Forecasting Maximum Power Point in Solar Panels Using CNN-GRU

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 7

Year of Publication : 2024

Authors : Diaa Salman, Yonis Khalif Elmi, Abdullahi Sheikh Mohamed, Yakub Hussein Mohamed

10.14445/23488379/IJEEE-V11I7P119

How to Cite?

Diaa Salman, Yonis Khalif Elmi, Abdullahi Sheikh Mohamed, Yakub Hussein Mohamed, "Forecasting Maximum Power Point in Solar Panels Using CNN-GRU," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 7, pp. 215-227, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I7P119

Abstract:

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

Keywords:

Solar energy forecasting, Maximum power point, Hybrid models, Predictive accuracy, Renewable energy optimization

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