Results of the Building TSoS Use Case

Problem Definition:

The human requires specific thermal conditions at the workplaces and living environment. Keeping the temperature, humidity and the air quality within these boundaries requires building technology. The energy supply system is responsible for providing the heat, cold and electrical energy. The air-handling units provide the air change that is responsible for fresh air.

Depending on the deployment of the heating and cooling devices within the rooms, the energy demand of the building appears. That means, a good control algorithm is capable to reduce the building`s energy consumption and to keep the thermal comfort at the same time.

We try to find the optimal control actions that lead the energy consumption of the entire building to a minimum accomplishing the thermal comfort required.

Control strategy description:

The control strategy in Local4Global focused on room control. Conference rooms were considered as test bed for L4G application within the conventional controller. To keep thermal comfort as required, we measure room temperature and CO2-concentration as indication for air quality.

The actuators, that represent our tools for influencing the thermal comfort, consists of a volume flow controller and an after cooler. The volume flow controller facilitates changing the air flow, the after cooler facilitates to modify the air temperature of the air stream into the room. Both together influence the thermal comfort within the room and facilitate to react on changing weather conditions or sudden occupancy.

results

Reference/Base Case control:

The base control strategy applies the same measurements and control the same actuators. The algorithm between bases on a so called PID-controller, which is classical controller, applied a million of times in technical systems. The PID-controller tries to adjust the measured room temperature to the related set point by opening and closing the valve of the after cooler. The volume flow controller is controlled reacts with opening when the level of CO2 is too high and/or there is a request to support the after cooler by keeping the room temperature within the required limits.

The control quality depends mainly on some parameters of the PID-controller. The base control strategy is mostly suitable for keeping thermal comfort, but it does not pay any attention to the energy consumption.

Implementation architecture setup:

The implementation of L4G control and optimization tool (L4GPCAO) was challenging, as the building management system (BMS) is proprietary and dos not foresee any integration of additional programs. Therefore, we developed the so called L4G gateway, which allows the data acquisition and the control access in parallel to the BMS. Furthermore, we developed a software framework, enabling the interaction with the L4G gateway and the processing of L4GPCAO.

The gateway was configured gathering the sensor and actuator data points directly in the communication bus of the BMS. It also logged the values into a data base for later evaluation. The values of the related data points have been linked to L4GPCAO and the rectified control actions have been delivered the same way back.

Performance Criterion:

The performance criterion was the non-renewable energy consumed, that means the fraction of fossil energy taken from the energy and gas grids. The consumption of fossil energy is the main driver of the climate change. The reduction of this indicator is similar to a reduction of climate active emissions and of energy costs as well.

Application Test Scenario:

The application of L4GPCAO was successful realized in two experiments, each of 7 days. One experiment took place at summer period, when mainly cooling is requested. The other took place in late autumn, when mainly heating is requested. The occupancy was similar, as it is the common occupancy of the rooms as a university building during lectures period.

Results Evaluation:

The evaluation leads to the following graphs, which compare the progression of NREC during the related experiment. Index ‘b’ stands for base case, index ‘t’ stands for test case.

It is visible, that L4G is capable to reduce NREC continuously. The periods, where this reverses (when the distance decreases a bit) prove the different control behaviour. It also is visible, that the improvement in summer period is not as high as in autumn period, similarly the absolute values.

The reasons for that are multiple, but mainly it is a higher sensitivity of modern buildings against cooling loads than against heat loads. In summer, the cooling loads of the weather and the internal loads by occupants, devices and light compound the total cooling load, in autumn, they compensate each other. The better the insulation of the building is the greater becomes the effects influence.

At the end, L4GPCAO improved the NREC in the building use case about 27,5 % in average, thus a quarter of fossil energy was consumed during L4GPCAO operations without a reduction of thermal comfort.

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