Summary of Development Of Low-cost Iot Units For Thermal Comfort Measurement and Ac Energy Consumption Prediction System, by Yutong Chen et al.
Development of Low-Cost IoT Units for Thermal Comfort Measurement and AC Energy Consumption Prediction System
by Yutong Chen, Daisuke Sumiyoshi, Riki Sakai, Takahiro Yamamoto, Takahiro Ueno, Jewon Oh
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Japanese government’s BI-Tech project aims to promote voluntary energy-saving behaviors in buildings using AI and IoT technologies. This study introduces a cost-effective IoT-based BI-Tech system for small and medium-sized office buildings, utilizing the Raspberry Pi 4B+ platform for real-time monitoring of indoor thermal conditions and air conditioner set-point temperature. The system employs machine learning and image recognition to analyze data and predict energy consumption changes due to temperature adjustments. A mobile and desktop application integrates with the system to provide users with information on their energy usage, encouraging behavior modifications to promote energy efficiency. The study’s machine learning model achieved an R2 value of 97%, demonstrating its effectiveness in promoting energy-saving habits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Japanese government wants people to save energy in buildings by using AI and technology. They made a special system that can help small offices use less energy. It uses tiny computers called Raspberry Pi to measure how hot or cold the office is, and how much energy is being used. The system also looks at pictures of the temperature gauges to predict when people will need to turn up or down the air conditioning. A smartphone app helps people see how they’re using energy and makes suggestions for them to use less. This system worked really well, with a special machine learning model that was right 97% of the time. |
Keywords
» Artificial intelligence » Machine learning » Temperature
