Paradigm Shift for Predictive Maintenance and Condition Monitoring from Industry 4.0 to Industry 5.0: A Systematic Review, Challenges and Case Study

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 102935 - 102935

Published: Sept. 1, 2024

Language: Английский

---

Optimal load forecasting and scheduling strategies for smart homes peer-to-peer energy networks: A comprehensive survey with critical simulation analysis

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102188 - 102188

Published: May 3, 2024

The home energy management (HEM) sector is going through an enormous change that includes important elements like incorporating green power, enhancing efficiency forecasting and scheduling optimization techniques, employing smart grid infrastructure, regulating the dynamics of optimal trading. As a result, ecosystem players need to clarify their roles, develop effective regulatory structures, experiment with new business models. Peer-to-Peer (P2P) trading seems be one viable options in these conditions, where consumers can sell/buy electricity to/from other users prior totally depending on utility. P2P enables exchange between prosumers, thus provide more robust platform for This strategy decentralizes market than it did previously, opening up possibilities improving trade customers Considering above scenarios, this research provides extensive insight structure, procedure, design, platform, pricing mechanism, approaches, topologies possible futuristic while examining characteristics, pros cons primary goal determining whichever approach most appropriate given situation HEMs. Moreover, HEMs load framework simulation model also proposed analyze network critically, paving technical directions scientific researchers. With cooperation, age technological advancements ushering intelligent, interconnected, reactive urban environment are brought life. In sense, path living entails reinventing as well how people interact perceive dwellings larger city. Finally, work comprehensive overview challenges terms strategies, solutions, future prospects.

Language: Английский

---

Wind power forecasting method of large-scale wind turbine clusters based on DBSCAN clustering and an enhanced hunter-prey optimization algorithm

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 307, P. 118341 - 118341

Published: March 28, 2024

Language: Английский

---

Energy consumption prediction in water treatment plants using deep learning with data augmentation

Results in Engineering, Journal Year: 2023, Volume and Issue: 20, P. 101428 - 101428

Published: Sept. 26, 2023

Wastewater treatment plants (WWTPs) are energy-intensive facilities that play a critical role in meeting stringent effluent quality regulations. Accurate prediction of energy consumption WWTPs is essential for cost savings, process optimization, regulatory compliance, and reducing carbon footprint. This paper introduces an efficient approach predicting WWTPs, leveraging deep learning models, data augmentation, feature selection. Specifically, Spline Cubic interpolation enriches the dataset, while Random Forest model identifies important features. The study investigates impact lagged to capture temporal dependencies. Comparative analysis five models on original augmented datasets from Melbourne WWTP demonstrates substantial performance improvement with data. Incorporating further enhances accuracy, providing valuable insights effective management. Notably, Long Short-Term Memory (LSTM) Bidirectional Gated Recurrent Unit (BiGRU) achieve Mean Absolute Percentage Error (MAPE) values 1.36% 1.436%, outperforming state-of-the-art methods.

Language: Английский

---

Bearings faults and limits in wind turbine generators

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101891 - 101891

Published: Feb. 8, 2024

The detection of sudden faults in wind turbine generator (WTG) is a complex task, especially bearings. Usually, the evaluation methodologies such as vibration, ultrasound, and bearing temperatures are widely used predictive maintenance, an important aspect for traditional approach, fault detection, limited analysis with single variable or temperature. For instance, these sensors detect 5–20% torsional vibration drivetrain 55% has failure due to lubricant problem, 20% solid contamination 9% incorrect application bearing. Consequently, solve this limitation failures modes, research evaluated limits focused on early generators; it utilized multi-stage approach involving Random Forest, XGBoost, Light XGB, Logistic Regression, followed by probability scores optimal features search grid validation, addition, validated results through finite element modeling, Boroscopy, analysis. Hence, database considers bearings, gearboxes, normal operation; regarding 8,711,808 samples validating process. result study five days before high classification accuracy 99.994%, recall 99.982%, F1 score 98.124%, kappa 99.330%, test set time 22.82 s. This new provides compared bearings gearboxes.

Language: Английский

---

Designing and prototyping the architecture of a digital twin for wind turbine

International Journal of Thermofluids, Journal Year: 2024, Volume and Issue: 22, P. 100622 - 100622

Published: March 5, 2024

This paper outlines the key components necessary to develop a digital twin (DT) for wind turbine, aiming provide detailed methodology and guidelines building this system, which facilitates optimization during operation helps prevent system failures. It presents four major systems required construct DT: physical, digital, connection, service systems. study also critical design, measured, calculated parameters of are essential development DT. The physical turbine is examined, components, including rotor, blades, shaft, generator, tower, nacelle, discussed in detail. explores DT, data storage, models, mathematical modelling. problems that may occur were presented addition possible solutions must suggest. According project's needs requirements, it was found DT can employ various connection such as supervisory control acquisition, wireless sensor networks, smart grids, Internet Things, cloud-based

Language: Английский

---

Enhancing wind power prediction with self-attentive variational autoencoders: A comparative study

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102504 - 102504

Published: July 14, 2024

Accurate wind power prediction is critical for efficient grid management and the integration of renewable energy sources into grid. This study presents an effective deep-learning approach that improves short-term forecasting accuracy. The method incorporates a Variational Autoencoder (VAE) with self-attention mechanism applied in both encoder decoder. empowers model to leverage VAE's strengths time-series modeling nonlinear approximation while focusing on most relevant features within data. effectiveness this evaluated through comprehensive comparison eight established deep learning methods, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Bidirectional LSTMs (BiLSTMs), Convolutional (ConvLSTMs), Gated Units (GRUs), Stacked Autoencoders (SAEs), Restricted Boltzmann Machines (RBMs), vanilla VAEs. Real-world data from five turbines France Turkey used evaluation. Five statistical metrics are employed quantitatively assess performance each method. results indicate SA-VAE consistently outperformed other models, achieving highest average R2 value 0.992, demonstrating its superior predictive capability compared existing techniques.

Language: Английский

---

A Comprehensive Survey on Load Forecasting Hybrid Models: Navigating the Futuristic Demand Response Patterns through Experts and Intelligent Systems

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102773 - 102773

Published: Aug. 24, 2024

Language: Английский

---

A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 350 - 350

Published: Jan. 15, 2025

Wind power prediction is essential for ensuring the stability and efficient operation of modern systems, particularly as renewable energy integration continues to expand. This paper presents a comprehensive review machine learning techniques applied wind prediction, emphasizing their advantages over traditional physical statistical models. Machine methods, especially deep approaches such Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), ensemble like XGBoost, excel in addressing nonlinearity complexity data. The also explores critical aspects data preprocessing, feature selection strategies, model optimization techniques, which significantly enhance accuracy robustness. Challenges acquisition difficulties, complex terrain influences, sensor quality issues are examined depth, with proposed solutions discussed. Additionally, highlights future research directions, including potential multi-model fusion, emerging technologies Transformers, smart sensors IoT develop intelligent, automated, reliable systems. By existing challenges leveraging advanced this work provides valuable insights into current state offers strategic guidance enhancing applicability reliability models practical scenarios.

Language: Английский

---

Forecasting the power generation at renewable power plants in Sri Lanka using regression trees

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102111 - 102111

Published: April 8, 2024

This paper presents the application of regression trees as a versatile alternative to other machine learning and statistical modelling techniques forecast power generation at five renewable plants: one large hydropower plant, two mini plants, wind farms in Sri Lanka. The prediction models for each station were developed by varying depth tree. tree model with lowest that forecasts output (power) terms all predictor variables was selected accuracy evaluated means Mean Absolute Error (MAE), Percentage (MAPE), Root Squared (RMSE), Coefficient Determination (R2). According degree above performance indicators, i.e. very low values MAE, MAPE, RMSE supplemented R2 0.95 or more, method proved be convenient forecasting technique predict both hydro plants. Further, it could found good correlation between input paves way smaller Moreover, presented here accurately identify relationship generated most influential weather factors, without being affected potential outliers missing while managing collinearity too. Extension this study would enable generalize based on method, leading towards minimizing use fossil fuel.

Language: Английский

---