Advanced Automated Machine Learning Framework for Photovoltaic Power Output Prediction Using Environmental Parameters and SHAP Interpretability

Results in Engineering, Год журнала: 2025, Номер unknown, С. 103838 - 103838

Опубликована: Янв. 1, 2025

Язык: Английский

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Real-Time Ultra Short-Term Irradiance Forecasting Using a Novel R-GRU Model for Optimizing PV Controller Dynamics

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105046 - 105046

Опубликована: Апрель 1, 2025

Язык: Английский

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A multi-scale spatial–temporal interaction fusion network for digital twin-based thermal error compensation in precision machine tools

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127812 - 127812

Опубликована: Май 1, 2025

Язык: Английский

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A comparative study of deep learning approaches for real-time solar irradiance forecasting

EPJ Web of Conferences, Год журнала: 2025, Номер 326, С. 05002 - 05002

Опубликована: Янв. 1, 2025

Accurate forecasting of Global Horizontal Irradiance (GHI) is critical for enhancing both grid stability and the efficiency solar energy systems. A comparative assessment several deep learning models presented in this study real-time GHI forecasting, specifically Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), a hybrid LSTM-GRU architecture. Approach performance evaluated using standard metrics, including MAE, RMSE, R². Findings indicate that while GRUs are computationally efficient, they struggle to maintain long-term temporal dependencies. In contrast, LSTMs effectively capture these dependencies, resulting improved accuracy. Notably, model outperforms individual architectures, achieving lowest MAE (12.931), RMSE (21.825), highest R² (0.996), thereby demonstrating superior predictive performance. These results highlight potential applications, improving forecast reliability stability. This advances irradiance methodologies, facilitating integration renewable sources effectiveness operations.

Язык: Английский

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A lightweight model for long-term multivariate time series forecasting via high-dimensional feature maps

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113208 - 113208

Опубликована: Май 1, 2025

Язык: Английский

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TCN-GRU Based on Attention Mechanism for Solar Irradiance Prediction

Energies, Год журнала: 2024, Номер 17(22), С. 5767 - 5767

Опубликована: Ноя. 18, 2024

The global horizontal irradiance (GHI) is the most important metric for evaluating solar resources. accurate prediction of GHI great significance effectively assessing energy resources and selecting photovoltaic power stations. Considering time series nature monitoring sites dispersed over different latitudes, longitudes, altitudes, this study proposes a model combining deep neural networks convolutional multi-step GHI. utilizes parallel temporal gate recurrent unit attention prediction, final result obtained by multilayer perceptron. results show that, compared to second-ranked algorithm, proposed improves evaluation metrics mean absolute error, percentage root square error 24.4%, 33.33%, 24.3%, respectively.

Язык: Английский

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Ultra Short-Term Forecasting for the Propulsion Energy Consumption of All-Electric Ships Based on TCFFA-GRU-Parallel Network

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103767 - 103767

Опубликована: Дек. 1, 2024

Язык: Английский

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Real-Time Mechanism Based on Deep Learning Approaches for Analyzing the Impact of Future Timestep Forecasts on Actual Air Quality Index of PM10

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103434 - 103434

Опубликована: Ноя. 1, 2024

Язык: Английский

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