Explore Asset Life
- Patent pending AI – driven image recognition to identify cars and forecast EV-penetration
- Identification of current and future distributed generation, storage, and heating sources
- Exploit the great potential of smart meter data where there are no online measurements
- Forecast loading and voltage for every asset
- Easy-to-use dashboard with GIS, SCADA, and MDM integration
- Create maintenance plans and long-term investment plans based on risk
- Optimize decisions and reduce cost by 25%
- SAIFI and SAIDI optimizations
- Optimize the regulated economy
- Integration with GIS - and accounting systems
- Open-source software for regulators and data scientists
- Condition-based risk assessment for regulatory reporting
- Generic algorithms for probability of failure, consequences of failure and risk matrices
- Weibull analysis on failure statistics
- Train probability of failure models on your own data
- Mobile application for technicians
- Visual condition-based question connected to on-premises databases
- Register asset replacements, repairs, and inspections directly
- Easy access to instructions and data manuals
- Integrations with management-, and GIS-systems
We work for
At Utiligize, we’ve spent the last couple of years fine-tuning our asset management software to minimise costs for utilities. Losses are a key driver of costs, whether you operate a gas, water, heating or power network.
The Danish Energy Agency today published our report on how distributed energy resources (DERs) like electric vehicles (EVs), heat pumps and solar panels will impact the distribution grid in Denmark.
The green transition will require significant investment in utility infrastructure. Many countries have restrictive income caps or fixed tariffs that do not allow for investment to support the electrification of transport, heating and agricultural processes.
This white paper discusses a data-driven approach to investments, maintenance and containing risk, using forwarding looking asset management for utilities. Download a copy here.
We are extremely proud of Jules Truong, who finished his Master Thesis under Utiligize’s and Professor Pierre Pinson’s supervision. He applied machine learning to forecast production and consumption on Evonet’s (now merged into N1) network – from high to low voltage.