Introducing AI-Driven IoT Energy Management Framework

TL;DR

This paper proposes a comprehensive AI-driven framework for IoT energy management that enhances power efficiency, predictive accuracy, and grid stability through contextual decision-making and scalable architecture.

Contribution

It introduces a novel holistic framework integrating long-term and short-term forecasting, anomaly detection, and qualitative data analysis for IoT energy management.

Findings

Framework effectively reduces power consumption.

Demonstrated high accuracy in power usage predictions.

Supports grid stability through proactive adaptation.

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.