Streaming tech helped PAUL optimize energy consumption, improving building efficiency
Data Streaming Technology Used:
What problem were they looking to solve with Data Streaming Technology?
The problem that PAUL is solving is of great significance as it involves reducing energy consumption in buildings by up to 40%. This is a crucial issue in today's world where energy conservation and sustainability are of utmost importance. Overall, PAUL's goal is to make buildings more efficient and save energy with minimal physical modifications to the existing infrastructure and without losing comfort for the people using the buildings.
How did they solve the problem?
PAUL integrates IoT devices via Sparkplug Edge of network nodes with Kafka using Kafka Connect. Confluent's MQTT Source Connector is used to subscribe to data topics on the MQTT broker to get the IoT data into Kafka. A custom partitioner ensures data from the same edge node are stored together to keep the true sequence of events for later processing. Once in Kafka topics, a set of Kafka Streams applications, leverage PAUL's ML models to optimize actuator values. The optimized values are sent back to the IoT devices using Confluent's MQTT Sink Connector.
What was the positive outcome?
PAUL's use of Kafka and Kafka Connect optimized energy consumption, making buildings more efficient by processing and optimizing data from sensors and actuators with PAUL's ML models. This reduced energy costs and promoted a sustainable use of resources, benefiting both their business operations and the environment.
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