Automatic threshold selection of machine learning/deep learning model for anomaly detection of connected chillers
US11604441B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 21, 2018 |
| Grant date | Mar 14, 2023 |
| Priority date | — |
| Expiry date | Oct 16, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
A chiller threshold management system for a building, including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions to be executed on the one or more processors. The one or more processors are configured to determine whether chiller fault data exists in chiller data used to generate a plurality of chiller prediction models. The one or more processors are further configured to generate a first threshold evaluation value for each of the plurality of chiller prediction models using a first evaluation technique in response to a determination that chiller fault data exists in the chiller data, and generate a second threshold evaluation value for each of the chiller prediction models using a second evaluation technique in response to a determination that chiller fault data does not exist in the chiller data. The one or more processors are configured to select a first threshold for each of the plurality of chiller prediction models based on the first threshold evaluation values in response to the determination that chiller fault data exists in the chiller data, and select a second threshold for each of the plurality of…
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.