Advanced Temporal Deep Learning Framework for Enhanced Predictive Modeling in Industrial Treatment Systems

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104158 - 104158

Опубликована: Янв. 1, 2025

Язык: Английский

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De-Trend First, Attend Next: A Mid-Term PV forecasting system with attention mechanism and encoder–decoder structure

Applied Energy, Год журнала: 2023, Номер 353, С. 122169 - 122169

Опубликована: Ноя. 6, 2023

Язык: Английский

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A point-interval wind speed forecasting system based on fuzzy theory and neural networks architecture searching strategy

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 132, С. 107906 - 107906

Опубликована: Янв. 28, 2024

Язык: Английский

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Enhanced PV Power Prediction Considering PM10 Parameter by Hybrid JAYA-ANN Model

Electric Power Components and Systems, Год журнала: 2024, Номер 52(11), С. 1998 - 2007

Опубликована: Март 4, 2024

The demand for electrical energy is continuously increasing in these days, particularly due to advancements the industrial sector. This surge has underscored importance of seeking alternative sources, with solar emerging as a standout option its low investment costs and environmental friendliness. However, variability photovoltaic power production, influenced by meteorological data, necessitates accurate prediction methods. To enhance precision predictions, incorporating new parameters alongside existing data advantageous. In this regard, study explores impact particulate matter (PM10) parameter on using artificial neural network (ANN) model JAYA-ANN. Comparing results based root mean squared absolute percentage errors reveals that hybrid JAYA-ANN consistently outperforms ANN persistence models. Notably, PM10 proves be significant input forecasting daily power.

Язык: Английский

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SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea

Electronics, Год журнала: 2024, Номер 13(11), С. 2071 - 2071

Опубликована: Май 27, 2024

We present SolarFlux Predictor, a novel deep-learning model designed to revolutionize photovoltaic (PV) power forecasting in South Korea. This uses self-attention-based temporal convolutional network (TCN) process and predict PV outputs with high precision. perform meticulous data preprocessing ensure accurate normalization outlier rectification, which are vital for reliable analysis. The TCN layers crucial capturing patterns energy data; we complement them the teacher forcing technique during training phase significantly enhance sequence prediction accuracy. By optimizing hyperparameters Optuna, further improve model’s performance. Our incorporates multi-head self-attention mechanisms focus on most impactful features, thereby improving In validations against datasets from nine regions Korea, outperformed conventional methods. results indicate that is robust tool systems’ management operational efficiency can contribute Korea’s pursuit of sustainable solutions.

Язык: Английский

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Improving multi-site photovoltaic forecasting with relevance amplification: DeepFEDformer-based approach

Energy, Год журнала: 2024, Номер 299, С. 131479 - 131479

Опубликована: Апрель 29, 2024

Язык: Английский

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FabricGAN: an enhanced generative adversarial network for data augmentation and improved fabric defect detection

Textile Research Journal, Год журнала: 2024, Номер 94(15-16), С. 1771 - 1785

Опубликована: Март 15, 2024

When deep learning is applied to intelligent textile defect detection, the insufficient training data may result in low accuracy and poor adaptability of varying types trained model. To address above problem, an enhanced generative adversarial network for augmentation improved fabric detection was proposed. Firstly, dataset preprocessed generate localization maps, which are combined with non-defective images input into training, helps better extract features. In addition, by utilizing a Double U-Net network, fusion defects textures enhanced. Next, random noise multi-head attention mechanism introduced improve model’s generalization ability enhance realism diversity generated images. Finally, we merge newly image original realize enhancement. Comparison experiments were performed using YOLOv3 object model on before after The experimental results show significant improvement five – float, line, knot, hole, stain increasing from 41%, 44%, 38%, 42%, 41% 78%, 76%, 72%, 67%, 64%, respectively.

Язык: Английский

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Solving few-shot problem in wind speed prediction: A novel transfer strategy based on decomposition and learning ensemble

Applied Energy, Год журнала: 2024, Номер 377, С. 124717 - 124717

Опубликована: Окт. 24, 2024

Язык: Английский

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Big data analytics for photovoltaic and electric vehicle management in sustainable grid integration

Journal of Renewable and Sustainable Energy, Год журнала: 2025, Номер 17(1)

Опубликована: Янв. 1, 2025

In recent years, integration of sustainable energy sources into power grids has significantly increased data influx, presenting opportunities and challenges for system management. The intermittent nature photovoltaic output, coupled with stochastic charging patterns high demands electric vehicles, places considerable strain on resources. Consequently, short-term forecasting output vehicle load becomes crucial to ensuring stability enhancing unit commitment economic dispatch. trends transition accumulate vast through sensors, wireless transmission, network communication, cloud computing technologies. This paper addresses these a comprehensive framework focused big analytics, employing Apache Spark that is developed. Datasets from Yulara solar park Palo Alto's have been utilized this research. focuses two primary aspects: generation the exploration user clustering addressed using artificial intelligence. Leveraging supervised regression unsupervised algorithms available within PySpark library enables execution visualization, analysis, trend identification methodologies both behaviors. proposed analysis offers significant insights resilience effectiveness algorithms, so enabling informed decision-making in area

Язык: Английский

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From LMP to eLMP: An accurate transfer strategy for electricity price prediction based on learning ensemble

Energy, Год журнала: 2025, Номер unknown, С. 135926 - 135926

Опубликована: Апрель 1, 2025

Язык: Английский

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