article · Results in Engineering
This research introduces a novel hybrid deep learning approach to enhance the accuracy of short-term solar irradiance forecasting. The method integrates Bayesian Optimized Attention-Dilated Long Short-Term Memory with Savitzky-Golay filtering. Data from a solar irradiance probe in Douala, Cameroon, was used, with initial data augmentation and quality enhancement via a Bayesian optimised Savitzky-Golay filter. Various deep learning models were evaluated, and the proposed hybrid architecture, incorporating attention mechanisms and dilated convolutional layers, demonstrated superior performance in forecasting accuracy and convergence. Bayesian optimisation was also used to fine-tune the filter and model hyperparameters. The empirical findings indicate that this methodology significantly improves forecasting capabilities, surpassing previous studies.
Accurate short-term solar irradiance forecasting is crucial for effectively integrating solar energy into power grids. This research offers a more precise method for predicting solar energy availability, which can lead to more stable and efficient energy management, reducing reliance on fossil fuels and optimising renewable energy use.
This research provides an advanced forecasting tool that could be used by solar plant managers to optimise the operation and integration of solar energy systems into the power grid. The methodology offers improved accuracy for short-term predictions, which is valuable for energy scheduling and grid stability. It appears to be applied research, offering a refined technique that could be integrated into existing energy management software or systems, moving towards practical application for operational improvements.
AI-generated from the published abstract. Always read the original work before citing.
The optimization of solar energy integration into the power grid relies heavily on accurate forecasting of solar irradiance. In this study, a new approach for short-term solar irradiance forecasting is introduced. This method combines Bayesian Optimized Attention-Dilated Long Short-Term Memory and Savitzky-Golay filtering. The methodology is implemented to analyze data obtained from a solar irradiance probe situated in Douala, Cameroon. Initially, the unprocessed data is augmented by integrating distinctive solar irradiation variables, and the Savitzky-Golay filter with Bayesian Optimization is used to enhance its quality. Subsequently, multiple deep learning models, including Long Short-Term Memory, Bidirectional Long Short-Term Memory, Artificial Neural Networks, Bidirectional Long Short-Term Memory with Additive Attention Mechanism, and Bidirectional Long Short-Term Memory with Additive Attention Mechanism and Dilated Convolutional layers, are trained and evaluated. Out of all the models considered, the proposed approach, which combines the attention mechanism and dilated convolutional layers, demonstrates exceptional performance with the best convergence and accuracy in forecasting. Bayesian Optimization is further utilized to fine-tune the polynomial and window size of the Savitzky-Golay filter and optimize the hyperparameters of the deep learning models. The results show a Symmetric Mean Absolute Percentage Error of 0.6564, a Normalized Root Mean Square Error of 0.2250, and a Root Mean Square Error of 22.9445, surpassing previous studies in the literature. Empirical findings highlight the effectiveness of the proposed methodology in enhancing the accuracy of short-term solar irradiance forecasting. This research contributes to the field by introducing novel data pre-processing techniques, a hybrid deep learning architecture, and the development of a benchmark dataset. These advancements benefit both researchers and solar plant managers, improving solar irradiance forecasting capabilities.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.rineng.2024.102461
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
