Most buildings including small and mid-sized commercial office building managers are unaware and unable to assess all fault points (such as sensor failures, outside air damper failures etc) within their energy systems, causing higher energy consumption and therefore higher energy costs. Several Fault detection and Diagnostics platform is trying to to address visualization issues. While this seems to work on Facilities with existing Sensor data (from a Building automation system). However fails to address these issues with minimal sensor data, In addition, one of the barriers for these platforms is developing a business case, thankfully application of Data Science with AI is helping address these problems of visualization. The Facilities also rely on the software vendors that uses specialize programming language to develop these “fault rules”.

The proposed platform is a three phased solution: (1) Use of Simple English language – The AI platform has the capability to write fault algorithm in simple English language.

 (2)It has the capability to Use of Sensor data from Building automation system or simple data loggers to detect anomalies. It can work in an offline mode with CSV/ Excel input files.

 (3) AI based Anomaly detection module – The platform uses a Deep learning, module to review data patterns and generate new fault rules