An Efficient Photovoltaic Power Forecasting using Adaptive Learning Rate Enhanced Gated Recurrent Unit (ALRE-GRU) network optimized with Enhanced Dynamic Grasshopper Optimization Algorithm (EDGOA)
適応学習率強化ゲート付き回帰ユニット（ALRE-GRU）ネットワークと強化動的バッタ最適化アルゴリズム（EDGOA）を用いた高効率太陽光発電予測 (AI 翻訳)
Parchami J, Darroudi A, Ali A, Al-kaabi H, Al-ibraheemi FA
🤖 gxceed AI 要約
日本語
本論文は、変分モード分解（VMD）と適応学習率強化ゲート付き回帰ユニット（ALRE-GRU）を組み合わせ、強化動的バッタ最適化アルゴリズム（EDGOA）でハイパーパラメータを最適化する太陽光発電予測手法を提案。オーストラリアの実データで評価し、R²99.91%と高精度を達成。再生可能エネルギーの安定運用に貢献する。
English
This paper proposes a hybrid framework combining Variational Mode Decomposition (VMD) with an Adaptive Learning Rate Enhanced Gated Recurrent Unit (ALRE-GRU) optimized by the Enhanced Dynamic Grasshopper Optimization Algorithm (EDGOA) for photovoltaic power forecasting. Using real data from Australia, it achieves high accuracy (R² 99.91%), demonstrating effectiveness for short-term and long-term solar forecasting, which supports stable renewable energy integration.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも太陽光発電の導入が進んでおり、出力変動対策として高精度な予測技術が重要。本手法は日本企業の再エネ運用や電力系統の安定化に応用可能。
In the global GX context
Accurate PV forecasting is critical globally for integrating solar power into grids. This method offers a robust AI-based solution that can be adopted by utilities and energy managers to improve operational efficiency and support the energy transition.
👥 読者別の含意
🔬研究者:AIと再生可能エネルギー予測の融合に興味ある研究者は、VMD+GRU+メタヒューリスティクスの組み合わせ手法を参考にできる。
🏢実務担当者:太陽光発電所の運用管理者は、本手法を導入することで予測精度向上によるコスト削減や系統安定化が期待できる。
🏛政策担当者:再エネの大量導入を進める政策立案者は、系統安定化のための予測技術の重要性を認識し、関連研究への支援を検討すべき。
📄 Abstract（原文）
<title>Abstract</title> <p>Accurate photovoltaic (PV) power forecasting is essential for the reliable operation of renewable energy systems due to the nonlinear and non-stationary nature of solar power generation. This study proposes a hybrid framework that integrates Variational Mode Decomposition (VMD) with an Adaptive Learning Rate Enhanced Gated Recurrent Unit (ALRE-GRU) network optimized using the Enhanced Dynamic Grasshopper Optimization Algorithm (EDGOA). VMD is applied to decompose PV power signals into multiple band-limited intrinsic modes, improving data quality through noise reduction and feature separation. The decomposed modes are used as inputs to the ALRE-GRU model, which employs a parameter-wise adaptive learning rate strategy based on first- and second-order gradient moments to enhance training stability and convergence speed. EDGOA is further utilized to optimally tune critical hyperparameters, particularly the hidden layer size. The incorporation of dynamic inertial motion weights and an adaptive mutation probability coefficient within the Enhanced Dynamic Grasshopper Optimization Algorithm (EDGOA) effectively mitigates the risk of premature convergence to suboptimal local optima. The proposed method is evaluated using real-world PV data from the Yulara Solar System in Australia for short-term (60-hour) and long-term (300-hour) forecasting. Experimental results demonstrate superior performance, achieving an R² of 99.91%, RMSE of 11.41, MAE of 8.51, and MAPE of 0.70%, outperforming several state-of-the-art forecasting models. These results confirm the effectiveness and robustness of the proposed framework for photovoltaic power forecasting applications.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-9797800/v1first seen 2026-06-11 04:37:06
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