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May 29, 2026工程学研究与实用0 citationsOpen Access

AI驱动的智能家居场景化控制策略设计与能效优化研究

YLYing Liu涌凌涌 凌张张贵恒

Key Points

  • This research aims to design AI-driven control strategies for smart homes and optimize energy efficiency.
  • Analyzed key technologies including AI, IoT, cloud computing, and edge computing.
  • Detailed the design of contextual control strategies and energy optimization techniques.
  • Constructed models and implemented technical solutions for energy consumption analysis.
  • Identified effective AI methods (deep learning, reinforcement learning) enhancing smart home control.
  • Demonstrated significant energy savings through optimized control strategies and mechanisms.

Abstract

本文聚焦ai驱动的智能家居场景化控制策略设计与能效优化。剖析了ai、物联网、云计算与边缘计算等 关键技术，指出深度学习、强化学习、nlp等技术为场景化控制提供支撑，物联网实现设备互联，云边协同平衡算力 与实时性。详细阐述了场景化控制策略设计与能效优化策略，涵盖需求分析、模型构建、实现技术及能耗现状、优化 机制、技术实现。旨在通过ai技术实现智能家居的场景化控制与能效提升，为用户提供更便捷、节能的家居体验。

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Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a192d2dfab5b468c4415fa7https://doi.org/10.37155/2717-5316-0705-9
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Artificial Intelligence for Creating a Digitized Lifestyle and Multifaceted Applications: A Case Study in the Realm of Smart Home Technology2024
  2. 2A Review of Artificial Intelligence and Deep Learning Approaches for Resource Management in Smart Buildings2025 · 21 citations
  3. 3Shaping the Next Generation of AI-Integrated Smart Homes: Innovations in Intelligence, Security, Sustainability, and User Experience2025
  4. 4Artificial Intelligence-Driven User Interaction with Smart Homes: Architecture Proposal and Case Study2025 · 1 citations
  5. 5The Importance of AI-Enabled Internet of everything Services for Smart Home Management2024 · 5 citations