aedifion.analytics¶

Offering deep insights and auto-generated recommendations to increase performance and operational quality of energy systems and their plants.

Overview¶

aedifion.analytics is built for technicians and engineers who want to improve the performance of buildings, HVAC components, energy-related plants, or energy networks such as district heating and cooling grids in terms of operation costs, indoor comfort, energy efficiency, CO2 emission, supply stability, and maintenance.

aedifion.analytics is a framework for automated and scalable analysis of large sets of time series data from such systems in order to derive recommendations on how to improve their operation. For this purpose, aedifion.analytics aims at profound transparency and interpretation of system operation at a deep, data-driven level.

Its workflow is straightforward as summarized in figure 1:

Figure 1: Schematic overview of aedifion.analytics

1. Collect all data and build your data lake with data from automation and monitoring networks of the building or energy system and combine it with internet data via aedifion.io.
2. Structure the data lake via digital twins of the physical components.
3. Analyze all digital twins via the aedifion.analytics algorithms.
4. Recommend and implement optimization measures received for aedifion.analytics.

For deeper technical insights check out the chapter framework.

Use cases¶

The use of aedifion.analytics is beneficial in several scenarios and business cases. Just to mention a few:

Asset and facility management: aedifion.analytics enables low-cost optimization of user comfort and energy efficiency, without causing capital exenditure - benefiting leasing rates, operational costs, and contractual obligations.

Optimization projects: aedifion.analytics provides deep system transparency and recommendations to optimize operations in the dimensions of energy service delivery, indoor comfort, energy efficiency, and maintenance expenses. Therefore aedifion.analytics can be used by technicians or engineers to support their optimization projects.

Original equipment manufacturers (OEMs): aedifion.analytics provides scalable analysis which can be offered as additional data services to end customers of OEMs. Furthermore, aedifion.analytics supports R&D departments of OEMs with deep insights into actual operational behavior and usage of their equipment in the field - of course without revealing the individuals behind the data.

Enhancement of existing software: Existing data applications and cloud services can be extended with aedifion.analytics functionalities. Integration of the API endpoints is all it requires.

Operation and energy monitoring: aedifion.analytics provides durable energy and maintenance efficiency throughout the whole building/plant life time, identifies aging phenomenons of components and recommends fixes. Therefore, aedifion.analytics significantly lowers operating costs. Furthermore, aedifion.analytics enables energy monitoring.

Commissioning project: aedifion.analytics supports commissioning via field layer and component functional tests.

aedifion continuously extends its scope. Do you have further ideas or questions if your use case can be supported by aedifion.analytics? Contact us!

Example¶

School A has extraordinary high primary energy consumption for heating. A technician is asked to optimize this system. After the technician plug and play installed aedifion.io at School A, the analysis of the building can start: One condensing boiler, and three heat distribution circuits shall be analysed.

The technician adds one boiler, and three heating loop component data models to the School A project and maps the datapoints to the pins of the instanced components - supported by the provided meta data on data points. Since the technician suspects something might be wrong with the temperature levels, the set-point compliance analysis function is run on the heat distribution circuits and on the boiler.

The analysis results confirm the assumption: All three circuits exceed their temperature levels while the boiler meets its set-point temperature quiet fine. The reason for that is identified by the decision engine of aedifion.analytics: The heating curve of the boiler is not designed according to demand. Therefore, the analytics results recommend an adjustment of the heat curve, which the technician does right away.

This means that not only can the boiler be operated with a significantly lower load, but also overheating of the classrooms can also be avoided. The school principal is glad about the saved energy costs. And the pupils and teachers are happy about the fact that they don't have to constantly open the window in winter, because it is too warm in the room.