Fault/Peak Power Consumption Prediction for a HVAC Manufacturer

Overview


VNB assist a leader in HVAC manufacturing to predict faults on power consumption using a PowerBI energy dashboard and Azure Analytics.

Customer Profile

A popular and well-known manufacturing division in a large conglomerate, builds and maintains large heating and cooling system for buildings, throughout the United States and many foreign countries. the customer was interested in a Power BI energy dashboard.

 

Customer Situation


power bi energy dashboard

Customer needed a proactive method to determine when peak power consumption occurs on its HVAC systems based upon many factors including fault prediction. Customer wanted to determine peak power consumption in advance so that scale back can occur to reduce building operating costs. It also wanted to predict faults to allow for optimal scheduling of maintenance personal for off-peak corrective actions. Customer reached out to VNB advance analytics team to give a predictive Power BI energy dashboard solution that is scalable, flexible and secure.

VNB Solution


azure analytics services

VNB advance analytics team proposed an analytics solution on Azure platform leveraging Azure Event Hub, Azure Data Factory, Azure Data Lake, Azure Machine Learning and Power BI. Using sensor and location data provided by the Customer VNB data scientist team created and trained models leveraging Azure Machine learning Studio. Following are the important activities performed during the project: Followed Data Science Process Lifecycle (TDSP) for this implementation Gathered unstructured data using Azure Event Hubs and structured Data Azure Data Factory. Created an Azure Data Lake Store within the subscription for unstructured and structured data. Created an Azure Machine Learning Server within the subscription to train, perform all experiments. Created R scripts to be run within the models. Wrangled and normalized the input sensor data within the experiment, adding weather data specific to the site locations and other types of inputs to optimize the solution Leveraged PowerBI data visualizations to output the results of the predictive models

Customer Benefits

  • Customer was able to reduce cost for occupants of the buildings they manage, while being able to prevent long term outages due to preventative maintenance benefits, provided by predicting failures before they occur.
  • The new predictive solution helped customer be more proactive in resolving HVAC system faults resulting in improved customer service.

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