Key takeaways:
fEnOMS optimizes industrial heating and cooling processes by analyzing operational data and adapting to changing conditions
The system forecasts demand and schedules energy-intensive operations during low-cost times to reduce electricity expenses
Offered in 3 levels, fEnOMS can serve single sites, multiple locations, or integrate renewable generation data
Germany-based company Flexality’s fEnOMS energy management system was announced as one of the finalists of The smarter E AWARD 2025 under the Smart Integrated Energy category.
fEnOMS is a cloud-based system that optimizes energy consumption in industrial heating and cooling processes. It is based on large amounts of operational data and an adaptive algorithm that improves and recalibrates with each new dataset, according to the company. The main goal of the product is to reduce electricity costs. The company states that it analyzes the plant, its operations, the power contract, and any self-generated power supply by the client before optimizing the processes using fEnOMS. Flexality also works with refrigeration equipment manufacturers to review technical requirements for optimal operation.
By analyzing this data, fEnOMS forecasts electricity demand, which can help identify cost-effective timeslots for controlling heating and cooling processes. All energy-intensive operations are scheduled for times when prices are lower, reducing operational costs. The company claims that the software can integrate into existing control systems, enabling automated energy management.
Depending on the requirement, fEnOMS is available in 3 different levels: Base, Multisite, and Solar & Wind Add-Ons. The Base version is a compact solution that can operate at a single refrigeration plant, integrating multiple smart meters to optimize energy consumption. The software license for fEnOMS Multisite enables the aggregation of data from multiple sites of an enterprise and provides insights for energy managers. The last version is specifically designed for energy producers, integrating weather data and data from metering points and inverters to provide a comprehensive overview of both energy consumption and generation.