Different Database Designs for Different Planning Questions

Public synthetic load profile databases have significantly improved access to detailed residential building energy simulations. They are especially useful for research, policy studies, technology sensitivity analysis, and national or regional electrification scenarios.

MAISY databases were developed for a different purpose: practical utility planning using detailed residential and commercial customer load information derived from millions of utility customer records, weather-adjusted hourly load estimates, customer segmentation, and end-use load detail. The focus is not simply on generating representative load shapes, but on supporting utility applications where observed customer diversity, geographic detail, and planning workflow practicality matter.

Comparison of NREL-Type Synthetic Load Profiles and MAISY Utility-Derived Databases

Topic	NREL / ResStock / OEDI-Type Public Load Profile Databases	MAISY Utility Customer Energy Use and Hourly Loads Databases
Core methodology	Physics-based building simulation using modeled building characteristics, equipment, weather, and operating assumptions.	Utility-derived statistical and end-use load estimation based on millions of residential and commercial utility customer records and related customer/building information.
Primary purpose	Research, policy analysis, technology assessment, and broad scenario evaluation.	Utility planning, customer load analysis, DSM/VPP evaluation, EV and electrification analysis, forecasting, and localized grid impact assessment.
Data foundation	Synthetic building models calibrated and validated at broad levels using measured data and other reference sources.	Customer-level utility data foundation expanded and organized into statistically representative customer databases with end-use and hourly load estimates.
Customer behavior representation	Depends on modeled schedules, equipment assumptions, occupancy assumptions, and simulation inputs.	Reflects the diversity of actual customer energy use patterns embedded in utility-derived data and customer segmentation.
Geographic detail	Strong for climate, building stock, and broad geographic scenario analysis.	Designed for utility service areas, ZIP codes, customer segments, block groups, and other planning geographies depending on application.
End-use detail	Detailed simulated end uses such as heating, cooling, water heating, appliances, lighting, and other building loads.	Whole-building and end-use hourly loads including space heating, air conditioning, water heating, appliances, and other customer load components.
Time resolution	Hourly or sub-hourly profiles depending on dataset and extraction method.	8,760 hourly loads and, for selected applications, 15-minute load data and load shapes.
Practical data handling	Large public datasets can require substantial preprocessing, cloud storage, cloud computing, or custom data pipelines for many utility applications.	Structured for direct use in utility consulting, planning studies, forecasting models, dashboards, customer segmentation, and Grid Impact Model applications.
Operational realism	Useful for engineered scenario consistency; results can depend heavily on modeling assumptions and may require additional calibration for specific utility planning applications.	Designed to reflect practical customer-level load diversity and planning conditions observed in utility-derived data.
Distribution planning suitability	Useful input for broad electrification and technology studies; may require additional mapping, calibration, aggregation, and utility-specific adaptation for feeder or transformer planning.	Designed to support localized planning, including EV clustering, electrification impacts, DSM/VPP targeting, ZIP/block-group analysis, and feeder/transformer stress screening when linked to utility geography.
Best-fit users	Researchers, national laboratories, policy analysts, building technology analysts, and utilities performing broad scenario research.	Utilities, engineering consultants, energy analysts, DSM/VPP planners, EV program planners, rate analysts, technology companies, and distribution planning teams.

When Public Synthetic Load Profiles Are Often the Right Starting Point

Public synthetic load profile resources are valuable and should not be dismissed. They are often a strong starting point when the objective is to evaluate technologies, compare building efficiency measures, test electrification assumptions, or conduct broad policy and research analysis.

When Utility-Derived Hourly Load Databases Are Often More Appropriate

Many utility applications require more than a representative synthetic load shape. Utilities often need load data that can be connected to customer segmentation, local geography, DSM/VPP program targeting, EV adoption patterns, and distribution planning questions.

Why the Difference Matters for Utility Planning

For many utility planning applications, the central question is not simply what an efficient or electrified home might consume under a modeled set of assumptions. The planning question is often more specific: which customers, neighborhoods, ZIP areas, feeders, or transformers are most likely to create emerging peak load problems, and what utility programs can reduce those impacts?

That question requires customer diversity, geography, hourly timing, end-use composition, technology adoption, and customer participation assumptions to work together in a practical planning framework. This is where the MAISY databases and Jackson Associates forecasting models provide value beyond generic hourly load profiles.

Connection to the Grid Impact Model

The Grid Impact Model extends the MAISY database and forecasting framework to evaluate EV adoption, electrification, weather extremes, managed charging, DSM, and VPP strategies at localized planning levels. The model is designed to help utilities identify where and when load stress emerges, estimate the magnitude of that stress, and evaluate mitigation options before reliability problems or capital investment needs become unavoidable.

Rather than replacing power-flow models or engineering studies, the Grid Impact Model provides upstream scenario intelligence. It helps utilities and consultants focus detailed engineering work on the locations and future scenarios most likely to matter.

Summary: Not Better or Worse — Built for Different Uses

NREL, ResStock, OEDI, OpenEI, and related public resources have made important contributions to residential load profile research. MAISY serves a different role. It provides utility-derived hourly load databases and modeling support designed for planning applications where customer-level realism, segmentation, geography, and practical implementation are central.

For broad research and public scenario analysis, synthetic load profiles can be the right starting point. For utility-specific planning, DSM/VPP analysis, EV and electrification impacts, and localized grid stress evaluation, a utility-derived hourly load database may provide a more practical foundation.