Workshops offered on June 5
| Time | Room 1 | Room 2 |
|---|---|---|
| 8:30 AM - 9:00 AM | Light breakfast and workshop registration | |
| 9:00 AM - 10:30 AM | Dr. Zhang - Modelica | Hussein - EnergyPlus Python API |
| 10:30 AM - 10:50 AM | Coffee break | |
| 10:50 AM - 12:20 PM | Dr. Zhang - Modelica (contd.) | Hussein - EnergyPlus Python API (contd.) |
| 12:20 PM - 1:30 PM | Lunch | |
| 1:30 PM - 3:00 PM | Dr. Zhang - Modelica (contd.) | Dr. Boafo - BESOS: a Collaborative Building and Energy Systems Simulation Platform |
| 3:00 PM - 3:20 PM | Coffee break | |
| 3:20 PM - 4:50 PM | Dr. Zhang - Modelica (contd.) | Dr. Boafo - BESOS: a Collaborative Building and Energy Systems Simulation Platform (contd.) |
Modeling and simulation of building energy systems using Modelica
Workshop Instructor: Kun Zhang
Kun Zhang is an Associate Professor in Mechanical Engineering at École de technologie supérieure in Montréal. His research focus on the development of next-generation modelling languages and tools, advanced controls, data analytics, and machine learning and investigates the best approach to integrate them into buildings and cities to improve energy efficiency and flexibility. He has developed models included in the Modelica Buildings library, as well as developed Modelica-based models for Model Predictive Control (MPC) and implemented MPC controllers for real buildings. He has contributed to the development of Spawn of EnergyPlus and the Control Description Language.
Workshop description
Modelica is an equation-based, acausal and object-oriented modelling language designed for the simulation of complex physical systems across various domains, including mechanical, electrical, thermal, and control systems. It is a preferred choice for system-level simulation and design optimization in diverse industries such as automotive, aerospace, energy, and robotics. It has witnessed a growing interest in the buildings sector, and is currently being employed in the development of next generation energy simulation tool Spawn of EnergyPlus supported by the U.S. Department of Energy (DOE). The latest ASHRAE Standard 231P A Control Description Language for Building Environmental Control Sequences is also based on the Modelica language.
In this course, we will first give an introduction to the Modelica language and its basic syntax, and then present briefly the open-source model library Modelica Buildings. Afterwards, we will discuss best practices in setting up thermo-fluid flow models and how to avoid potential problems. In hands-on exercises, participants will practice constructing models of simple heating and air conditioning systems, linking them to a thermal load, and adding feedback control. The models will be built by leveraging components from the Modelica Buildings Library.
Workshop requirements
No previous Modelica experience is needed for this course.
Introduction to the EnergyPlus Python API
Workshop Instructor: Hussein Elehwany
Hussein Elehwany is a third-year PhD student in Building engineering at Carleton University, supervised by Dr Gunay and Dr Ouf. His research revolves around utilizing reinforcement learning for occupant-centric controls. As part of the Building Performance Research Centre (BPRC), I have helped in deploying different projects using the EnergyPlus Python API, such as reinforcement-learning-based prediction of thermal preferences, deep-learning-based supply air temperature reset strategy, and direct load control sequences using genetic algorithms.
Workshop description
This half-day workshop will introduce attendees to the two methods of using the EnergyPlus Python API: the EnergyPlus library and the Python Plugin. Participants will gain a basic understanding of the prerequisites for using the EnergyPlus library and will be introduced to the functionalities of the runtime API and the data-transfer API. The workshop will explain the role of variable handles and demonstrate how to read variables and set actuators. Attendees will be guided through the process of developing a control sequence using the API. Additionally, the workshop will cover the prerequisites for using the Python Plugin in EnergyPlus and provide a demonstration of an example class instance for the Python plugin. By the end of the workshop, attendees will have a foundational knowledge of both modes of using the EnergyPlus Python API and will have access to a GitHub repository containing the demonstrated examples.
Workshop requirements
BESOS: a Collaborative Building and Energy Systems Simulation Platform
Workshop Instructor: Fred Boafo and Dave Rulff.
Fred Boafo is a Post Doc. Research Fellow at the Institute for Integrated Energy Systems,
University of Victoria and works in the Energy in Cities Group, headed by Prof. Ralph Evins.
Fred’s research focuses on building energy performance simulation and evaluation,
sustainable energy technologies and energy-saving solutions, and machine learning
problem-solving models towards intelligent buildings and smart cities.
David Rulff is a PhD student at the University of Victoria with Prof. Ralph Evins and the
Energy in Cities Group. David has also spent 10 years as a Technical Lead in the Climate
Change, Resiliency and Sustainability group at WSP. His research explores functional
decomposition of building energy models and the use of machine learning techniques to
build back up useful component surrogate models.
Workshop description
Numerous software modelling tools and design techniques exist to answer questions related to optimal or exploration of building and energy system design. Current research requires that these tools and techniques are combined, leading to the energy-and time-consuming task of linking them. This workshop will introduce attendees to BESOS, a Building and Energy Systems Optimization and Surrogate-modelling platformdeveloped by the Energy in Cities Group and University of Victoria, and used to facilitate interaction of several commonly-used tools to design and analyze building and energy systems. The BESOS Platform is a Jupyter Hub that is able to run the BESOS code base and has all the dependencies installed. This platform is cloud-based and open-source, making it widely available for the research community. An example extended application of BESOS is the Net Zero Navigator (NZN) that functionally explores new building designs in relation to net-zero ready targets. Attendees will be introduced to the platform, run, and modify two test example applications during the demo. The first one will focus on constructing a surrogate model that predicts building energy demand, the second, a building optimization problem.
Workshop requirements
No previous Python/Jupyter notebook coding experience is needed, but a basic understanding of Python/Jupyter notebook could be helpful. Participants will need to install BESOS, and guidance through the installation process will be provided.