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Thornton Tomasetti Corporate Responsibility Report
  • Overview
    • Financial Vitality Progress Report
    • Executive Message
    • Our Corporate Responsibility Approach
    • Our Materiality Topics
    • Our Goals & Targets
  • Our Projects
    • Our Projects Progress Report
    • Climate Action Innovations
      • AI & Machine Learning for Climate Prediction
      • A Greener Floor System
      • Next-Gen AI Energy Modeling
      • Optioneering Façade Embodied Carbon
      • Sustainable Façades Get Greener
    • Energy Demand Projects
      • The City of London’s Lowest-Embodied-Carbon Building
      • Making Thermal Networking Work
      • Mass Timber on a Large Scale
      • The Long Decarbonization Game
    • Energy Supply Projects
      • All Power to the Energy Transformation
      • Growing Energy Independence
    • Resilience Projects
      • Going Against the Flow
      • Preparing for a 500-Year Flood
  • Sustainable Operations
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    • Building Great Workplaces Progress Report
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Home Our Projects

AI & Machine Learning for Climate Prediction

June 26, 2024
Measured Temperature vs. AI and Common-Practice Models.

Climate risk assessments that inform how the built environment might accommodate future weather rely heavily on climate-model predictions derived from historical trends and data. AI presents an exciting opportunity to explore new ways to model these conditions.

Using 70-year continuous historical datasets recorded by the National Oceanic and Atmospheric Administration, we trained ML models to predict an hourly temperature dataset that can be used in building analytics to estimate monthly heating and cooling demand.

Incorporating additional information, like additional data stations, dry and wet bulb temperatures, dew point and relative humidity, we trained the model to predict hourly temperature data for 2020. The predicted and measured data track each other closely.

Our AI models, overall, produced less error than the common practice (University of Southampton’s CCWorldWeatherGen), which underpredicts heating demand and overpredicts cooling load. Further refinement of our approach should lead to improved building design and economic modeling of heating/cooling demands.

 

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Related Our Projects Articles

The City of London’s Lowest-Embodied-Carbon Building

Preparing for a 500-Year Flood

Sustainable Façades Get Greener

A Greener Floor System

Making Thermal Networking Work

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  • Overview
    • Financial Vitality Progress Report
    • Executive Message
    • Our Corporate Responsibility Approach
    • Our Materiality Topics
    • Our Goals & Targets
  • Our Projects
    • Our Projects Progress Report
    • Climate Action Innovations
      • AI & Machine Learning for Climate Prediction
      • A Greener Floor System
      • Next-Gen AI Energy Modeling
      • Optioneering Façade Embodied Carbon
      • Sustainable Façades Get Greener
    • Energy Demand Projects
      • The City of London’s Lowest-Embodied-Carbon Building
      • Making Thermal Networking Work
      • Mass Timber on a Large Scale
      • The Long Decarbonization Game
    • Energy Supply Projects
      • All Power to the Energy Transformation
      • Growing Energy Independence
    • Resilience Projects
      • Going Against the Flow
      • Preparing for a 500-Year Flood
  • Sustainable Operations
    • Sustainable Operations Progress Report
    • Emissions Report 
    • Green Offices Report
    • Sustainable Procurement 
  • Helping Our Communities
    • Helping Our Communities Progress Report
    • Charitable Giving
    • Community Service
    • Disaster Response & Relief
    • Thornton Tomasetti Foundation Update
  • Building Great Workplaces
    • Building Great Workplaces Progress Report
    • Equity, Diversity & Inclusion
    • Learning & Professional Growth 
    • Our Workplace Culture
  • ESG Disclosures

© 2024 Thornton Tomasetti