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INTELLIGENT ARCHITECTURES FOR SUSTAINABLE HOUSING: USE OF AI IN THE MANAGEMENT AND STORAGE OF RENEWABLE ENERGY

持続可能な住宅のためのインテリジェントアーキテクチャ：再生可能エネルギーの管理と貯蔵におけるAIの活用 (AI 翻訳)

WILLIAM NEVES DA SILVA, Ana Katherine Silveira Pereira Caracas, Ana Katherine Silveira Pereira Caracas, Emanuel Lopes Frate, Francisco Jeandson Rodrigues da Silva, Francisco José Lopes Cajado, Givanildo Ximenes Santana, Irene Mendes Fontes, Jaqueline de Paula Coutinho Nóbrega, Priscila Cabral Gomes, Roberto Augusto Caracas Neto, Wellington Assunção da Silva

Seven Editora eBooksジャーナル2026-05-04#再生可能エネルギー
DOI: 10.56238/sevened2026.019-022
原典: https://doi.org/10.56238/sevened2026.019-022
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🤖 gxceed AI 要約

日本語

本研究は、持続可能な住宅における再生可能エネルギーの管理と貯蔵を最適化するためのAIベースのインテリジェントアーキテクチャを分析する。質的アプローチにより、複数のデータソースから解釈的分析を行い、AIとエネルギー貯蔵システムの統合が住宅の効率性と自律性を高めることを示した。理論的枠組みの提示に留まるが、スマートホームとGXの接点を考察する上で示唆に富む。

English

This qualitative study analyzes how AI-based intelligent architectures can optimize renewable energy management and storage in sustainable housing. By integrating theoretical and documentary data, it demonstrates that combining AI, energy storage, and smart systems enhances residential energy efficiency and autonomy. While lacking empirical validation, it provides a conceptual framework for smart home-GX integration.

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

本論文は日本のGX文脈において、再生可能エネルギーの住宅利用とAI管理の可能性を提示する。SSBJや有報開示とは直接関係しないが、家庭部門の脱炭素化やエネルギー貯蔵技術の進展に関心のある実務者にとって参考となる。日本ではZEH（ネット・ゼロ・エネルギー・ハウス）政策との関連で注目される可能性がある。

In the global GX context

This paper contributes to the global GX context by exploring AI-driven energy management in residential buildings, a key area for demand-side decarbonization. It aligns with trends in smart grids, distributed energy resources, and building-integrated renewables. However, it lacks quantitative evidence or policy linkage, limiting its direct applicability to corporate disclosure or transition finance frameworks like TCFD/ISSB.

👥 読者別の含意

🔬研究者:Offers a conceptual framework for AI-renewable integration in housing, useful for further empirical studies.

🏢実務担当者:May inform smart home system designers and energy service companies on AI applications for residential energy management.

🏛政策担当者:Provides background for policies promoting AI-enabled energy efficiency in housing, but lacks concrete recommendations.

📄 Abstract（原文）

The growing integration between digital technologies and renewable energy sources has redefined how energy consumption is organized in residential environments, especially in the context of sustainable housing. The present study aims to analyze how intelligent architectures based on artificial intelligence can optimize the management and storage of renewable energy in sustainable housing, aiming at greater energy efficiency and residential autonomy. Regarding the research design, a qualitative approach was adopted, guided by the interpretative analysis of data from multiple sources. This methodological choice allows for examining the investigated phenomenon in greater detail, considering not only the data itself but also the contexts in which they are embedded and the meanings associated with the analyzed technologies. By articulating theoretical and documentary information, it becomes possible to develop a consistent analysis capable of integrating different perspectives and expanding the understanding of the topic in a more comprehensive and contextualized manner. In summary, the research demonstrated that the integration of intelligent architectures, artificial intelligence, and energy storage systems contributes to the development of more efficient and autonomous housing by enabling more precise and adaptive energy management. The articulation of these technologies promotes the rational use of renewable sources and fosters greater balance in residential energy consumption, consolidating a housing model aligned with current demands for efficiency and sustainability.

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。