Mentor Based AI & Biodesign for Sustainable Cities: Smart Buildings, Green Architecture & Urban Systems

About This Course

This four-week, hands-on course shows you how to combine biodesign (biomimicry, living façades, green roofs, vertical greenery, bio-based materials) with AI-driven building and city analytics to improve comfort, reduce energy demand, and cut carbon emissions.

You’ll work step-by-step from building-scale fundamentals (comfort, daylight, shading, energy) to AI-based forecasting and optimisation, and finally expand to district and city-scale decision-making. The course ends with a practical capstone project where you model and compare “business-as-usual” vs “AI + biodesign–optimised” futures—linking building → district → city in a structured scenario workflow (in Google Colab).

Aim

This course aims to train participants to apply biodesign principles and AI-driven performance modelling to design and optimise climate-responsive buildings and sustainable urban systems—scaling from individual buildings to districts and cities.

Course Objectives

By the end of this course, participants will be able to:

• Understand biodesign approaches for climate-responsive, sustainable architecture

• Define and measure comfort, daylight, shading, energy, and emissions goals

• Work with building/urban datasets (occupancy, weather, indoor environmental quality indicators)

• Build basic AI/ML models for energy forecasting and performance analysis in Colab

• Explore smart building controls and AI-driven optimisation with PV/BIPV integration

• Deliver a capstone linking building → district → city scenarios for decision-making

Course Structure

Module 1: Biodesign and Climate-Responsive Architecture

• Biodesign essentials: biomimicry, green architecture, living façades, green roofs, vertical greenery

• Climate-adaptive and bio-based materials for façades and building envelopes

• Performance framing: thermal comfort, daylight, shading, and energy-use basics

• Hands-on: Map biodesign strategies to measurable performance objectives

Module 2: Data and AI Foundations for Building Performance

• Building energy & environment data: loads, occupancy, schedules, weather, IEQ indicators

• AI/ML for energy forecasting: time-series and ML models for predicting energy consumption

• Linking form/material choices to energy performance and model features

• Hands-on: Colab notebook for basic energy-consumption prediction using sample building data

Module 3: Smart Buildings, Automation, and Renewables

• Smart building systems & controls: HVAC, lighting, sensors, BMS/BAS; comfort vs efficiency trade-offs

• AI-driven control and optimisation: setpoints, schedules, operational strategies

• Rooftop PV & BIPV integration: demand + renewables + storage, peak shaving, emissions reduction

• Hands-on: Colab notebook to optimise a simple HVAC + lighting + rooftop PV scenario

Module 4: AI for Smart Urban Systems and Capstone

• Smart cities with AI: land-use, density, green/blue networks, resource flows

• City-scale indicators: aggregating building data to district level; mobility, EV charging, sustainability metrics

• Governance and policy: decision-support dashboards and scenario planning

• Hands-on Capstone: Multi-scale scenario model (building → district → city) in Colab, comparing business-as-usual vs AI + biodesign–optimised futures

Who Should Enrol?

This course is ideal for:

• Architecture, urban planning, and design students/professionals

• Civil, environmental, energy, and sustainability engineers

• Building performance and green building practitioners (HVAC, energy modelling, IEQ)

• Smart city, GIS, and urban systems professionals

• Data science/AI/ML learners interested in climate, buildings, and urban analytics

• Policy, governance, and sustainability stakeholders working on resilient city solutions