Introducing AI-Driven IoT Energy Management Framework

2025-11-29

AI Summary

To address high energy consumption, low prediction accuracy, and context-agnostic decision-making in modern power systems, this paper proposes an AI-driven IoT-based energy management framework. The framework integrates multi-timescale (long- and short-term) time-series forecasting, real-time anomaly detection, and qualitative modeling of contextual factors—such as user behavior and environmental semantics—to enable context-aware proactive decision-making. Its key contributions are: (1) a unified, scalable IoT architecture supporting heterogeneous data integration and edge-cloud collaborative analytics; and (2) the first incorporation of qualitative knowledge into the energy consumption prediction–control closed loop, enhancing both interpretability and adaptability of decisions. Experiments on real-world power time-series data demonstrate an 18.7% reduction in prediction error, an anomaly detection F1-score of 0.92, and a 12.3% average monthly electricity cost reduction—significantly improving grid stability and energy efficiency management.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Develops an AI-driven IoT framework for energy management
• Focuses on reducing power consumption and improving grid stability
• Integrates forecasting, anomaly detection, and qualitative data analysis

Innovation

• AI-driven IoT framework for energy management
• Proactive adaptation with long and short-term forecasting
• Anomaly detection integrating qualitative data analysis