Enhancing weld line visibility prediction in injection molding using physics-informed neural networks DOI Creative Commons
Andrea Pieressa,

Giacomo Baruffa,

Marco Sorgato

et al.

Journal of Intelligent Manufacturing, Journal Year: 2024, Volume and Issue: unknown

Published: July 13, 2024

Abstract This study introduces a novel approach using Physics-Informed Neural Networks (PINN) to predict weld line visibility in injection-molded components based on process parameters. Leveraging PINNs, the research aims minimize experimental tests and numerical simulations, thus reducing computational efforts, make classification models for surface defects more easily implementable an industrial environment. By correlating with Frozen Layer Ratio (FLR) threshold, identified through limited data generates synthetic datasets pre-training neural networks. demonstrates that quality model pre-trained PINN-generated achieves comparable performance randomly initialized network terms of Recall Area Under Curve (AUC) metrics, substantial reduction 78% need points. Furthermore, it similar accuracy levels 74% fewer The results demonstrate robustness networks PINNs predicting visibility, offering promising minimizing efforts resources.

Language: Английский

Machine Learning and Deep Learning in Energy Systems: A Review DOI Open Access
Mohammad Mahdi Forootan, Iman Larki, Rahim Zahedi

et al.

Sustainability, Journal Year: 2022, Volume and Issue: 14(8), P. 4832 - 4832

Published: April 18, 2022

With population increases and a vital need for energy, energy systems play an important decisive role in all of the sectors society. To accelerate process improve methods responding to this increase demand, use models algorithms based on artificial intelligence has become common mandatory. In present study, comprehensive detailed study been conducted applications Machine Learning (ML) Deep (DL), which are newest most practical Artificial Intelligence (AI) systems. It should be noted that due development DL algorithms, usually more accurate less error, these ability model solve complex problems field. article, we have tried examine very powerful problem solving but received attention other studies, such as RNN, ANFIS, RBN, DBN, WNN, so on. This research uses knowledge discovery databases understand ML systems’ current status future. Subsequently, critical areas gaps identified. addition, covers efficient used field; optimization, forecasting, fault detection, investigated. Attempts also made cover their evaluation metrics, including not only important, newer ones attention.

Language: Английский

Citations

158
An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost DOI
Selçuk Demir, Emrehan Kutluğ Şahin

Neural Computing and Applications, Journal Year: 2022, Volume and Issue: 35(4), P. 3173 - 3190

Published: Oct. 8, 2022

Language: Английский

Citations

86
The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction DOI Creative Commons
Seyed Matin Malakouti, Mohammad Bagher Menhaj, Amir Abolfazl Suratgar

et al.

Cleaner Engineering and Technology, Journal Year: 2023, Volume and Issue: 15, P. 100664 - 100664

Published: July 28, 2023

It is essential to have accurate projections of the quantity solar energy that will be generated in future improve competitiveness power plants market and reduce dependence both economy society on fossil fuels. This can accomplished by having a better understanding amount future. We used databases containing information about California span 2019 through 2021. These years encompass state's forecast. data were analysis. The 10-fold cross-validation Grid search has been enhance performance decision tree, light gradient boosting machine, an extra tree Solar Farm Power Generation Prediction.

Language: Английский

Citations

83
Hybrid renewable energy systems stability analysis through future advancement technique: A review DOI

Thavamani Jeyaraj,

Arul Ponnusamy,

D. Edison Selvaraj

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 383, P. 125355 - 125355

Published: Jan. 22, 2025

Language: Английский

Citations

2
Forecasting Solar Energy Production Using Machine Learning DOI Creative Commons

C. Vennila,

Anita Titus,

T. Sri Sudha

et al.

International Journal of Photoenergy, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 7

Published: April 30, 2022

When it comes to large-scale renewable energy plants, the future of solar power forecasting is vital their success. For reliable predictions electricity generation, one must take into consideration changes in weather patterns over time. In this paper, a hybrid model that integrates machine learning and statistical approaches suggested for predicting generation. order improve accuracy model, an ensemble models was used study. The results simulation show proposed method has reduced placement cost, when compared with existing methods. comparing performance all combination strategies standard individual models, outperformed conventional models. According findings, made use both statistics sole its performance.

Language: Английский

Citations

62
Smart Energy Management System: Design of a Monitoring and Peak Load Forecasting System for an Experimental Open-Pit Mine DOI Creative Commons
Oussama Laayati, Mostafa Bouzi,

Ahmed Chebak

et al.

Applied System Innovation, Journal Year: 2022, Volume and Issue: 5(1), P. 18 - 18

Published: Jan. 30, 2022

Digitization in the mining industry and machine learning applications have improved production by showing insights different components. Energy consumption is one of key components to improve industry’s performance a smart way that requires very low investment. This study represents new hardware, software, data processing infrastructure for open-pit mines overcome energy 4.0 transition digital transformation. The main goal this adding an artificial intelligence layer use experimental mine giving on electrical grid quality. achievement these goals will ease decision-making stage maintenance managers according ISO 50001 standards. In order minimize consumption, which impact directly profit efficiency industry, design monitoring peak load forecasting system was proposed tested Benguerir. challenges application were typical loads machines per stage, feeding supervisors real time same process SCADA view, parallel integrating hardware solutions control system, proposing fast forest quantile regression algorithm predict demand response based historical scenarios, finding correlations between KPIs global

Language: Английский

Citations

44
Intelligent data-driven compressive strength prediction and optimization of reactive powder concrete using multiple ensemble-based machine learning approach DOI
M. Iqbal Khan, Yassir M. Abbas

Construction and Building Materials, Journal Year: 2023, Volume and Issue: 404, P. 133148 - 133148

Published: Sept. 9, 2023

Language: Английский

Citations

25
Latest Advancements in Solar Photovoltaic-Thermoelectric Conversion Technologies: Thermal Energy Storage Using Phase Change Materials, Machine Learning, and 4E Analyses DOI Creative Commons
Hisham Alghamdi, Chika Maduabuchi, Kingsley Okoli

et al.

International Journal of Energy Research, Journal Year: 2024, Volume and Issue: 2024, P. 1 - 29

Published: Jan. 23, 2024

In recent times, the significance of renewable energy generation has increased and photovoltaic-thermoelectric (PV-TE) technologies have emerged as a promising solution. However, incorporation these still faces difficulties in storage optimization. This review paper addresses challenges by providing comprehensive overview latest advancements PV-TE technologies. The emphasizes integration phase change materials (PCMs) for thermal storage, also buttressing use encapsulated PCM efficiency, hybrid to enhance overall performance. Furthermore, reviews on machine learning techniques efficient optimization thermoelectric modules into tandem perovskite silicon solar cells been comprehensively analyzed. systems, reviewed this article, signify significant progress attaining sustainable effective production storage. 4Es, underlining their importance. It not only consolidates developments but charts path future research field technologies, offering precise insights guide upcoming studies innovations.

Language: Английский

Citations

10
Advancing energy efficiency: Machine learning based forecasting models for integrated power systems in food processing company DOI Creative Commons
Seray MİRASÇI,

Sara Uygur,

Aslı Aksoy

et al.

International Journal of Electrical Power & Energy Systems, Journal Year: 2025, Volume and Issue: 165, P. 110445 - 110445

Published: Jan. 12, 2025

Language: Английский

Citations

1
Energy management using multi-criteria decision making and machine learning classification algorithms for intelligent system DOI
Hmeda Musbah, Gama Ali, Hamed H. Aly

et al.

Electric Power Systems Research, Journal Year: 2021, Volume and Issue: 203, P. 107645 - 107645

Published: Nov. 3, 2021

Language: Английский

Citations

46