A GIS-based thermal mapping and forecasting approach to decarbonize residential heating systems based on metals: A city-level case study

Decarbonizing residential heating requires the widespread implementation of cleaner energy infrastructures. The absence of reliable technologies to phase out natural gas from this sector remains as a key barrier in arresting residential emissions. A methodology to quantify community resources with energetic value to meet residential heating demands is developed. The enabling technology is a seasonal energy storage model that relies on community scrap metals to produce hydrogen and heat on demand via metal-water reactions. A comprehensive geospatial quantification of suitable metal stocks at the city level is derived from municipal solid waste data. The model integrates open-source data and machine learning algorithms for waste generation forecasting. Results indicate that Hamilton, Ontario, can produce between 10 and 14.6 GWh of clean heat per year between 2021 and 2050. This represents an overall reduction of 2.9 ktonnes of space heating CO2 emissions every year. An optimized machine learning model based on a gradient boosting algorithm captured 86% of the variability in the municipal waste data with a root mean square error of 11.8 kg/person. The methodology presents reproducible steps to replicate the analysis in different contexts to facilitate community energy planning.