HYBRID CCO–SWO OPTIMIZATION FRAMEWORK FOR NET-ZERO SMART BUILDING ENERGY CONTROL
ネットゼロスマートビルエネルギー制御のためのハイブリッドCCO-SWO最適化フレームワーク (AI 翻訳)
M. RAJKUMAR, Dr. V. GOKULA KRISHNAN, Dr. P. JESU JAYARIN, R. SENTHILKUMAR, Dr. KARNAM SREENU, Dr. S. KAVIARAAN
🤖 gxceed AI 要約
日本語
本論文は、スマートビルディングのネットゼロエネルギー制御に向け、新たなハイブリッドメタヒューリスティック最適化手法（CCO-SWO）を提案。シミュレーションの結果、従来手法と比較して総エネルギー消費を31.5%、系統依存度を41.3%削減し、熱的快適性を維持しつつ高いネットゼロ指数(0.96)を達成。
English
This paper proposes a hybrid metaheuristic optimization framework (CCO-SWO) for net-zero energy control in smart buildings. Simulation results show a 31.5% reduction in total energy consumption, 41.3% less grid reliance, and a high Net-Zero Index of 0.96 while maintaining thermal comfort within 0.94°C deviation, outperforming standard methods like PSO, GWO, and HHO.
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、ZEB（ネット・ゼロ・エネルギー・ビル）普及が進んでおり、本手法はビルエネルギー管理システム（BEMS）の高度化に寄与する可能性がある。特に、再生可能エネルギー導入と快適性の両立が求められる日本の商業・業務用ビルに応用が期待される。
In the global GX context
Globally, net-zero building optimization is critical for decarbonization. This hybrid optimization approach offers a practical solution to balance energy efficiency, renewable integration, and occupant comfort, which are key challenges faced by building energy management systems worldwide.
👥 読者別の含意
🔬研究者:Introduces a novel hybrid optimizer (CCO-SWO) with variance-driven switching, providing a benchmark for future optimization studies in building energy.
🏢実務担当者:Offers a proven method to significantly reduce energy consumption and grid dependence in smart buildings, directly applicable to BEMS implementation.
🏛政策担当者:Demonstrates technical feasibility of achieving high net-zero performance, supporting policy targets for building decarbonization.
📄 Abstract（原文）
It is hard to get smart buildings to operate on net-zero energy mainly because energy consumption, occupant comfort, and renewable energy integration are nonlinear, multi-objective, and dynamic. Traditional optimization and rule-based control methods do not solve the problem effectively as they are less adaptive to changing environmental conditions and complex system interactions. As a result, they lead to suboptimal performance and increased reliance on the grid. In that regard, the present paper introduces an innovative hybrid metaheuristic optimization tool that combines the Centered Collision Optimizer (CCO) and Spider Wasp Optimization (SWO) for adaptive and real-time energy management. This new tool uses a variance-driven switching method to constantly adjust the balance between global exploration and local exploitation, thus preventing early convergence and improving the quality of the solution. Among other methods, the proposed CCO-SWO model has been shown to significantly outperform standard methods such as PSO, GWO, and HHO in EnergyPlus and MATLAB simulations. The report reveals a 31.5% drop in total energy consumption, a 41.3% less reliance on the grid, and an enhanced level of thermal comfort with a deviation of only 0.94C, whereas the high Net-Zero Index (NZI) of 0.96 was also maintained. These results prove the proposed framework as a highly efficient, reliable, and versatile approach for intelligent energy controlling in smart buildings strongly supporting sustainable and net-zero energy system deployment.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20253533first seen 2026-05-18 04:12:14 · last seen 2026-05-29 04:15:05
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