| I&T Solution |
AI Energy Management Platform
(REF: S-1634) |
| Trial Project |
|
| Solution Feature |
- Automated Fault Detection (AFDD) and Diagnostics for predictive maintenance and Energy Visualisation to know equipment status
- AFDD is applicable to detect failures and evaluate the health of AHU parts. Thus, AFDD can detect and predict the parts failure before it happens.
- AFDD is built up by machine learning method and historical data which is handled by "Physics-Guided Machine Learning". It applies physical law to clean the bias data. With this AI model and real time data, AFDD predicts the fault by model search of AI model.
- Accuracy of predictions can enhance with increasing using time. The model will keep learning from an operating equipment. Model can learn with self-adjustment, if there are some changes of equipment.
- ADFF can be a cloud base. Data is not only from BMS or AHU, but also internet eg. weather forecast by API technology. Cloud base system provide better accessibility to know updated status of equipment.
|
| Trial Application and Expected Outcome |
- Build-up equipment models by existing BMS data with AI, machine learning and data cleaning technology.
- Predict AHU lifespan and component faults and preform predictive maintenance for AHU by model search.
- Develop a web-based dashboard with real-time AHU status including real-time equipment status, trend analysis and alerts.
- Accuracy evaluation of the AI model.
- Provide training, consultancies, and recommendations on system implementation.
|
| Additional Solution Information |
ATAL Smart Building Platform_2023.pdf
|
| Info on I&T Solution Provider |
| Solution Provider | : | ATAL Building Services Engineering Limited | | Address | : | 13/F, Island Place Tower, 510 King's Road, North Point, Hong Kong | | Contact Person | : | Mung Hong Lim, Tom |
| Position | : | Energy Consultant | | Tel | : | 63313669 | | Email | : |
tommung@atal.com | | Webpage | : | http://www.atal.com |
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