Silkeborg Municipality manages a building portfolio of 550,000 m² with over 2,500 remotely monitored utility meters collecting data on electricity, water, and heating consumption. Given this vast amount of data, it was nearly impossible for staff to monitor energy usage effectively using manual methods. 

The transition to a new EMS was a complex and time-consuming process, as data had to be collected from multiple sources, including energy meters and specialized systems containing property data such as ownership details and building areas. Completing this transition typically requires 1–2 years. 

Despite the need for more efficient workflows, the municipality felt constrained by its existing EMS and struggled to implement an alternative solution that could enhance operational efficiency. 

The solution is an innovative new EMS developed by Ento Labs ApS. 

The system automatically retrieves Silkeborg Municipality’s consumption data from Denmark’s national DataHub, as well as essential property portfolio information from the national Building and Housing Register (BBR). By combining this data with publicly available sources such as Google Maps opening hours, the system was fully implemented within just two days—immediately operational and ready to identify energy inefficiencies. 

Powered by AI and machine learning, the system continuously monitors the municipality’s property portfolio and alerts technical staff if electricity, water, or heating consumption exceeds expected levels. 

While alarm-based monitoring is not a new concept, the intelligence of the new EMS lies in its ability to incorporate external data, such as opening hours from Google and weather data from the national meteorological institute. These data points are analyzed alongside the building’s specific usage patterns to detect anomalies. This advanced approach provides the municipality with a significantly improved overview of energy consumption and waste. 

Additionally, the municipality uses the system to verify whether energy optimization projects achieve the expected savings post-implementation. 

This data-driven approach has transformed Silkeborg Municipality’s energy management, freeing up valuable staff time that is now used to actively reduce energy waste within buildings.

The implementation has significantly improved the quality of the municipality’s energy management while optimizing workflows. Silkeborg Municipality now has precise control over energy consumption, leading to reduced waste and lower operational costs. The system’s automated monitoring and real-time alerts enable proactive responses to deviations, contributing to the municipality’s goal of achieving CO₂ neutrality by the end of 2025. 

Within the first two years, Silkeborg Municipality identified annual savings equivalent to the system’s licensing and procurement costs. Additionally, workflow efficiency improved, allowing staff to focus on other essential tasks. 

Through targeted efforts and AI-driven insights from the new EMS, municipal employees have increased energy production from the municipality’s 52 solar panel installations by 25% between 2021 and 2024.