Residential and commercial customer data for load forecasting, technology analysis, customer segmentation and utility planning. Select the geography, variables and load detail your project needs—or have Jackson Associates prepare the analysis.
The database families serve different purposes. Select them by customer population, geographic requirements and analytical use rather than assuming every product includes the same fields.
More than 7 million identity-protected household records combine demographic, income, dwelling, equipment, commuting and energy-use information. Block group and ZIP-defined selections support geographic and customer-segment analysis, with whole-home and end-use load options.
Household ownership probabilities and modeled charging profiles connect adoption potential and travel patterns to hourly demand. Optional baseline whole-home loads show how charging combines with existing residential use.
Extensions of EIA’s 2020 Residential Energy Consumption Survey add whole-home hourly profiles, optional end-use detail and emissions estimates to the survey’s 18,400+ household records. This resource supports analysis anchored to RECS building, equipment and occupant variables.
The commercial offering includes 600,000+ customer records with building, equipment, operating and energy-use characteristics. Whole-building and end-use hourly profiles support technology, market, forecasting and customer-class studies.
Available fields vary by database. A project can request selected variables, data groups, customer records or prepared summaries.
| Data category | Representative content | What it supports |
|---|---|---|
| Customer characteristics | Household income, composition, education and travel characteristics; commercial establishment and operating characteristics. | Customer segmentation, program targeting and market-potential analysis. |
| Buildings and equipment | Floor area, heating fuel, HVAC equipment, appliances and other product-specific building variables. | Electrification, efficiency and technology studies. |
| Annual and end-use energy | Electricity and other fuel use, energy costs and end-use breakdowns such as space conditioning and water heating. | Baseline analysis, fuel switching, savings estimates and technology sizing. |
| Time-specific electric loads | 8,760 hourly profiles, optional 15-minute detail, whole-building and end-use loads, and selected summary profiles. | Peak coincidence, customer-class profiles, load-factor analysis and flexible-load studies. |
| Geographic information | ZIP, county, metropolitan area, state and other identifiers available with the selected resource. | Geographic comparisons and area-specific customer analysis. |
| EV and travel data | Ownership probabilities, commuting characteristics and modeled charging profiles where specified. | EV scenarios, localized demand and managed-charging analysis. |
| Emissions estimates | Fuel-specific and total CO2e estimates where included in the selected database. | Comparisons of energy use, fuel choices and technology scenarios. |
MAISY integrates public and proprietary household, building, equipment, energy, travel and utility data. Proprietary machine-learning integration methods extend actual identity-protected individual customer records with fields from supporting sources.
Hourly end-use profiles draw on metered source samples and are calibrated to annual energy use.
This approach provides a robust customer-level analytical foundation.
GIM combines the single-family residential data foundation with an Excel interface, forecasting engine and analytical worksheets.
Allocate utility-wide adoption scenarios to representative customers and develop customer, block-group, ZIP and service-area charging-load results.
Combine localized forecasts with utility-selected capacity, existing loading and planning limits to identify areas deserving asset-specific engineering review.
Evaluate avoided wholesale demand charges, energy margins, participation, control effectiveness and program costs through payback, NPV and cash-flow outputs.
Compare electrification, weather stress, customer growth, demand response, batteries and VPP scenarios on a common hourly-load foundation.
JA’s historical assignments span multiple databases, models and consulting services. They demonstrate analytical experience across the portfolio, rather than implying that every client used the current GIM.
Customer-class hourly loads, cost-of-service studies, end-use forecasts and efficiency or demand-response program analysis.
Customer-segment potential, equipment sizing and economics for PV, storage, heat pumps, CHP and other energy technologies.
Energy scenarios, technology assessment, efficiency-program evaluation and analysis of policies and standards.
Geographic and customer-segment analysis, load shapes and market potential for electricity suppliers and energy-service firms.
Fuel-use baselines, heating and equipment changes, and emissions comparisons under defined assumptions.
Cross-tabs, segment profiles, geographic comparisons and client-specified summaries prepared by Jackson Associates.
| Example Applications | |||||
| Application Areas | Applications | Clients | Database | Scope | JA Deliverables |
| Electric vehicles | Mkt sizing, mkt potential | 3 EV companies | Residential | States | Databases & analysis |
| PV solar | Product sizing, mkt potential, profit analysis | 3 manufacturer-retailers | Residential | States | Databases & analysis |
| Battery storage | Product sizing, mkt potential, profit analysis | 1 manufacturer, 1 PV solar company | Residential & Commercial | States | Databases & analysis |
| Microgrids | Product evaluation, mkt potential | 1 electric/gas utility | Residential | States | Databases |
| Ground source ht pumps | Mkt potential, profit analysis | 1 electric/gas utility | Residential | Florida | Analysis |
| Smart grid tech (2-way thermostats, etc) | Business case analysis for individual technologies | 20 Electric Utilities | Residential & commercial | Utilities/states | Analysis & Software |
| Electricity forecasts (AB models through 2060) | Utility electricity forecasts/ energy efficiency regulations impacts | 26+ electric utilities, utility organizations | Residential & commercial | Utilities/states | Forecasting software |
| Retail electricity suppliers | Mkt potential, profit analysis | 13 retail electricity suppliers | Residential & commercial | States/ national | Databases |
| Govt energy forecasts, technology, program analysis | Tech analysis, energy forecasts and regulation impact analysis | 2 national labs, DOE, 9 state agencies | Residential & commercial | National | Databases & analysis |
| Energy efficiency firms | Mkt potential, profit analysis | 5 firms | Residential & commercial | States/national | Databases analysis |
| Small CHP (cogeneration) | Product sizing, mkt potential, profit analysis | 2 manufacturers | Residential & commercial | National | Analysis |
| Commercial CHP (cogeneration) | Product sizing, mkt potential, profit analysis | 2 manufacturers | Commercial | National | Databases & Analysis |
| Fuel cells | Product sizing, mkt potential, profit analysis | 3 manufacturers | Residential & Commercial | National | Databases & Analysis |
| Cool storage | Product sizing, mkt potential, profit analysis | 1 manufacturer | Commercial | National | Databases |
| Site-based wind | Mkt potential, profit analysis | 1 manufacturer | Commercial | Texas | Databases |
| Cost-of-service analysis | 8760 hourly loads by customer class | 3 electric utilities | Residential & commercial | Utilities/states | Databases & analysis |
Database cost and licensing are defined for the geography, fields, time detail and required outputs.
Describe the planning, market or technology question and the customer population.
Identify block groups, ZIPs, states or other requested areas and the appropriate database family.
Select customer variables, energy fields, end uses and hourly or 15-minute requirements.
Specify CSV records, Excel summaries, cross-tabs, load files or prepared analysis.
Agree on data vintage, assumptions, deliverables, permitted use and project cost.
Customer-based profiles and engineering simulation datasets can both be useful. Evaluate their geographic detail, source vintages, available variables, load calibration, processing requirements and suitability for the decision.
No. That figure describes the broad residential database resource. RECS extensions, commercial data and geographic selections have different populations and record counts.
Yes. The engagement can specify data groups, individual variables and geographic coverage, or request summaries instead of the full customer-level dataset.
Proprietary machine-learning methods integrate actual metered end-use 15-minute loads source data with end use appliances and energy use available in each household record and calibrate to whole building household energy use.
They are an option in selected MAISY offerings. Confirm the database, end-use coverage, time convention and file structure in the project scope.
No. The commercial data support separate analysis applications. Current GIM applications cover single-family residential utility customers.
Yes. JA can supply client-specified tables, cross-tabs and analysis outputs. A GIM-based report is also available for questions within the model’s current scope.
Discuss a database selection, customized analysis or utility-specific Grid Impact Model application.