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占有計画管理の40年：建物エネルギー性能とスマートビルシステムの社会技術的共進化フレームワーク

Forty Years of Occupancy Planning Management: A Socio-Technical Co-Evolution Framework for Building Energy Performance and Smart Building Systems (原題)

Valero A

Research Squareプレプリント2026-08-28#省エネOrigin: Global経営インパクト: コスト削減対象セクター: real_estate
DOI: 10.20944/preprints202608.2089.v1
原典: https://doi.org/10.20944/preprints202608.2089.v1

🤖 gxceed AI 要約

日本語

本レビューは、1980年代のCADからAI自律制御までの占有計画管理（OPM）技術の進化を体系的に分析し、技術的能力が必ずしもエネルギー性能向上に結びつかない理由を社会技術的共進化フレームワークで説明する。占有応答型HVAC制御によるエネルギー削減可能性を示す一方、行動モデルの質がセンサー機器と同等に重要であると指摘する。

English

This systematic review traces the co-evolution of Occupancy Planning Management (OPM) technologies from 1980s CAD to AI-enabled autonomous control, analyzing why technical capability often fails to translate into energy performance gains. It introduces a Socio-Technical Co-Evolution Framework and shows that occupancy-responsive HVAC can reduce energy use, but behavioral model quality is as critical as sensor hardware.

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ZEB化や省エネ法改正に伴うビルエネルギー管理の高度化に示唆を与える。特に、技術導入だけでなく組織慣行や規制の整合が重要とするフレームワークは、日本のビルオーナーやESCO事業者にとって参考になる。

In the global GX context

This review contributes to global discourse on building decarbonization by highlighting the socio-technical barriers to energy-efficient smart buildings. It offers a framework relevant to ISSB-aligned disclosure and transition finance, as it underscores the need for robust governance and behavioral factors in energy performance.

👥 読者別の含意

🔬研究者:Provides a comprehensive framework and research agenda for studying socio-technical dynamics in building energy management.

🏢実務担当者:Offers practical guidance on aligning technology, organizational practices, and regulatory context to achieve energy savings in smart buildings.

🏛政策担当者:Highlights the importance of regulatory alignment and privacy-preserving sensing for effective building energy policies.

📄 Abstract（原文）

The transition to occupancy-responsive building energy management is one of the most consequential and least-understood energy transitions in the commercial built environment. This systematic review traces how Occupancy Planning Management (OPM) technologies have co-evolved with organisational practices and regulatory regimes over four decades, from 1980s CAD platforms through IoT sensor networks to AI-enabled autonomous control, and examines why technical capability has repeatedly failed to translate into measurable energy performance gains. A PRISMA 2020-aligned search across six databases yielded 103 sources (72 peer-reviewed; 31 AACODS-appraised grey literature). Bibliometric analysis identifies three citation clusters: building energy performance, facility management strategy, and AI-driven occupancy prediction. Drawing on IEA EBC Annexes 53, 66, 79, and 95, the review demonstrates that occupancy-responsive HVAC control can reduce energy use substantially, but that the accuracy ceiling of demand-responsive building management is determined as much by behavioural model quality as by sensor hardware, a finding with direct implications for decarbonisation investment. An original Socio-Technical Co-Evolution Framework explains why energy transitions in OPM succeed only when technological capability, organisational paradigm, and socio-regulatory context align simultaneously, accounting for patterns that technology-centred adoption models cannot. A governance quadrant for privacy-preserving occupancy sensing, a standardised four-level occupancy lexicon, and a prioritised ten-gap research agenda complete the practical contribution.

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