MAISY^® Residential and Commercial Utility Customer Databases and Forecasting/Analysis Models

Foundational Resources Supporting Electric Services, Technologies, and Forecasts MAISY® Residential Utility Customer Energy Use and Hourly Loads Databases provide the granular, neighborhood-level energy use and load data utility planners and technology developers need to move from broad assumptions to precise action. Applications include energy customer market assessments, energy technology product development, AI agent-based and digital twin forecasting model development and more. Unlike high-level model-based static EIA and NREL datasets, MAISY® captures localized, hourly energy use tied to housing, appliances, demographics, weather and price/program responses for 7+ million actual U.S. households — ideal for dynamic feeder-level EV impact analysis, smart technology design, and targeted DER deployment. Integrated with MAISY® , the Excel-based SGRC Grid Impact Model applies AI and digital twin analysis to simulate real-world localized grid behavior. With intuitive tools for scenario testing, flexible inputs, and real-time visualization, the model makes complex hourly load modeling both accessible and actionable. ... The SRGC AI Digital Twin Model A critical new imperative for many utilities is forecasting the upsurge in small-area grid loads caused by rapidly growing EV ownership, electrification and weather extremes. Unanticipated grid overloading can cause low voltage, flickering lights, reduced transformer lifetimes and even blown transformers, not to mention customer complaints/ dissatisfaction. Forecasting these neighborhood-level loads requires an entirely new set of tools well beyond traditional utility system-wide energy and peak modeling methodologies. Digital twin modeling, a new forecasting technology that is increasingly being used to model and analyze electric utility distribution equipment, is a perfect application to forecast these small areas EV grid threats. EV digital twin modeling applies information on actual utility customers identifying new EV ownership and grid load impacts and results of load-shifting utility programs. Models are updated periodically to refine customer ownership, loads and utility program model relationships based on actual household information. MAISY Energy Use and Hourly Load Residential Databases, consisting of more than 7+ million actual utility customers across the US, provide the perfect foundation for digital twin modeling. Information for each customer includes hourly electricity use by end-use (space heating, water heating, etc.), dwelling unit data, EV ownership, commuting schedules, income, and a variety of other socio-economic variables. The SGRC Grid Impact Model (GIM) extracts utility customer records for customers residing in a specific utility service area for its digital twins. Changes in customer loads resulting from increased EV ownership, increased electric appliances, weather extremes, price and utility program are forecast to determine impacts on local area transformers and feeders. The digital twins reflect a statistically valid sample of actual utility customers within local areas, providing a low-cost alternative to collecting information on every customer in the service area. Problem areas exposed in GIM scenario analysis can be further evaluated with potential load-shifting programs represented in the GIM and/or with on-the-ground assessments or with detailed transformer/feeder load flow models. SGRC Grid Impact Models Page Read More

Primary MAISY Utility Customer Databases Residential Database 7+ Million actual individual household records across the US Geographic detail: ZIP code areas, counties, utility service areas, etc. SocIo-economic, dwelling unit, end-use energy use, hourly/15-minute electric loads, EV charging loads for each household Commercial Database 600,000+ Commercial US Utility Customer Records building, equipment, occupant, and operational data end-use energy energy use and hourly electric loads for each household Geographic detail: ZIP areas, Metro, Utility, State. ... Summary of all MAISY Databases> Partial list of MAISY Database clients Sample database applications Applications by industry/technology Analysis and consulting projects Read More

Smart Grid Research Consortium (SGRC) The Smart Grid Research Consortium (SGRC) is Back! SGRC 2.0 provides members with individual utility service area Grid Impact Models providing 2030 and 2035 utility, ZIP and neighborhood forecasts with digitial twins modeling technology: Electricity use and hourly kW loads Future EV ownership and commuting hourly load impacts Demand management program impacts Electrification and price response impacts Extreme weather extreme impacts SGRC models use data from the 7+ million MAISY Residential Utility Customer Database in a digital twins AI machine-learning process to model actual utility customers in ZIPs and neighborhoods within each utility service area. An Excel workbook interface provides easy-to-use applications. ... Consortium definition: "A consortium is an association of two or more organizations with the objective of participating in a common activity or pooling their resources for achieving a common goal." source: Wikipedia The Smart Grid Research Consortium (SGRC) was established at Texas A&M University in 2008 to provide utilities with smart grid technology and program cost/benefit models to evaluate new untested technologies/programs for individual utilities. Models reflected unique utility system characteristics to provide utility-specific expected costs and benefit analysis. SGRC management transitioned from Texas A&M to Jackson Associates in 2011 when consortium developer and leader, Dr. Jerry Jackson left his Signature Professorship at A&M and accepted responsibility for meeting continuing needs of Consortium members through his consulting firm. By 2018 field experience with most smart grid technologies and programs provided utility decision-makers with sufficient results to move beyond a model-based decision process. SGRC 2.0 has been re-energized in 2025 to address new electric utility uncertainty challenges that are now more focused on substation, distribution and even tranformer grid threats posed by: increased EV ownership and charging profiles increasing electrification, and weather extremes Assessing these challenges requires knowlege of baseline ZIP and neighborhood whole building utility customer loads and of future EV charging loads, electrification and extreme weather impacts. New SGRC digital twins models provide member utilities with the ability to identify these challenges and to explore demand management, pricing and grid upgrade strategies to avoid threats to grid stability. See Consortium Benefits

Let Us Help Define Your Data Needs Not sure exactly what data items you need, or which data items are best for the project at hand? - Let us help! – just e-mail us with your questions and/or suggest a time to discuss. We provide free consultations to help identify the most useful data/analysis for your application. We also provide free telephone support to assist in client data applications after data delivery. Our no-hassle data-delivery is available as (1) detailed databases, (2) tables and crosstabs or (3) more detailed analysis results with exactly the information you need.

MAISY Energy and Load Forecasting Models MAISY energy models and forecasts have powered energy applications for decades. MAISY clients include fortune 100 companies, start-ups, electric utilities, US, state energy agencies and more. MAISY AI agent-based models provide forecasts and analysis for geographic areas ranging from ZIP codes to utility service areas to states. Model output can also provide detailed household record data for users who want to drill down on specific issues. ... MAISY AI agent-based model methology EV ZIP/census tract saturation forecasts , household hourly loads W/WO EV charging Smart grid, solar, battery storage, DER market analysis and peak hour impacts Microgrid design and assessments Residential household forecasts including household income, demographics, dwelling unit, appliance, energy use, and hourly loads data for 6+ million US households Dwelling unit data, e.g., square feet, space heating equipment, appliances, etc. Location data: ZIP code, county, place, metro area, 30-year degree days Emissions data: Total, electricity, natural gas, fuel oil, propane Annual Energy Use by fuel type (electricity, natural gas, fuel oil, propane) and end use. 8760 and 15-minute kW loads (whole building and end-use, including EVs, monthly averages) See Model Applications

Jackson Associates Provides Industry-Leading Data, Models and Analysis Why trust Jackson Associates (JA) to help with your forecasting, analysis and data needs? The internet is filled with sites offering all kinds of information, often of dubious quality - consider the following: We take responsibility for data integrity - providing decision-makers with information that often informs multi-million-dollar investment decisions- decisions related to product development, marketing and sales strategies for some of the largest US corporations. Our energy, hourly load data, and forecasts/analysis results and our expert witness testimony have supported electric utility and regulatory agency investment decisions in dozens of states. MAISY data have provided the information basis for development of several US Department of Energy energy efficiency standards. Finally, our analytical reputation is outstanding. We were among the first to apply machine learning to integrate and validate disparate data sources and have extended applications to include a variety of statistical enhancements. Our patented business intelligence drill-down software (US Patent 5,894,311, Computer-Based Visual Data Evaluation) has been licensed by every major business intellegence software and database provider including Microsoft, SAP, Oracle, and others.

Recent Updates, Notes & White Papers
Agent-Based Model Uses AI to Map Future Utility EV Distribution Challenges; Identifies ZIPs with Greatest Future EV Increases	Residential Smart Grid and Load Management Technologies Can Offset Near-term Challenges of Unexpected Surges in Electricity Demand	Next-Gen Models: Forecasting EV Loads with Digital Twins. Forecasting New Neighborhood Loads Requires New Tools

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Sample MAISY Clients

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