BES ANNとMCDMを統合したインテリジェント・フレームワークによる農村建築設計の最適化
Zhongcheng Duan1, Renyong Zhang1, Yidi Zhao2
1School of Architecture and Design, China University of Mining and Technology, Xuzhou, 221116, China.
Scientific reports
|August 27, 2025
まとめ
この研究は,エネルギー使用,快適さ,コストのバランスをとって,農村部の建物の設計を最適化するための統合された枠組みを紹介しています. この最適化されたモデルはエネルギー消費と不快感を大幅に削減し エネルギー効率の良い農村建設の 複製可能な経路を提供します
科学分野:
- 建築科学
- 持続可能な建築
- コンピュータによる設計
背景:
- 田舎の建物は エネルギー消費,熱的快適さ,建設コストのバランスをとる上で 課題に直面しています
- 既存の最適化方法は,高次元の設計空間と苦戦し,コンバージェンスが遅く,計算の複雑さにつながります.
研究 の 目的:
- 田舎の建物の性能最適化のための新しい枠組みを提案し,検証する.
- ビルディングエネルギーシミュレーション (BES),人工ニューラルネットワーク (ANN),マルチクリテリア意思決定 (MCDM) を統合して,多目的の最適化を行う.
- エネルギー効率の良い農村構造の設計における予測の精度と効率を改善する.
主な方法:
- フレームワークは,敏感性分析のモリス方法を使用して,バッチシミュレーションのためにDesignBuilderとJePlusを統合します.
- MATLABでは,パフォーマンスの予測のための人工ニューラルネットワーク (ANN) モデルが開発されました.
- 多基準意思決定 (MCDM) のコンポーネントでは,最適の設計ソリューションを選択するためにTOPSISアプローチが採用されました.
主要な成果:
- 最適化されたモデルは,総エネルギー消費を61.64%,不快な時間を32.04%大幅に削減しました.
- この最適化は73,5196円の追加建設コストで達成され,バランスのとれたパフォーマンスの改善を示しています.
- 提案された枠組みは,従来の方法と比較して,予測の精度と最適化効率の向上を示した.
結論:
- 開発された BES-ANN-MCDM フレームワークは,田舎の建物の設計を最適化するための新しく効果的なアプローチを提供します.
- この研究は,再現可能な経路と,パフォーマンス主導のエネルギー効率の良い農村建築設計のための理論的基礎を提供します.
- 田舎の住宅のエネルギー効率と 暖房の快適さを大幅に改善する可能性を強調しています
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