負荷予測によるスマートグリッド効率向上
Smart Grid Efficiency Enhancement through Load Forecasting (原題)
Preeti Manke, Dr. Sourabh Rungta, Dr. Satyadharma Bharti, Sharad Tembhurne
🤖 gxceed AI 要約
日本語
本論文は、電力系統の複雑化と再生可能エネルギーの統合に対応するため、グラフQネットワーク（GQN）を用いた新しい負荷予測モデルを提案する。従来手法と比較してMAPE 1.2%、RMSE 0.8 MWと高精度で、電力損失を最小化し、運用遅延を削減する。適応性が高く、スマートグリッドや再生可能エネルギー統合の高い系統に適用可能である。
English
This paper proposes a novel load forecasting model based on Graph Q Networks (GQNs) to address the challenges of modern power grids with high renewable integration. The model achieves superior accuracy (MAPE 1.2%, RMSE 0.8 MW) compared to existing methods, minimizing power losses and reducing operational delays. Its adaptability makes it suitable for smart grids and systems with high renewable penetration.
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
日本の電力系統は再生可能エネルギーの大量導入に伴い需給調整が課題となっており、高精度な負荷予測は系統運用の効率化と安定化に寄与する。本モデルは、日本のスマートグリッドや再生可能エネルギー統合の進む地域での適用が期待される。
In the global GX context
Globally, the integration of renewable energy sources into power grids demands advanced load forecasting to ensure stability and efficiency. This model offers a data-driven solution that can support grid operators in reducing losses and adapting to dynamic conditions, aligning with global energy transition goals.
👥 読者別の含意
🔬研究者:Provides a novel application of graph-based reinforcement learning to load forecasting, offering a benchmark for future research in smart grid optimization.
🏢実務担当者:Offers a practical tool for grid operators to improve load forecasting accuracy, reducing operational costs and enhancing grid stability.
🏛政策担当者:Highlights the potential of AI-driven forecasting in supporting renewable integration and grid modernization, informing policy on smart grid investments.
📄 Abstract（原文）
The demand for reliable and efficient power systems has been increasing day by day due to the increasing complexity of electrical grids and the integration of renewable energy sources. Traditional power load forecasting methods are struggling to keep pace with these evolving demands, often resulting in significant power losses and inefficiencies for different scenarios. Existing load forecasting models often face challenges in terms of accuracy, adaptability and computational efficiency. These models typically do not fully utilize the intricate relationships within power systems, leading to suboptimal predictions and higher power losses. Furthermore, they often suffer from delays in processing and adapting to real-time changes in the grid, thereby reducing their practical utility. To address these issues, this paper introduces a novel load forecasting model based on Graph Q Networks (GQNs). GQNs leverage the power of graph-based learning to effectively model complex interdependencies in power systems. The GQN model exhibits MAPE of 1.2% and RMSE of 0.8 MW of Method [12], significantly outperforming the most efficient method. The proposed model outperforms MAPE of 2.4% and RMSE of 1.9 MW of Method [13] most inefficient method among the studies. The proposed model allows for more accurate and dynamic load forecasting, significantly minimizing power losses. By integrating these methods with graph theory, the model not only enhances forecasting accuracy but also reduces operational delays. Moreover, its adaptability makes it suitable for a wide range of power systems to emerging smart grids and those with high renewable energy integration, further reinforce its utility.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/22097067first seen 2026-08-26 04:12:54 · last seen 2026-08-28 04:14:01
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