Innovative fuzzy reinforcement learning based energy management for smart homes through optimization of renewable energy resources with starfish optimization algorithm
スマートホームのための革新的ファジィ強化学習ベースのエネルギー管理：スターフィッシュ最適化アルゴリズムによる再生可能エネルギー資源の最適化 (AI 翻訳)
Mohammad Mahdi Kordian Hamedani, Alireza Jahangiri, R. Mehri, A. Shamim
🤖 gxceed AI 要約
日本語
本研究では、ハイブリッド再生可能エネルギーシステム（HRES）の電力フロー最適化のため、ファジィ強化学習とスターフィッシュ最適化アルゴリズム（SFOA）を用いたエネルギー管理システム（Fuzzy-EMS）を提案する。太陽光、風力、蓄電池、電気自動車を統合し、固定・リアルタイム・前日価格モデルで35.2%、23.8%、26.43%のコスト削減を達成。さらにディーゼル比較で二酸化炭素排出量を11.87-18.7%削減し、再生可能エネルギー利用率を最大70%向上させる。
English
Proposes fuzzy reinforcement learning based energy management system (Fuzzy-EMS) optimized by Starfish Optimization Algorithm (SFOA) for hybrid renewable energy systems (HRES) including PV, wind, batteries, and EVs. Achieves cost reductions of 35.2-26.43% under various pricing models and reduces carbon emissions by 11.87-18.7% compared to diesel, with up to 70% renewable utilization. Simulated in MATLAB over 20 years.
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
日本では再生可能エネルギーの導入拡大とスマートホームの普及が進んでおり、本論文のファジィ強化学習を用いたHEMS最適化手法は、日本の住宅用エネルギー管理システムの効率化に示唆を与える。特に電気自動車の活用やリアルタイム価格対応は、日本の電力市場改革とも関連する。
In the global GX context
This paper contributes to global GX by proposing an adaptive energy management system for HRES that combines fuzzy logic, reinforcement learning, and starfish optimization. It demonstrates significant cost and carbon emission reductions, relevant for residential sector decarbonization worldwide. The multi-objective framework balancing cost and renewable utilization aligns with net-zero goals.
👥 読者別の含意
🔬研究者:Researchers in smart grid and energy optimization can adopt the fuzzy reinforcement learning framework and SFOA for further HEMS studies.
🏢実務担当者:Home energy management system developers can apply the proposed algorithm to reduce operational costs and carbon footprint in real-world deployments.
🏛政策担当者:Policymakers can reference the demonstrated cost-effectiveness of renewables plus storage when designing incentives for smart home technologies.
📄 Abstract（原文）
Population growth and economic development have increased the world’s energy consumption, making it more difficult to manage peak loads and lower the cost of home energy management systems (HEMS). This has led to a need for smart, flexible solutions that incorporate renewable resources to improve sustainability and economic efficiency. To optimize power flow in a hybrid renewable energy system (HRES), this study suggests a fuzzy logic-based energy management system (Fuzzy-EMS) that is improved with reinforcement learning and optimized using the Starfish Optimization Algorithm (SFOA). It incorporates solar photovoltaic (PV), wind turbines (WT), battery storage systems (BSS), and electric vehicles (EVs). Adaptive handling of uncertainties in renewable generation and load demand using a Takagi–Sugeno fuzzy reinforcement learning model with triangular membership functions and 81 rules, real-time energy trading with the upstream grid, and a multi-objective framework that balances cost minimization and renewable utilization maximization are among the main contributions. Cost reductions of 35.2%, 23.8%, and 26.43% under fixed pricing, real-time pricing (RTP), and day-ahead pricing (DAP) models, respectively, are examples of how MATLAB simulations outperform well-known techniques. Furthermore, the system outperforms diesel-based systems by lowering operating costs and carbon emissions by 11.87–18.7% and increasing the use of renewable energy by up to 70% in hybrid scenarios, resulting in a net present cost (NPC) of $269,246 and a levelized cost of electricity (LCOE) of $0.281 over a 20-year period.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1038/s41598-026-40247-6first seen 2026-05-15 17:34:03
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