CONTROLLING TECHNOLOGY &
SELF-LEARNING ALGORITHM
charge/discharge process by connecting to the heating or cooling circuit: monitors the availability of cheaper energy and the changes in requested and outside temperatures
HIGHLIGHTS
control circuit & process
controller & battery symbiosis
system’s heat characteristic
Intelligent Controller optimizes the integration of the new circulation branch supplemented with the Thermal Battery into the existing or new thermal engineering system, thus improving the thermal operation of the entire system. The savings come from the energy storage options (Thermal Battery) and the timing management (Intelligent Controller). Additional control of the thermal engineering system is provided by the controller & data capturing hardware, and the related local (onboard) and centralised (cloud-based, tenant partitioned) software.
The basis of the Intelligent Controller and controlling process is the continuous monitoring and measurement of indicators, the creation and evaluation of data series. The control process itself is conducted by switching on and off the existing circulation circuit. It does not directly change the settings of the cooling/heating unit, although its parameters (flow water temperature, etc) will be reviewed depending on the new system design! The behaviour of the Intelligent Thermal Battery as a consumer or energy source changes the load data of the cooling/heating circuit in a way that is perceptible to the cooling/heating machine, to which the heat generator reacts, according to its own settings.
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Like the numerous glands regulating the function of the human body (Intelligent Thermal Battery, Chiller), which mix their contents into the bloodstream through their sensors examining the body (cold, hot thermal energy), thus changing its composition (circulating cooling/heating fluid), as a communication channel (liquid), they notify the other organs (cooler / heating units), which then modify their function (adjust) based on signals received through the blood.
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Intelligence is the ability to achieve complex things. Such complex control systems, with high redundancy, individual parameterization, and autonomous operation, nevertheless strive for a common goal (energy saving), within their own operational limits, finely tuned to the equilibrium state of the system.
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In addition to the current data, based on the creation of the thermal characteristics of the building and the knowledge of daily environmental curves (stored data), the Intelligent Controller begins to "adjust" this inertia, dead-time control system at an optimal time.
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Based on the Thermal Battery performance data, the Intelligent Controller calculates the timing of the intervention and the "dose" to achieve the desired condition. The two components (controlling & storage) have been developed together with the determination of the required properties and performance indicators to achieve the best control capability.
The challenge we face is that today, we cool when we are warm, and we heat when we are cold. In other words, immediately! However, the optimal efficiency and price of thermal energy production do not correspond with this on-demand approach. Moreover, this trend is only intensifying in the world of the increasing share of „hectic” green energy resources.
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The Self-Learning Algorithm identifies the user's cooling/heating characteristics by learning their energy characteristics from dynamic data through cooling/heating profiles. Based on a large number of data points and on a prepared model which is dependent on the change of input factors (e.g. external- internal temperature data), the controller calculates and performs control tasks, such as connecting the new bypass pipeline to the heating/cooling system, as a consuming or producing element.
The Intelligent Controller seals individually and centrally stores data and provides access to data flow to Building Management Systems. Through web access, it calculates the „past to future” energy curves, along with the typical status of the supplemented control circuit (charge-storage-discharge). In the absence of the installation of separate heat consumption meters, the „past” curve can be calculated from periodic data using an algorithm audited by thermal engineering experts by interpolation.
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Central logic and data storage make it possible to analyze temperature data specific to regions and their effects on specific industries (similar users) and to optimally adjust individual systems. By preparing an additional ‘ideal’ curve, the reserves of the setting can be detected and the required storage capacity calculated. As the storage system is modular, further development of the optimal storage size, if necessary, depends on the users ongoing and future requirements.
The "2 curves" then make it easy to follow the operation of the control, the method, time and cyclicality of savings, as well as the amount of energy saved, its price and CO2 savings. In addition, the calculated data (3rd curve) provides information on available savings for setting the optimal size of the Thermal Battery Pack and for further expansion of the system.
This is the basis for the user's energy audit, CO2 quotas and certification of environmentally conscious behaviour.