Public synthetic load profiles and utility-derived hourly-load databases are both valuable. They are built differently, however, and are best suited to different utility planning questions.
Residential hourly-load data supports EV charging analysis, electrification studies, DSM and VPP program design, solar and storage analysis, rate design and distribution planning.
Resources such as NREL ResStock and OEDI End-Use Load Profiles simulate building characteristics, equipment, weather and operating schedules. They are particularly useful for research, policy studies, technology sensitivity analysis and national or regional scenarios.
MAISY was developed for practical utility planning using detailed residential and commercial customer information derived from millions of utility customer records, weather-adjusted hourly-load estimates, end-use detail and customer segmentation.
The best choice depends on whether the work emphasizes engineered building scenarios or utility-specific customer and geographic planning.
Scroll horizontally to view the complete comparison table.
| Topic | NREL / ResStock / OEDI-type public 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 and 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. | A customer-level utility-data foundation organized into statistically representative customer databases with end-use and hourly-load estimates. |
| Customer behavior | 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 the 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 the dataset and extraction method. | 8,760 hourly loads and, for selected applications, 15-minute loads and load shapes. |
| 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 planning studies, forecasting models, dashboards, customer segmentation and Grid Impact Model applications. |
| Operational realism | Useful for engineered scenario consistency; results can depend heavily on assumptions and may require utility-specific calibration. | Designed to reflect practical customer-level load diversity and planning conditions observed in utility-derived data. |
| Distribution planning | Useful for broad electrification and technology studies; feeder or transformer applications may require additional mapping, calibration, aggregation and utility-specific adaptation. | Supports EV clustering, electrification impacts, DSM/VPP targeting, ZIP and block-group analysis, and feeder or transformer stress screening when linked to utility geography. |
| Best-fit users | Researchers, national laboratories, policy analysts, building-technology analysts and utilities conducting broad scenario research. | Utilities, engineering consultants, energy analysts, DSM/VPP planners, EV program planners, rate analysts, technology companies and distribution-planning teams. |
A strong starting point when the objective is to evaluate technologies, compare building-efficiency measures, test electrification assumptions or conduct broad research.
Often more appropriate when hourly loads must connect to customer segments, local geography, program targeting, technology adoption and distribution planning.
For many utility applications, the central question is not simply what an efficient or electrified home might consume under modeled assumptions. The planning question is more specific: which customers, neighborhoods, ZIP areas, feeders or transformers are most likely to create emerging peak-load problems, and which utility programs can reduce those impacts?
Answering that question requires customer diversity, geography, hourly timing, end-use composition, technology adoption and participation assumptions to operate within one practical planning framework. MAISY customer records reflect the loads of actual, identity-protected utility customers and include detailed socioeconomic, dwelling-unit, appliance, commuting and other characteristics within local utility geographies. NREL ResStock records represent synthetic customers characterized by a more limited set of customer attributes.
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. It helps utilities identify where and when load stress emerges, estimate its magnitude and evaluate mitigation options before reliability problems or capital-investment needs become unavoidable.
The Grid Impact Model does not replace power-flow models or detailed engineering studies. It provides upstream scenario intelligence so utilities and consultants can focus engineering work on the locations and future scenarios most likely to matter.
NREL, ResStock, OEDI, OpenEI and related public resources have made important contributions to residential load-profile research. MAISY serves a different role: utility-derived hourly-load databases and modeling support for applications where customer-level realism, segmentation, geography and implementation are central.
For broad research and public scenario analysis, synthetic 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.
Jackson Associates provides customer energy-use and hourly-load databases, forecasting and analysis, and the Grid Impact Model for electric utility planning.