用AI驱动的物联网框架实现智能能耗管理
Introducing AI-Driven IoT Energy Management Framework
- 基于上下文决策与主动适应的系统架构
- 支持长期、短期预测及异常检测，降低能耗
- 适合需要电网稳定性的智慧能源场景
由于对技术进步的持续依赖，电力消耗已成为现代生活中的关键问题。通过减少用电量或遵循用电预测，可降低月度开支并提升电力可靠性。本文提出一个综合性框架，聚焦于物联网系统的上下文决策、主动适应与可扩展结构，建立有序开发流程以降低能耗并支持电网稳定。该框架包含长期与短期预测、异常检测，以及对定性数据的考量，所有能源管理决策均基于此。性能评估基于电力消耗时间序列数据，验证了框架的直接应用可行性。
原文摘要 · Abstract (English)
Power consumption has become a critical aspect of modern life due to the consistent reliance on technological advancements. Reducing power consumption or following power usage predictions can lead to lower monthly costs and improved electrical reliability. The proposal of a holistic framework to establish a foundation for IoT systems with a focus on contextual decision making, proactive adaptation, and scalable structure. A structured process for IoT systems with accuracy and interconnected development would support reducing power consumption and support grid stability. This study presents the feasibility of this proposal through the application of each aspect of the framework. This system would have long term forecasting, short term forecasting, anomaly detection, and consideration of qualitative data with any energy management decisions taken. Performance was evaluated on Power Consumption Time Series data to display the direct application of the framework.
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