The Functional Mockup Interface (FMI) is a tool independent standard for the exchange of dynamic models and for Co-Simulation. The first version, FMI 1.0, was published in 2010. Already more than 30 tools support FMI 1.0. In this paper an overview about the upcoming version 2.0 of FMI is given that combines the formerly separated interfaces for Model Exchange and Co-Simulation in one standard. Based on the experience on using FMI 1.0, many small details have been improved and new features introduced to ease the use and increase the performance especially for larger models. Additionally, a free FMI compliance checker is available and FMI models from different tools are made available on the web to simplify testing.

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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.

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Performance measurement (PM) has become increasingly popular in the management of public sector organizations (PSOs). This is somewhat paradoxical considering that PM has been criticized for having dysfunctional consequences. Although there are reasons to believe that PM may have dysfunctional consequences, when they occur has not been clarified. The aim of this research is to conceptualize the dysfunctional consequences of PM in PSOs. Based on complementarity theory and contingency theory we conclude that dysfunctional consequences of PM are a matter of interactions between PM design and PM use, between control practices in the control system and between PM and context.

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Abstract:We present the open-source software framework in JModelica.org for numerically solving large-scale dynamic optimization problems. The framework solves problems whose dynamic systems are described in Modelica, an open modeling language supported by several different tools. The framework implements a numerical method based on direct local collocation, of which the details are presented. The implementation uses the open-source third-party software package CasADi to construct the nonlinear program in order to efficiently obtain derivative information using algorithmic differentiation. The framework is interfaced with the numerical optimizers IPOPT and WORHP for finding local optima of the optimization problem after discretization. We provide an illustrative example based on the Van der Pol oscillator of how the framework is used. We also present results for an industrially relevant problem regarding optimal control of a distillation column.