EnergyPATHWAYS
A bottom-up demand-side forecasting model with detailed energy system accounting. Tracks sales, stocks, service demands, costs, and the equipment components of energy bills — producing full energy carrier demands with high degrees of spatial and temporal granularity. Integrated with RIO for economy-wide analysis.
Who Uses It
State Agencies
California Energy Commission (CEC)
Demand forecasting for state energy planning, policy evaluation, and resource adequacy.
Utilities
Load forecasting
Long-term load forecasting with technology-specific detail, capturing electrification and efficiency impacts on demand shapes.
National Labs & ISOs
NREL, ISO/RTOs
Load shape development — bottom-up hourly profiles that reflect how end-use technology changes reshape system peaks and valleys.
Key Capabilities
Detailed Energy System Accounting
Tracks every piece of energy-consuming infrastructure at the stock level. Sales, retirements, and replacements are modeled explicitly — capturing the infrastructure inertia that determines how energy demand actually evolves over time.
Technology-Level Demand Forecasting
Detailed technology-level energy carrier forecasts — from hybrid heat pump adoption curves and their impact on gas vs. electric demand, to mobility-pattern temporal vehicle load shapes across vehicle classes. Every end use builds up to a complete demand picture.
Sales, Stock & Service Demand Modeling
Projects technology sales shares, stock composition through detailed rollover mechanics, and service demands (miles driven, tons produced, square feet heated). When equipment retires, replacements follow user-specified sales shares, with a visible lag between sales and fleet turnover.
Climate Impacts on Energy Demand
Models how changing climate conditions reshape energy demand — shifting heating and cooling loads, altering peak demand patterns, and changing the seasonal profile of electricity consumption. Essential for utilities and grid operators planning for a warming world.
Hourly Load Forecast Development
Builds bottom-up 8760 hourly load shapes from individual end uses, capturing how technology changes (electrification, efficiency, demand response) reshape system peaks, valleys, and ramp rates. Used by NREL, ISO/RTOs, and utilities for resource planning.
Cost & Energy Bill Components
Calculates the equipment components of energy bills — capital costs, fuel costs, and maintenance disaggregated by energy type and customer class. Users can see how technology decisions flow through to what customers actually pay.
How It Works
Demand Drivers
Population, GDP, industrial output, climate variables, and other drivers form the skeleton for all projections — with regression techniques for years without exogenous data.
Service Demands
Drivers translate into demand for energy services — miles traveled, tons produced, square feet heated — the physical basis for all energy calculations.
Technology Stocks
Stock rollover tracks what equipment exists, what retires, and what replaces it. Sales shares drive fleet composition, with explicit vintage tracking.
Service Efficiency
Technology-specific efficiencies (e.g., miles per gallon, heat pump COP) convert service demand to final energy — capturing gains from technology switching.
Hourly Load Shapes
Bottom-up 8760 profiles built from individual end uses — accounting for weather, technology mix, and usage patterns to produce hourly forecasts by carrier and geography.
RIO Integration
Full energy carrier demands feed directly into RIO for capacity expansion and dispatch optimization, providing the demand foundation for economy-wide analysis.
History
The earliest form was an Excel tool developed for California's Global Warming Solutions Act (AB 32). Later versions were developed in Analytica as the PATHWAYS model before being rewritten in Python as EnergyPATHWAYS and released in 2016 as a full-system energy scenario tool. The supply side of the model was deprecated in 2018 and is now modeled with RIO, an economy-wide optimization. EnergyPATHWAYS today focuses entirely on demand-side forecasting with deep technology-level granularity.
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