Problem
The objective of the case study was to develop accurate predictive models that could identify potential commercial HVAC equipment failures. The goal was to detect pre-failure conditions within a specific time frame to allow for proactive maintenance services to be deployed. In addition, the project involved analyzing extensive and diverse data sources, including legacy systems, third-party data, time-series data, and location data.
The Before State
- Develop accurate predictive models to identify commercial HVAC equipment that is trending toward failure
- Models must detect pre-failure conditions within a timeframe that allows proactive maintenance services to be deployed
- Large volumes of disparate data sources (legacy systems, third-party, time-series, location data)
What KPI Delivered
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A series of XGBoost classifiers that could predict individual unit pre-failure conditions with over 90% accuracy
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Harmonized data sets that included terabytes of sub-second time series IoT streams, third-party API weather forecasts, and building/mechanical features
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Integrated MLOps prediction API into existing proprietary dashboards
The After State
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Remote building engineers were now able to be alerted to likely HVAC failures up to 5 days in advance
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Significant reduction in unnecessary truck rolls, minimizing service technician dispatches, and optimizing operational efficiency
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Highly scalable and repeatable processes as additional sites and units come online
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