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Toolbox for Development and Validation of Grey-Box Building Models for Forecasting and Control

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Toolbox for Development and Validation of Grey-Box Building Models for Forecasting and Control Toolbox for development and validation of grey-box building models for forecasting and control De Coninck, Roel; Magnusson, Fredrik; Åkesson, Johan; Helsen, Lieve Published in: Journal of Building Performance Simulation, Taylor & Francis DOI: 10.1080/19401493.2015.1046933 2016 Document Version: Peer reviewed version (aka post-print) Link to publication Citation for published version (APA): De Coninck, R., Magnusson, F., Åkesson, J., & Helsen, L. (2016). Toolbox for development and validation of grey-box building models for forecasting and control. Journal of Building Performance Simulation, Taylor & Francis, 9(3), 288-303. https://doi.org/10.1080/19401493.2015.1046933 Total number of authors: 4 General rights Unless other specific re-use rights are stated the following general rights apply: Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal Read more about Creative commons licenses: https://creativecommons.org/licenses/ Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. LUND UNIVERSITY PO Box 117 221 00 Lund +46 46-222 00 00 Toolbox for development and validation of grey-box building models for forecasting and control Roel De Conincka;b;c, Fredrik Magnussond, Johan Åkessone, Lieve Helsenb;c a3E nv, 1000 Brussels, Belgium, bKU Leuven, Department of Mechanical Engineering, 3001 Heverlee, Belgium, cEnergyVille, 3600 Waterschei, Belgium, dDepartment of Automatic Control, Lund University, SE-221 00 Lund, Sweden, eModelon AB, Ideon Science Park, SE-223 70 Lund, Sweden Abstract detection, energy efficiency analysis and model-based building operation. A first step in many of these ap- As automatic sensing and Information and Communi- plications is the creation of a building energy system cation Technology (ICT) get cheaper, building moni- model. toring data becomes easier to obtain. The availability Models can be classified according to the white- of data leads to new opportunities in the context of en- box, grey-box and black-box paradigm. (Bohlin, ergy efficiency in buildings. 1995; Madsen and Holst, 1995; Kristensen et al., 2004; This paper describes the development and valida- Henze and Neumann, 2011). Although the bound- tion of a data-driven grey-box modelling toolbox for aries between these categories are blurry and often buildings. The Python toolbox is based on a Modelica overlapping, this paradigm is useful for understanding library with thermal building and Heating, Ventilation the modelling procedure. White-box modelling bases and Air-Conditioning (HVAC) models and the optimi- the model solely on prior physical knowledge of the sation framework in JModelica.org. The toolchain fa- building. Most building simulation software falls un- cilitates and automates the different steps in the system der this category, like TRNSYS, EnergyPlus and many identification procedure, like data handling, model se- others (Crawley et al., 2008). Black-box modelling lection, parameter estimation and validation. bases the model solely on response data (monitoring To validate the methodology, different grey-box of the building) and a universal model set, including models are identified for a single-family dwelling with e.g. AR and ARMAX. Although physical insight is detailed monitoring data from two experiments. Vali- not required for making a black-box model, a model dated models for forecasting and control can be iden- structure has to be chosen and this often involves mak- tified. However, in one experiment the model perfor- ing assumptions about the system, for example with mance is reduced, likely due to a poor information regard to linearity. Grey-box identification methods content in the identification dataset. and tools cater for the situation where prior knowledge Keywords: grey-box models, parameter estimation, of the object is not comprehensive enough for satisfac- collocation method, validation, Modelica tory white-box modelling and, in addition, purely em- pirical black-box methods do not suffice because the Introduction involved physical processes are too complex. Grey- and black-box models are also called inverse models. The continuous progress in ICT has led to the avail- The difference between white- and grey-box mod- ability of small and low-cost sensors, low-power wire- elling is not in the complexity of the model. A single- less data transfer protocols, cheap and accessible data state model can be a white-box model if all param- storage and powerful servers. Applied to the building eters can be fixed based on physical knowledge only. sector, these technologies can be used to collect large However, when one or more parameters in a white-box amounts of building monitoring data at relatively low model are estimated based on a fitting of the model to costs. The abundance of data gives rise to new oppor- measurement data, the model becomes grey, no mat- tunities and applications in existing buildings like fault ter its complexity. Therefore, the distinction between white and grey cannot be made by only looking at the nich, Germany. The validation is carried out for two model structure: one has to know how the model pa- different experiments on the same building. rameters have been identified. All three model types can be either deterministic Part I or stochastic. A deterministic model cannot explain the differences between the model output and the true Methodology variations of the states (observations). Madsen and Holst(1995) therefore introduced a Wiener process in Overview the system equations to cope with the simplifications of the model and uncertainties in inputs and monitor- A high-level overview of the toolbox is shown in Fig- ing. The obtained model is a stochastic state-space ure1. The toolbox is composed of four major compo- model. nents: For existing buildings with available monitoring data, the grey-box approach is considered to combine 1. the Modelica library FastBuildings with thermal the best of two worlds: physical insight and model zone models, HVAC components and building structure from the white-box paradigm and parameter models; estimation and statistical framework from the black- box paradigm. This paper describes an approach to 2. different .mop files specifying the model compo- grey-box modelling for buildings and the development nents and which parameters to estimate; of a toolbox combining Modelica and Python. The re- 3. JModelica.org as a middle layer for compilation sulting framework will be referred to as the toolbox in of the .mop files as well as formulation and solu- the remainder of this paper and will be validated on tion of the optimisation problem; a single-family dwelling. The toolbox is not publicly available, but can be obtained with an open-source li- 4. Python module greybox.py delivering the user in- cense for research purposes by contacting the authors terface and top-level functionality. of this paper. The toolbox has been developed with two purposes in mind. A first application is model predictive control (MPC). In this context, the grey-box model serves as the control model in a feed-back loop with the build- ing. According to Henze(2013), the process of model identification accounts for 70 % of the effort for imple- menting an MPC controller. Automating this process can therefore reduce the total cost of MPC in build- ings. To validate such a control model, the k-step pre- diction performance is used. A second application is load forecasting for real buildings. The forecast hori- zon is typically one day or one week. In this case, a different metric to validate the model is required: the simulation performance. This is the model deviation from a measured output in an open-loop simulation Figure 1: Overview of the grey-box buildings toolbox. when measured disturbances are applied. It is clear that a model showing a good simulation Modelica library FastBuildings performance will also have a good k-step prediction performance. The opposite is not true. Therefore, we Modelica is an equation-based modelling language for will use the simulation performance as quality crite- cyber-physical systems (Elmqvist, 1997). The object- rion for the model validation. oriented philosophy stimulates model reuse, resulting This paper is split in two parts. The first describes in many available libraries, often open-source and free. the methodology and development of the toolbox. The Modelica is gaining importance in the building simula- second describes the validation results for a well mon- tion community (Wetter, 2011; Wetter and Van Treeck, itored experimental single-family dwelling near Mu- 2013). The choice for Modelica for the construction of the models is based on three major arguments (Wet- Currently, the thermal zone models available in ter, 2009). Firstly, Modelica allows for linear, non- the FastBuildings library are based on a resistor- linear and hybrid model formulations and therefore capacitance (RC) network analogy which is often used it does not limit the model structure as such. Sec- for the modelling of thermal processes. This is how- ondly, Modelica is equation-based, thus allowing effi- ever not required: any model that specifies a relation- cient Newton-type solvers to be used as an alternative ship between the heat flows and temperatures at the to for example genetic algorithms. Thirdly, Modelica interface of a thermal zone can be implemented. Dif- has a connector concept to support component-based ferent examples of models in the library are schemati- modelling.
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