CentraLine covers all aspects of energy management, from data acquisition via normalization and archiving to energy reporting and analysis. For monitoring basic consumption data, the CentraLine portfolio contains a vast range of meters for monitoring heating and cooling energy, flow, and electricity. Electrical meters range from 1-phase (32 A) to 3-phase versions (up to 1500 A using converters). Heat meters support automatic switching between heating and cooling energy measurement based on the system's supply and return temperatures. Meters are flexible to cover a variety of BMS connections including pulse counting, MBUS reading, or ModBus reading.
The CentraLine HAWK or EAGLEHAWK controllers can act as metering data collectors for the supervisor system and can archive metering data until it is retrieved by the supervisor, thus ensuring that no data is lost. Additionally, these controllers provide advanced functions for data normalization including:
- Compensation by fixed values such as "square meters of an area"
- Compensation by variable data representing production output such as "number of produced cars",
- Compensation by environmental data, through calculation of, e.g., heating or cooling degree days and their use for normalization.
As a result, CentraLine controllers can provide consumption data which is comparable across buildings and thus allows direct benchmarking. Also, the data can be presented to the user in a way which makes sense to his particular business, e.g. "kWh per m² and produced car" for a car manufacturer or "kWh per hours of instruction and number of pupils" for the principal of a school. Readings of different meters can be easily mathematically combined, to calculate, e.g., the total consumption of electrical energy as a sum of all the floors.
The CentraLine Energy Management dashboard finally presents the data in an easy-to-understand fashion. Different chart types are available:
- Simple line or bar charts aid in understanding the energy consumption of a single datapoint over a defined timeline. A datapoint can be a single meter or multiple meters added up.
- Line or bar charts with multiple y-axes allow comparisons to be made between different datapoints over a defined timeline. An example would be showing energy consumption on one axis and the degree days or outdoor temperature on the other.
- Pie charts help to understand the percentage of contribution of different energy meters to total consumption
- Stacked bars allow the comparison of the contribution of different energy meters to the total consumption over a defined timeline.
- Scatter charts provide the detailed analysis of energy consumption based on a secondary measurement (e.g., consumption based on outdoor temperature). This is a powerful instrument to get insight in optimization potential.
- The ISO 50001 standard requires businesses to manage energy in an intelligent way to achieve improvement. Techniques such as regression analysis, benchmarking against weather compensated projections, comparison against equivalent usage historical data can develop knowledge and insights. Energy Management makes these complex and sophisticated techniques accessible to the general user and displays information in simple and easy to understand terms. Its intuitive presentation of information allows improvements to be communicated effectively across and outside your business.
Energy Management is a professional tool for energy benchmarking and analysis, built on CentraLine Niagara web technology, providing a large range of techniques for managing energy-related data. Energy Management is the ideal system to:
- help manage energy, analyze, and optimize the operation of facilities and
- measure performance across multiple levels within buildings or estates
SMART INTEGRATION AND SUPERVISION
Supervisor for long-term archiving and visualization of consumption data.
The CentraLine portfolio contains a vast range of meters for monitoring heating and cooling energy, flow, and electricity.
Smart data collector supporting different types of meters: Pulse (through Digital I/O module, M-Bus, Modbus, LONWORKS). Supports short-term data archiving and data normalization / calculation of KPIs.
Integrated building controller, with BACnet (IP and MSTP) incl. B-BC certification, LONWORKS (FTT-10A and IP), Modbus, M-BUS, DALI, KNX, EnOcean. Integration of lighting, shading, and other buildings applications.
Scatter charts represent a powerful tool for analyzing energy consumption based on a secondary measurement. Secondary measurements are, e.g., the outdoor air temperature or production data to which the energy consumption is related.
In the scatter chart above, every dot represents the energy consumption of a specific day, superimposed upon the average outdoor air temperature of that day. Analyzing the "point cloud" provides deep insight into the building and tenant characteristics:
- The "knee" of the curve shows the real heating (or cooling) limit of the given building. Heating is required only below the heating limit, cooling is required only above the cooling limit. Between these two limits, only little energy for DHWS purpose is spent in this specific building.
- The slope of the curve is an indicator of the building's transmission losses. The flatter the curve, the smaller the effect of the outdoor air temperature on the energy consumption. Steep curves with high heating limits indicate high transmission losses which could be countered by better insulation.
- Strong deviation of the point cloud from its regression curve indicates a strong variation of energy consumption under identical environmental conditions. The reasons could be manifold and are often caused by tenant behavior. Examples include varying opening hours on different days, windows left open, higher DHWS demand on certain days (e.g., due to showers in sports facilities), etc. Such variations, especially when occurring frequently or even regularly, call for further analysis. In the graphic above, the regular outliers on the bottom are caused by a reduced use of the building during weekends, which is an expected behavior. The regular outliers above the regression curve were caused by an incorrectly set time program causing around-the-clock heating to high temperatures on one day of the week. Once identified, the problem can be easily solved by correcting the heating system's schedule.