Forecasting residential heating and electricity demand with scalable, high-resolution, open-source models

Electrifying space and water heating is a critical priority for the energy transition. The necessary widespread adoption of heat pumps will have significant impacts on the power grid. Studies report heating electrification may increase winter peak electricity demand by up to 70%, with some colder regions experiencing a more than fourfold increase in peak demand. Contending with this increased demand will necessitate unprecedented upgrades to the power grid. The process of upgrading the grid has a critical spatial dimension, as heating demand, electricity demand, and the capacity of existing grid infrastructure vary significantly across regions. Grid planning also involves a critical temporal dimension: short-term weather patterns and long-term climate change introduce complexities and uncertainties that can be difficult to quantify. However, most existing demand forecasts are provided and validated only at aggregated spatial scales, lack temporal detail, and provide single-valued predictions. Without accurate, probabilistic, and spatially and temporally resolved demand forecasts, planners risk misallocating scarce resources. We present a novel framework for high-resolution forecasting of residential heating demand and non-heating electricity demand using probabilistic deep learning models. Because our models are trained on electricity consumption from a predominantly gas-heated region, the learned electricity demand patterns primarily reflect non-heating end uses such as lighting, appliances, and cooling. We focus specifically on providing hourly building-level electricity and heating demand forecasts for the residential sector. Leveraging multimodal building-level information – including data on building footprint areas, heights, nearby building density, nearby building size, land use patterns, and high-resolution weather data – and probabilistic modeling, our methods provide granular insights into demand heterogeneity. Validation at the building level underscores a step change improvement in performance relative to NREL’s ResStock model, which has emerged as a research community standard for residential heating and electricity demand characterization. In building-level heating and electricity estimation backtests, our probabilistic models respectively achieve RMSE scores 18.8% and 27.6% lower than those based on ResStock, with probabilistic forecast quality measured via WIS improving by 59% for both applications. By offering an open-source, scalable, high-resolution platform for demand estimation and forecasting, this research advances the tools available for policymakers and grid planners, contributing to the broader effort to decarbonize the U.S. building stock and meeting climate objectives. • Developed a scalable, open-source probabilistic deep learning framework for building-level, hourly heating and electricity demand forecasting. • Achieves 18.8% and 27.6% RMSE reductions over NREL’s ResStock for heating and electricity, with 59% improvement in probabilistic forecast quality (WIS). • Leverages multimodal geospatial and weather datasets including remote sensing, census, and ERA5 reanalysis data for robust, granular insights. • Demonstrates heat pump classification using learned consumption patterns (ROC- AUC 0.77) and building attributes alone (ROC-AUC 0.72), enabling penetration assessment without customer surveys. • Supports grid planning, electrification strategies, and policy development for building decarbonization.