Energy Intelligence API
The most flexible tool in a developer’s energy toolbox
Get any household’s full energy footprint, as granular as hourly, disaggregated to end use.
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Use Cases
Palmetto’s Energy Intelligence API is a flexible tool for understanding the details of any home’s energy footprint
Baselines and hypothetical scenarios for any home
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Detailed simulations provide a comprehensive view of a home’s current energy usage, and potential future usage given any of 60+ home upgrades, including solar, HVAC, roofing, and more.
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Device-level consumption and production
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Altering device attributes lets a user understand the energy impact of upgrading specific appliances. In addition, all predictions are disaggregated to fuel type and end use, highlighting opportunities for improved efficiency.
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Bill size, savings, and climate impact
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Integrations with rates, tariffs, and carbon emissions data enable accurate estimates of utility costs, forecasted savings from energy upgrades, and carbon footprint. Provide homeowners with clear financial and environmental insights to drive smarter energy decisions.
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For developers, by developers
Developer-friendly API docs, interactive demo, quick-start recipes, and more, with free usage up to 500 API hits per month.
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The Science Behind Energy Intelligence
Palmetto’s Energy Intelligence team are experts in building science and geospatial data. Our software uses cutting-edge data enrichment, machine learning, and physics-based simulations to estimate the energy usage of any home in the U.S.—from single-family houses to apartments and mobile homes. By creating a digital twin of each home, our model predicts energy consumption down to the hourly level, considering heating, cooling, and base equipment loads, while also evaluating upgrade scenarios for improved efficiency and electrification. Each home is modeled as an ensemble of synthetic variations, capturing uncertainty and regional variability.
Our system intelligently infers 60+ building characteristics that matter for energy consumption. Overrides may be provided for any value. When actual energy usage data is available, the model calibrates for even greater accuracy.