How Kelag Energie & Wärme GmbH implemented an intelligent energy management system using sensors and Power BI

This use case shows how the energy supplier developed a smart metering system that uses LoRaWAN sensors to fully automatically read 2,750 meters throughout Austria on an hourly basis, transforming data into valuable information – for more efficiency, more profit and better environmental protection.

Summary
Kelag Energie & Wärme GmbH (Kelag) is a wholly owned subsidiary of Kelag-Kärntner Elektrizitäts-Aktiengesellschaft, operating 85 district heating networks and more than 900 central heating stations in Austria. In addition to industrial and large customers, Kelag also supplies public institutions and housing companies.

Challenge
Kelag was faced with new challenges following the amendment to the EU Energy Efficiency Directive (EED): To ensure that private customers regularly receive their consumption data, intelligent measurement devices are used to record consumption electronically and from a distance. However, Kelag’s systems for validating and verifying readings were not designed for such large amounts of data. The new legal requirements meant additional costs for developing the required technical infrastructure as well as for providing and visualising the data for customers. Kelag’s employees also had no context for the self-contained values, so they could not extract useful information from the data and were unable to make data-driven decisions.

Solution
Elvaco was the only provider that met the energy supplier’s demanding requirements for framework conditions such as authentication and key management for stable communication. In addition, the metering specialist offered an innovative system that could cope with the very high volume of data. Modules from major manufacturers could be easily integrated into the existing system landscape.

LoRaWAN wireless technology enabled Kelag to transmit readings to an IoT infrastructure at short intervals and in an energy-efficient manner. Kelag combined data transmission with its billing and database infrastructure, and automated the system.

Result
Today, Kelag records daily and hourly readings from LoRaWAN meters. The granularity of the data has improved enormously as a result. The data analysis service Power BI is used to gain insights into the structured meter data, helping Kelag to understand the data, identify new optimisation potentials and thus continuously increase efficiency. Power BI collates all heat meter data and other key figures such as SAP data, and analyses them. The proprietary business intelligence system provides analyses of individual district heating plants. Kelag also analyses the efficiency of its gateways using a LoRa report. The Company has set itself the target of improving the efficiency of its approximately 900 central heating stations by one per cent based on the data visualisation. The estimated savings achieved by data-driven decisions alone amount to EUR 1 million. This process is only possible because Kelag uses the hourly data collection, analysis and graphical processing to transform the data into information.

Products used
CMi4160 LoRaWAN modules were mounted inside Diehl Metering’s SHARKY 775 ultrasonic compact energy meters to deliver meter data to a receiving server via a LoRaWAN network. The modules have 11 years of battery life and a long communication range.  The CMi4160 can be retrofitted inside an already deployed meter. It can be configured via NFC using Elvaco’s OTC mobile app. Kelag also uses the CMi4110 for Landis + Gyr and CMi4140 for Kamstrup MULTICAL LoRaWAN modules, the CMi4170 integrated MCM for Engelmann Sensostar heating meters and the CMa10L temperature/humidity module.

Hannes Gütler, Head of Data and Energy Management at Kelag Energie & Wärme GmbH:
“The high quality and operational reliability of Elvaco’s products impressed us. We now operate more efficiently and are reaping the benefits: more profit, a better CO2 footprint and higher customer retention. We expect the investments in hardware, software and human resources to pay for themselves within about a year.”