スマート照明と遠隔管理のユニットレベル分析：都市・産業・インテリジェント環境における省エネルギーと炭素フットプリント削減の技術的リファレンス

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a reproducible methodology for unit-level energy and carbon assessment of smart lighting, useful for comparative studies.

🏢実務担当者:Offers a practical framework to quantify energy savings and carbon reductions from smart lighting, aiding in sustainability reporting and cost-benefit analysis.

🏛政策担当者:Demonstrates a data-driven approach to evaluate smart city infrastructure investments, informing urban energy policy and climate targets.

Smart lighting is becoming a strategic component of intelligent and low-carbon urban infrastructure because it combines efficient illumination with connectivity, remote management, and continuous operational monitoring. This study proposes a reproducible unit-level methodological framework that transforms field records from a functional smart-lighting installation into traceable indicators of electrical performance, energy efficiency, avoided emissions, preliminary economic benefit, sensitivity, and conditional scalability. The approach treats the luminaire not only as an electrical load, but as a monitored urban energy node whose operation can be validated, characterized, and compared under planning-oriented control scenarios. The methodology integrates data preprocessing, electrical consistency assessment, representative baseline definition, scenario-based energy modeling, explicit environmental conversion, and conditional scaling to homogeneous lighting assets. The results reveal a stable electrical operating regime and show that managed operating conditions can generate sustained reductions in energy use and associated environmental impacts while preserving analytical transparency between measured variables and scenario-derived indicators. Sensitivity and multivariable analyses further support the robustness of the unit-level interpretation and highlight the value of monitored lighting data for comparative decision-making. The framework therefore provides a technically grounded reference for smart-city lighting management, energy planning, and scalable infrastructure assessment, with relevance to the objectives of SDG 7, SDG 11, and SDG 13. Overall, the study contributes an original data-driven perspective for integrating IoT-enabled lighting, remote supervision, and sustainability-oriented urban management within a common analytical structure.

• openalex https://doi.org/10.3390/smartcities9090137first seen 2026-08-26 04:46:32