Building and district energy systems become increasingly complex, requiring accurate simulation and optimisation of systems that combine building envelope, heating ventilation and air conditioning, electrical distribution grids and advanced controllers. Hence, it becomes more challenging for existing simulation tools to provide integrated solutions for these multi-physics problems. Moreover, common building simulation frameworks tightly integrate model equations and their solvers in the program code, which affects model transparency and hampers tool extensions. This is contrasted by equation-based tools such as Modelica, for which different solvers can be used. In this context, the Integrated District Energy Assessment by Simulation (IDEAS) library is developed. After a recent development shift towards more detailed, multi-zone models, this paper presents a comprehensive, well-documented, overview of the buildings part of IDEAS. This includes new computational aspects of the library, improved usability aspects, an updated intercomparison with BESTEST and a verification based on IEA EBC Annex 58.

This paper presents an approach for speeding up Modelica models. Insight is provided into how Modelica models are solved and what determines the tool's computational speed. Aspects such as algebraic loops, code efficiency and integrator choice are discussed. This is illustrated using simple building simulation examples and Dymola. The generality of the work is in some cases verified using OpenModelica. Using this approach, a medium sized office building including building envelope, heating ventilation and air conditioning (HVAC) systems and control strategy can be simulated at a speed five hundred times faster than real time.

Optimal climate control for building systems is facilitated by linear, low-order models of the building structure and of its Heating, Ventilation and Air Conditioning (HVAC) systems. However, obtaining these models in a practical form is often difficult, which greatly hampers the commercial implementation of model predictive controllers. This work describes a methodology for obtaining a linear State Space Model (SSM) of Building Energy Simulation (BES) models, consisting of walls, windows, floors and the zone air. The methodology uses the Modelica library IDEAS to develop a BES model, including its non-linearities, and automates its linearisation. The Dymola function linearize2 is used to generate the state space formulation, facilitating further mathematical manipulations, or simulation in different environments. Optionally this model can then be reduced for control purposes using model order reduction (MOR) techniques. The methodology is illustrated for the zone air temperature in an office building. For this case, the absolute error between the non-linear BES and its SSM remains under 1 K and its yearly average is 0.21 K. The original 50 states SSM could furthermore be reduced to 16 states without significant loss of accuracy.

This paper presents TACO (Toolchain for Automated Control and Optimization), which is a Modelica-based automated toolchain for model predictive control (MPC) of building systems. Its goal is to significantly reduce the engineering expertise and the time investment required for applying MPC to buildings. TACO is based on JModelica. Modifications compared to JModelica are discussed and the implementation of our custom MPC problem formulation is presented. The implementation is verified using two example models and is benchmarked with respect to accuracy and computation time. These results show that the computation time can be reduced significantly using the toolchain options, while only slightly reducing the controller optimality.

Advanced control strategies are becoming increasingly necessary in buildings in order to meet and balance requirements for energy efficiency, demand flexibility, and occupant comfort. Additional development and widespread adoption of emerging control strategies, however, ultimately require low implementation costs to reduce payback period and verified performance to gain control vendor, building owner, and operator trust. This is difficult in an already first-cost driven and risk-averse industry. Recent innovations in building simulation can significantly aid in meeting these requirements and spurring innovation at early stages of development by evaluating performance, comparing state-of-the-art to new strategies, providing installation experience, and testing controller implementations. This paper presents the development of a simulation framework consisting of test cases and software platform for the testing of advanced control strategies (BOPTEST -Building Optimization Performance Test). The objectives and requirements of the framework, components of a test case, and proposed software platform architecture are described, and the framework is demonstrated with a prototype implementation and example test case.

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This paper describes the collaborative development of the Annex 60 Modelica library, a free, open-source library for building and community energy systems. The library is developed within the Annex 60 project that is conducted under the umbrella of the International Energy Agency's Energy in Buildings and Communities Programme (IEA EBC). Our goal is to develop and distribute a well documented, vetted and validated open-source library that serves as the core of future building simulation programs and that can be integrated with existing programs as well. The work brings together experts in Modelica for building energy applications and coordinates the previously fragmented development that led to four libraries that were incompatible, hard to combine and each itself limited in scope. The work resulted in a library that is now used as the core of these four Modelica libraries. The paper describes the agreed upon requirements, scope, current status of implementation, quality control process and structure of the library. The paper also provides illustrative examples.

Building systems and their heating, ventilation and air conditioning flow networks, are becoming increasingly complex. Some building energy simulation tools simulate these flow networks using pressure drop equations. These flow network models typically generate coupled algebraic nonlinear systems of equations, which become increasingly more difficult to solve as their sizes increase. This leads to longer computation times and can cause the solver to fail. These problems also arise when using the equation-based modelling language Modelica and Annex 60 based libraries. This may limit the applicability of the library to relatively small problems unless problems are restructured. This paper discusses two algebraic loop types and presents an approach that decouples algebraic loops into smaller parts, or removes them completely. The approach is applied to a case study model where an algebraic loop of 86 iteration variables is decoupled into smaller parts with a maximum of 5 iteration variables.