Roel De Coninck

Bio: Roel De Coninck is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Modelica & Model predictive control. The author has an hindex of 10, co-authored 19 publications receiving 568 citations.

Toolbox for development and validation of grey-box building models for forecasting and control

Abstract: As automatic sensing and information and communication technology get cheaper, building monitoring data becomes easier to obtain. The availability of data leads to new opportunities in the context of energy efficiency in buildings. This paper describes the development and validation of a data-driven grey-box modelling toolbox for buildings. The Python toolbox is based on a Modelica library with thermal building and Heating, Ventilation and Air-Conditioning models and the optimization framework in JModelica.org. The toolchain facilitates and automates the different steps in the system identification procedure, like data handling, model selection, parameter estimation and validation. To validate the methodology, different grey-box models are identified for a single-family dwelling with detailed monitoring data from two experiments. Validated models for forecasting and control can be identified. However, in one experiment the model performance is reduced, likely due to a poor information content in the identification data set.

Openideas - an open framework for integrated district energy simulations

Abstract: Contemporary research focuses on net-zero energy buildings and their integration in larger energy systems. By consequence, a vast set of research questions become increasingly multi-domain and multiscale. With this increasing complexity, the need for more elaborate building energy simulation tools rises. The presented paper reviews the development of the OpenIDEAS framework, an open framework developed for integrated district energy simulations consisting of IDEAS,StROBe,FastBuildings and GreyBox to answer the new research questions rising in the multidisciplinary building energy domain. The Modelica IDEAS Library allows simultaneous transient simulation of thermal, control and electric systems at (building and at) feeder level. The Python StROBe Module provides boundary conditions for IDEAS depicting the stochastic modelling of residential receptacle loads, internal heat gains, space heating set point temperatures and hot water redraws. The Modelica FastBuildings Library implements low-order building models that are compatible with IDEAS. The PythonGreyBox Module implements a semi-automated parameter estimation tool to obtain grey-box models that may serve as controller model in a model predictive controller framework for IDEAS.

CasADi: a software framework for nonlinear optimization and optimal control

Abstract: We present CasADi, an open-source software framework for numerical optimization. CasADi is a general-purpose tool that can be used to model and solve optimization problems with a large degree of flexibility, larger than what is associated with popular algebraic modeling languages such as AMPL, GAMS, JuMP or Pyomo. Of special interest are problems constrained by differential equations, i.e. optimal control problems. CasADi is written in self-contained C++, but is most conveniently used via full-featured interfaces to Python, MATLAB or Octave. Since its inception in late 2009, it has been used successfully for academic teaching as well as in applications from multiple fields, including process control, robotics and aerospace. This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last 7 years.

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Abstract: In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.