MQTT and Energy Monitoring Part 1

What a couple!
The Constraint
Recently we wanted to use a small test to see again the capabilities of Coreflux. So we teamed up with ABB, that had a client that have machines able to demand 150 KW of power easily while having 200KW solar panel installation to lower the energy costs.
Having information is good, you can make adjustments, but in the case of energy monitoring, you need to be remarkably quick to act. Cleary that data needs to select the best performance for your business. Some questions can help you drive into that correlation of data. Is the energy cost of this product better in this machine or another? Is the design of the part energy efficient?
In the end, we need to get the data from energy monitoring and from the production machines. So we needed to make in the end a system capable of predicting the best outcome in terms of energy efficiency. But for now, both companies decided to prove the concept to the client and do a Prototype test.
The Prototype
So usually, vendors designing energy monitoring devices are using Modbus to provide data to systems. We wanted to collect and distribute the data and ABB wasn’t any different to Modbus. So ABB lent us a very nice suitcase.
ABB — You cannot Improve what you cannot measure We used the ABB CMS-700 measuring simulated machine load, this would provide data to an MQTT broker inside the factory that with the Flux Bridge Asset we sent it to our MQTT platform server
The ABB CMS-700 fuse switch monitoring sensor In order to show the data in this prototype, we made a small 5-minute dashboard in the free tool Freeboard. Using the MQTT as a data provider. The last step was to prepare the Modbus Flux Asset. So we just went into our hub and installed and configured.
Since we are advanced users, we configure the container by hand. This was unnecessary but sometimes we like to make it harder.
All was set and ready to go!
The Results
We were mind blown with the capabilities of the integration, the device was pushing 489 variables at a rate of 12ms via Modbus. The events were arriving and we were able to draw charts with a proper refresh rate.
The Conclusion
The implementation with Coreflux was simple and scalable. The possibilities with this kind of integration are vast, one can create:

Control charging stations of electric-powered cars
Manage your production orders by deciding what machine to run and when
Turn your machines on with the most effective sequence
Check your energy balance with the production of your solar panels and your consumption

But all this can be made automatically, by using the C#, Python,, Node.js APIs you can run small programs to make these solutions. That will be the next step.
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