This section provides downloads and links to articles, papers, reports and diagrams, plus relevant and related guides.  

The project deliverables will also be accessible here, and shall be added to whilst the project progresses.


File size: 26mb

PhD Thesis April 2018
Author: Filip Jorissen
KU Leuven, ARENBERG DOCTORAL SCHOOL Faculty of Engineering Science
Supervisors: Lieve Helsen, Wim Boydens
A large potential exists to improve the current practice of HVAC design and operation of buildings with respect to occupant comfort, energy use or energy cost, and investment costs for design and construction. More specifically, design and control processes can be improved through the use of contemporary optimisation algorithms such as Model Predictive Control (MPC). Numerous papers have demonstrated the value of MPC using both simulation and demonstration projects. Practical implementation of MPC in industry is however hampered by the expert knowledge and time investment that is required for developing MPC controller models and algorithms. An MPC development methodology that is both practical and scalable to large problem sizes has not been demonstrated to date. Moreover, design studies typically do not take into account the interaction between control and design.

This PhD thesis therefore proposes a user-friendly, object-oriented method-ology for integrated optimal control and design of buildings. For this methodology, modelling experts develop generic, detailed, but easy to use component models for optimal control problems using the object-oriented modelling language Modelica.
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File size: 19mb

PhD Thesis September 2017
Author: Damien Picard
KU Leuven, ARENBERG DOCTORAL SCHOOL Faculty of Engineering Science
Supervisor: Lieve Helsen
Since May 2010, the directive 2010/31/EU of the European Parliament compelsits Member States to drastically decrease the energy use of buildings, to increasetheir energy efficiency and to increase the relative amount of renewable energythey use. One of the technologies recommended by the directive is the heatpump which efficiently uses electricity to extract thermal energy from a heator cold source. In this work, buildings equipped with the particularly efficienthybrid GEOTABSsystem are considered, consisting of a ground source heatpump (GSHP) coupled to a thermally activated building structure (TABS)system and optionally extended with a gas boiler, radiators or other auxiliarysystems. The main objective of this research is toimprove the thermal comfortand the energy efficiency of large hybrid GEOTABS buildings by applying modelpredictive control (MPC) and to improve their economic viability by optimizingthe size of the GSHP and of the auxiliary systems as well as the type of theauxiliary systems to install.
To this end, Building Energy Simulation (BES) models of an existing office building, a school, a retirement home and a block of flats were created usingand extending the open-source Modelica libraryIDEASto represent a wide setof hybrid GEOTABS buildings. The models include the building envelope, theheating, ventilation and air conditioning (HVAC) system, the occupancy and adefault rule-based building climate controller (RBC). Additionally, a method tolinearise the building envelope of the developed Modelica models was developedin order to obtain highly accurate controller models for MPC. The methodautomatically precomputes the non-linear equations which do not depend on themodel states and linearises the other equations. The obtained controller modelsare then used by a toolchain which semi-automatically generates a linear MPCand tests its control performance on a full year simulation of the developed BESmodels. Finally, a python tool was created to optimize the economic viabilityand CO2emissions of hybrid GEOTABS systems.
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