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Demand Side Management for Thermally Activated Building Systems based on Multiple Linear Regression
(2015)
There is a growing trend for the use of thermo-active building systems (TABS) for the heating and cooling of buildings, because these systems are known to be very economical and efficient. However, their control is complicated due to the large thermal inertia, and their parameterization is time-consuming. With conventional TABS-control strategies, the required thermal comfort in buildings can often not be maintained, particularly if the internal heat sources are suddenly changed. This paper shows measurement results and evaluations of the operation of a novel adaptive and predictive calculation method, based on a multiple linear regression (AMLR) for the control of TABS. The measurement results are compared with the standard TABS strategy. The results show that the electrical pump energy could be reduced by more than 86%. Including the weather adjustment, it could be demonstrated that thermal energy savings of over 41% could be reached. In addition, the thermal comfort could be improved due to the possibility to specify mean room set-point temperatures. With the AMLR, comfort category I of the comfort norms ISO 7730 and DIN EN 15251 are observed in about 95% of occasions. With the standard TABS strategy, only about 24% are within category I.
Adaptive predictive control of thermo-active building systems (TABS) based on a multiple regression algorithm: First practical test. Available from: https://www.researchgate.net/publication/305903009_Adaptive_predictive_control_of_thermo-active_building_systems_TABS_based_on_a_multiple_regression_algorithm_First_practical_test [accessed Jul 7, 2017].
Photovoltaics Energy Prediction Under Complex Conditions for a Predictive Energy Management System
(2015)
The building sector is one of the main consumers of energy. Therefore, heating and cooling concepts for renewable energy sources become increasingly important. For this purpose, low-temperature systems such as thermo-active building systems (TABS) are particularly suitable. This paper presents results of the use of a novel adaptive and predictive computation method, based on multiple linear regression (AMLR) for the control of TABS in a passive seminar building. Detailed comparisons are shown between the standard TABS and AMLR strategies over a period of nine months each. In addition to the reduction of thermal energy use by approx. 26% and a significant reduction of the TABS pump operation time, this paper focuses on investment savings in a passive seminar building through the use of the AMLR strategy. This includes the reduction of peak power of the chilled beams (auxiliary system) as well as a simplification of the TABS hydronic circuit and the saving of an external temperature sensor. The AMLR proves its practicality by learning from the historical building operation, by dealing with forecasting errors and it is easy to integrate into a building automation system.
Für die solarunterstützte CO2-neutrale Nahwärmeversorgung des Neubaugebiets Hülben in Holzgerlingen wurden Holzpelletskessel mit einer 249m²-großen Solaranlage kombiniert. Im ersten Intensivmessjahr vom 01.03.2007 bis 29.02.2008 wurde eine Gesamt-Wärmeabgabe ins Nahwärmenetz von 920.606 kWh gemessen, wobei der solare Anteil bei 84.033 kWh lag. Es wurden ein Systemnutzungsgrad von 23,8 % und ein solarer Deckungsanteil von 9,5% gemessen. Die vom Lieferanten abgegebene Energiegarantie wurde in der ersten Intensivmessphase nicht erreicht.
In this study, a high-performance controller is proposed for single-phase grid-tied energy storage systems (ESSs). To control power factor and current harmonics and manage time-shifting of energy, the ESS is required to have low steady-state error and fast transient response. It is well known that fast controllers often lack the required steady-state accuracy and trade-off is inevitable. A hybrid control system is therefore presented that combines a simple yet fast proportional derivative controller with a repetitive controller which is a type of learning controller with small steady-state error, suitable for applications with periodic grid current harmonic waveforms. This results in an improved system with distortion-free, high power factor grid current. The proposed controller model is developed and design parameters are presented. The stability analysis for the proposed system is provided and the theoretical analysis is verified through stability, transient and steady-state simulations.
The uncertain and time-variant nature of renewable energy results in the need to deal with peaks in the production of energy. One approach is to achieve a load shift and thereby help balancing the grid by using thermally Activated Building Systems (TABS). Control systems currently in place do not exploit the full potential of TABS. This paper reviews how Model Predictive Control can possibly reduce the fluctuations of the demand and supply of (renewable) energy as it enables the TABS to react to the dynamics of weather and its impact on the grid at any time.
Die Hochschule Offenburg begleitet seit Juli 2006 in Zusammenarbeit mit dem Fraunhofer ISE in Freiburg und der HfT Stuttgart die solar unterstützte Klimatisierung der Festo AG & Co. KG in Esslingen. Die Anlage wurde im Rahmen des Forschungsvorhabens „Solarthermie2000plus“ vom Bundesumweltministerium gefördert. Dabei wurde die bereits bestehende Adsorptionskälteanlage, die bisher mit Kompressorabwärme und Gaskesseln betrieben wurde, durch eine Solaranlage als drittem Wärmelieferanten ergänzt.
Kühlen im großen Stil
(2010)