This training course, delivered by experts from HiDALGO2 CoE, provides a comprehensive pathway from understanding building-level energy dynamics to performing city-scale energy simulations. Participants will gain hands-on experience using the Ktirio Urban Building framework to integrate GIS, vegetation, scenario, and weather data, enabling high-fidelity modeling of energy consumption and environmental impact. The course emphasizes optimization of building envelopes and systems, leveraging HPC resources for efficient large-scale simulations, and mastering data management platforms to store, retrieve, and analyze simulation results. Attendees will learn to interpret and communicate modeling outputs effectively to guide design decisions and support stakeholder engagement in urban planning and sustainable development projects.
Benefits for the attendees: what will they learn
- Comprehensive Modeling Workflow: Master an end-to-end process for building and city energy simulations using Ktirio tools.
- Data Preparation Expertise: Gain hands-on experience in preparing and processing complex GIS, scenario, and weather datasets for robust simulations.
- Performance Assessment and Optimization: Acquire the ability to assess, interpret, and improve energy performance across diverse urban settings.
- Scalability and Efficiency: Learn to leverage HPC resources and data management platforms to handle large-scale simulations and streamline workflows.
- Validate, interpret, and communicate simulation results to inform design and policy decisions.
Fees
This course is free of charge and is organized in the framework of the HiDALGO2 project.
Pre-required logistics
- Basic Command-Line Proficiency: Comfort with navigating file systems, running commands, and handling data in a Linux environment.
- Introductory HPC Concepts: A general understanding of high-performance computing workflows and job submission.
- Basic Building/Urban Planning Knowledge: A foundational grasp of building structures and urban layouts can help participants better understand the modeling scenarios.