Holstebro Municipality: Achieving Energy Efficiency with AI-based System
Technology Category
- Functional Applications - Computerized Maintenance Management Systems (CMMS)
- Sensors - Utility Meters
Applicable Industries
- Buildings
- Cities & Municipalities
Applicable Functions
- Facility Management
- Maintenance
Use Cases
- Energy Management System
- Time Sensitive Networking
Services
- System Integration
About The Customer
Holstebro Municipality is a Danish municipality with a large building portfolio of 230,000 m2 (2.5 million square feet) of properties. The municipality has a small energy team responsible for technical installations, construction and renovations, energy budgets, exterior maintenance, and inspections to solve problems in the buildings. The team comprises three people responsible for energy, engineering, and installation, and five for construction projects and building maintenance. Energy management is just one of the many tasks the team handles. Given the complexity of managing such a large portfolio, it is essential for the municipality to have the right tools to support the team.
The Challenge
The Danish Municipality of Holstebro was grappling with the challenge of managing energy consumption across its vast building portfolio. The small energy team was struggling to prioritize their efforts due to the sheer size of the portfolio. The existing energy management system (EMS) was outdated and focused more on data collection rather than providing actionable insights. The EMS was not only difficult to maintain, but it also lacked accuracy, leading to low confidence in the data it provided. The team had to spend a significant amount of time maintaining the system, which took away from their other tasks. The municipality needed a modern, efficient solution that could help them manage their energy consumption more effectively and save time for their team.
The Solution
The municipality turned to Ento's AI-based Energy Advisor to address their energy management challenges. The AI-based system analyzes data from the municipality's buildings and external variables such as weather data, calendar, and Covid-lockdown information to identify deviating consumption patterns. This allows the team to prioritize which buildings they should visit to identify and implement energy savings. The system also enables them to report implemented measures and monitor whether they achieve the expected savings over time. The AI-based system not only helps the team manage their energy consumption more effectively but also saves them time by reducing the need for manual data analysis. The system runs independently, allowing the team to spend more time in the buildings and less time analyzing data.
Operational Impact
Quantitative Benefit
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