Performance optimization of hybrid renewable energy systems with real-time load forecasting using grey wolf-based predictive models
グレイウルフ最適化に基づく予測モデルを用いたハイブリッド再生可能エネルギーシステムの性能最適化とリアルタイム負荷予測 (AI 翻訳)
Awoniyi, Olumuyiwa Ajibola, Ashigwuike, Evans Chinemezu, Ejimofor, Chijioke, Araoye, Timothy Oluwaseun
🤖 gxceed AI 要約
日本語
本論文は、ハイブリッド再生可能エネルギーシステム（HRES）の性能最適化のために、グレイウルフ最適化（GWO）に基づくリアルタイム負荷予測モデルを提案する。太陽光・風力の変動性に対応し、エネルギー負荷と日射量を短期的に予測して蓄電・発電を最適化する。GWOアルゴリズムにより運用コストとCO2排出削減を実現し、よりスマートで強靭なエネルギーグリッドに貢献する。
English
This paper proposes a grey wolf optimization (GWO)-based real-time load forecasting model for hybrid renewable energy systems (HRES) performance optimization. It addresses solar and wind intermittency by forecasting load and irradiance to optimize storage and generation. The GWO algorithm reduces operational costs and carbon emissions, contributing to smarter and more resilient energy grids.
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
日本でも再生可能エネルギー導入拡大に伴い、HRESの効率的運用が課題となっている。本手法は、太陽光・風力の出力変動に対応する最適化技術として参考になり、特に離島や地域マイクログリッドでの活用が期待される。
In the global GX context
Globally, the integration of variable renewable energy sources requires advanced optimization for grid stability. This study demonstrates a practical application of AI (GWO) for real-time forecasting and dispatch, which is relevant for energy transition efforts and smart grid development worldwide.
👥 読者別の含意
🔬研究者:The paper offers a GWO-based predictive optimization method for HRES, useful for researchers in renewable energy systems and AI applications.
🏢実務担当者:Energy system operators can apply the real-time forecasting and optimization framework to improve efficiency and reduce costs in hybrid renewable installations.
📄 Abstract（原文）
The performance optimization of hybrid renewable energy systems (HRES) is crucial for enhancing the efficiency, reliability, and sustainability of energy production. This study focuses on the integration of real-time load forecasting prediction using a grey wolf optimization (GWO)-based predictive model. The proposed methodology aims to address the challenges associated with the intermittent nature of renewable energy sources, such as solar and wind power, by providing accurate forecasts for load demands and solar irradiance. Real-time data from sensors and environmental parameters are incorporated to forecast the energy load and solar irradiance over short term periods, which are then used to optimize the energy storage and generation components of the HRES. The GWO algorithm, known for its high accuracy and computational efficiency, is employed to optimize the dispatch of power from various sources while minimizing energy losses and ensuring system stability. The integration of GWO with real-time forecasting not only enhances the predictive capability of the system but also improves the overall economic viability of HRES by reducing operational costs and carbon emissions. This study demonstrates the potential of using intelligent optimization techniques and real-time forecasting for the sustainable operation of hybrid renewable energy systems, contributing to the development of smarter and more resilient energy grids.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20636819first seen 2026-06-12 04:21:30
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。