Introducing AI-Driven IoT Energy Management Framework
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911294176624640 |
|---|---|
| author | Mruthyunjaya, Shivani Dutta, Anandi Islam, Kazi Sifatul |
| author_facet | Mruthyunjaya, Shivani Dutta, Anandi Islam, Kazi Sifatul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00321 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Introducing AI-Driven IoT Energy Management Framework Mruthyunjaya, Shivani Dutta, Anandi Islam, Kazi Sifatul Machine Learning Systems and Control 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. |
| title | Introducing AI-Driven IoT Energy Management Framework |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2512.00321 |