17FeatUre The energy efficient Grundfos Kollegiet building offers students an attractive living environment in the new harbor district of Århus State of the art energy automation linked to Microsoft s Azure cloud makes it possible to identify even the smallest energy savings potential having to modify the actual TwinCAT automation project protecting the investments made in existing systems Using cloud based services also makes it possible to flexibly adapt systems to changing needs without having to invest in your own hardware or software which also significantly reduces operator costs handles all message addressing functions From the perspective of the firewall placed in front of the gateway PC the data communication provides an encrypted link for both transmitted and received messages and the firewall makes it possible to completely block all incoming communications thus preventing any unwanted access from the outside This protects the residents personal data the companies intellectual property and building operations from accidental or intentional manipulation The Data Agent s graphical user interface GUI makes it easy to configure the sensor data for transmission to the Azure IoT Hub Through various parameters the administrator can also define when the transmission will be initiated cyclically when certain values change or when certain actions are executed Internal buffering mechanisms also ensure that any missing sensor data will be transmitted after a power failure If the connection fails the TwinCAT IoT Data Agent records a timestamp As soon as the connection has been restored the Data Agent retrieves the missing data from its internal memory and sends it to the Azure IoT Hub As a central and secure message based connectivity service the Azure IoT Hub is responsible for receiving and forwarding the energy data to all participating cloud services within Microsoft Azure Further analysis of the energy data is possible with the help of the Microsoft IoT Suite which administers the devices and collects raw data for processing via the Azure SQL Data Warehouse and PowerBI Azure Stream Analytics and Azure Machine Learning are used to detect anomalies Special algorithms in these services recognize whether the values detected by the sensors over a specific period fall outside the normal range or could possibly not be recorded If such an event occurs the system issues an alarm via e mail In addition the various user groups such as the student residents can access the energy data via a special programming interface to develop their own apps or algorithms as part of a project or to meet college course requirements for example The programming interface which includes a function for retrieving historical energy data is based on the Azure Service Fabric The data is protected via Azure s Active Directory and Application Insights services which authenticate the various user groups As this project demonstrates in impressive detail the Data Agent can be used to easily retrofit older existing control systems with new technologies and connect them to the cloud This is all possible without System Topology

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