TCN-GRU Based on Attention Mechanism for Solar Irradiance Prediction

Energies, Journal Year: 2024, Volume and Issue: 17(22), P. 5767 - 5767

Published: Nov. 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.

Language: Английский

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Optimizing Lightweight Recurrent Networks for Solar Forecasting in TinyML: Modified Metaheuristics and Legal Implications

Energies, Journal Year: 2024, Volume and Issue: 18(1), P. 105 - 105

Published: Dec. 30, 2024

The limited nature of fossil resources and their unsustainable characteristics have led to increased interest in renewable sources. However, significant work remains be carried out fully integrate these systems into existing power distribution networks, both technically legally. While reliability holds great potential for improving energy production sustainability, the dependence solar plants on weather conditions can complicate realization consistent without incurring high storage costs. Therefore, accurate prediction is vital efficient grid management trading. Machine learning models emerged as a prospective solution, they are able handle immense datasets model complex patterns within data. This explores use metaheuristic optimization techniques optimizing recurrent forecasting predict from substations. Additionally, modified optimizer introduced meet demanding requirements optimization. Simulations, along with rigid comparative analysis other contemporary metaheuristics, also conducted real-world dataset, best achieving mean squared error (MSE) just 0.000935 volts 0.007011 two datasets, suggesting viability usage. best-performing further examined applicability embedded tiny machine (TinyML) applications. discussion provided this manuscript includes legal framework forecasting, its integration, policy implications establishing decentralized cost-effective system.

Language: Английский

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Forecasting Maximum Power Point in Solar Panels Using CNN-GRU

International Journal of Electrical and Electronics Engineering, Journal Year: 2024, Volume and Issue: 11(7), P. 215 - 227

Published: July 31, 2024

The use of hybrid Convolutional Neural Network- Gated Recurrent Unit (CNN-GRU) models for solar panel Maximum Power Point (MPP) prediction is examined in this work. Improved energy forecasting accuracy essential grid integration and power-generating optimization. A novel CNN-GRU architecture that captures both temporal spatial patterns present data using a dataset includes temperature, irradiance, MPP characteristics utilized. comparison study with alternative architectures individual GRU CNN models. Model performance evaluated by evaluation metrics such as coefficient determination (R²), Mean Squared Error (MSE), Absolute (MAE). Results show the model achieves better voltage (Vmp) current (Imp) at than architectures. Furthermore, residual analysis against actual comparisons prove efficacy robustness suggested method. practical ramifications renewable management stability advance methods.

Language: Английский

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