting classifiers linear regressions. first plant produces best results, with an amazing 0.97% accuracy classifier regression classifier. Contrarily, second achieved 0.61% 0.62% models. study's techniques insights can help operators electricity market stakeholders make informed decisions use generated power, minimize waste, plan preservation, reduce costs, facilitate widespread

Language: Английский

Citations

0