With Elipsa’s auto-tagging capabilities, you can go from a list of equipment and point names to fully tagged equipment and points in a matter of minutes. Starting with a CSV file, you can import your information to be processed by Elipsa’s AI platform.
The sensitivity of the AI can be adjusted, with zero needing a near perfect match to an equipment or point name that the system has seen before and 10 providing a prediction even if it is a fairly low confidence.
Similar to how a human would look at RTU1 and know it is an RTU or VAV_102 and know it is a VAV, AI can make similar predictions. If the AI is wrong, you can utilize dropdowns to choose the correct type. Once equipment and points are added, the appropriate haystack tags are automatically applied. Additionally, the AI learning is updated to reinforce what it got right and correct what it got wrong.
For predicting point, we divide the equipment by type to make it easier to manage.
Looking at the RTUs, we show the imported points in orange and the mapped points in the dropdown. For equipment such as RTUs with multiple compressors, an AHU with multiple supply fans, or a chiller plant where all points are tied to chiller plant in your BMS rather than the individual equipment, you can add sub-equipment easily in the interface to create a connected system.
Similar to adding equipment, if the AI makes an incorrect prediction, you can choose the correct point in the dropdown. Saving the individual equipment will add the point to the learning. Refreshing the predictions will then be able to make an accurate prediction for all other equipment with similar named points.
Once we save an equipment type, the color indicator changes to green allowing us to keep track of what points have been added.
With equipment and points now mapped and saved, we can utilize the haystack tags to run features of the Elipsa platform just as fault detection and diagnostics or we can export the points to feed back into your BMS
To learn more, contact us at info@elipsa.ai