Image credit: Polar Bears International / BJ Kirschhoffer / AFP - Getty Images
Heating Loads in Alaska — and Beyond
Alaska– and the wider Arctic region– have experienced accelerated effects of climate change over the past decade. Rapid decarbonization of energy is a critical step for mitigating climate change and ensuring sustainable development. Since about 70% of energy consumption in Alaska can be attributed to commercial and residential heating, successful decarbonization requires an assessment of present and future heating needs in the region.
To date, comprehensive and accurate heating load estimates are lacking in Alaska. Modeling heating loads in Alaska is particularly complex for two distinct reasons:
- First, many communities do not connect to the Energy Railbelt utility grid, making it difficult to accurately measure their heating loads.
- Secondly, Alaska lacks centralization in utility providers and building codes, meaning there are no consolidated energy use data or efficiency standards for buildings.
This project develops a geospatial-first methodology using machine learning to estimate heating loads. It represents the first attempt to quantify Alaskan heating loads at scale and high granularity. We utilize open-access datasets and Google Earth Engine’s cloud computing platform to extract building features (e.g., height, square footage, and age) and local climate variables (heating and cooling degree days). We explore statistical and machine learning models and validate them against existing small-scale regional models from AK Warm simulation software.
This project has strong potential for scalability across the wider Arctic, which mirrors many of the energy security and decarbonization challenges faced in Alaska. Therefore, the models developed through this work offer a starting point for pan-Arctic energy estimation and climate change mitigation methods.
The Team
Project Lead: Dr. Erin Trochim
Data Science Lead: Dr. Nicholas Bolten
DSSG Fellows: