Energy Optimization through Software
Rising energy costs are a competitive factor for many companies. Whether it’s retail stores, administration buildings or hotels, commercial buildings often consume more energy than they should, driving up unnecessary costs.
- HVAC systems are incorrectly configured
- HVAC systems operate without real-time weather, occupancy, or building lifecycle data
- Technical facilities have limited resources for ongoing support
- Energy consumption is not fully transparent
- Machines are insufficiently maintained or outdated
- Investments in energy efficiency are too low
Reduce your Operating Costs
Energy costs can take a big financial chunk out of building operations. Reducing energy costs is key to keeping building operation costs low. Current measures don’t always add up, either. Some have long payback periods, making them difficult to enforce. And in the case of rental properties, energy efficiency measures aren’t always backed by the owner.
energyControl takes a fresh approach. Intelligent algorithms save your HVAC systems more than 20 percent of energy, with payback times often less than one year. Also, energyControl can be installed without an interruption in service.
For businesses that rent or lease space, energy savings can be realized with the simplicity of software instead of through installing additional building systems.
Energy-saving Measures Compared
Reduce Energy Consumption with Software
energyControl is a software solution for predictive control. Thanks to intelligent algorithms, it achieves significant savings: more than 20 percent less energy consumption of HVAC systems with payback times of less than 12 months.
How Predictive Energy Optimization Works:
Predictive energy optimization is based on ongoing operational data, weather and occupancy forecasts, and profound experience in efficient building operation. Intelligent models learn the building and system behavior from monitoring data. As a result, energy requirements of buildings or individual climate zones are precisely predicted down to the level of individual rooms, and get translated into a predictive control strategy.
1. Learning Models
Models learn the system and building behavior via machine learning methods based on algorithms and processes.
2. Precise Forecasting
energyControl uses system and operating data as well as weather and occupancy forecasts to determine the necessary thermal energy required in the building for the next few hours or longer.
3. Predictive Control
Based on the energy requirement, a predictive control strategy is determined and implemented. This reduces peak loads and optimizes energy consumption.
4. Continual Learning
As the model learns building trends, it uses these learnings to control energy systems even more accurately over time.