Examples of MAISY database, forecasting and analysis applications—from customer hourly loads and grid impacts to smart-grid programs, electrification and emissions.
Current applications focus on developing actionable customer and neighborhood load intelligence without requiring utility AMI interval data.
Utilities need customer-level hourly-load visibility to manage EV growth, electrification and rising peak demand. Raw interval data alone does not provide forward-looking load intelligence, and extracting value from AMI can require substantial investments in data systems, analytics and specialized staff.
Customer-level digital twins provide another path. Identity-protected customer characteristics can be matched with large, pre-developed databases and end-use load shapes to estimate how individual customers use energy hour by hour.
This approach does not replace AMI or engineering tools. It fills the upstream planning gap by showing where and when stress may emerge and where detailed engineering analysis is warranted.
Read the full paper Explore the Grid Impact Model
This paper describes how large public and proprietary data resources can be integrated to model the hourly electricity use of actual, identity-protected utility customers and determine neighborhood grid demands.
The application supports ZIP-detailed analysis of future EV charging, smart-grid management and control, weather extremes and other grid-planning questions.
Review the model methodology
Neighborhood-level EV load forecasting requires tools beyond traditional systemwide energy and peak models. This note explains how digital twins use actual customer characteristics to estimate new EV ownership, charging loads and the effects of utility load-shifting programs.
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The model forecasts whole-building 8,760 hourly loads and EV charging loads for ZIP-defined geographic areas, including utility service areas, metropolitan areas, states, neighborhoods and census tracts.
Read the two-page description
The analysis identifies ZIP areas with the greatest prospective EV increases to help utilities prioritize distribution planning. It addresses the local effects that can be hidden within systemwide forecasts, including low voltage, transformer loading and customer-service risks.
Read the four-page paperA May 14 study of 18,400 U.S. households examines the grid-capacity and emissions implications of residential electrification. Across the modeled states, average greenhouse-gas savings were 2.5%, while peak-period demand increased 8.5% and annual electricity use increased 9.1%. The paper provides state-level analysis and commentary on ten representative states.
View the studyApril 11. This update examines reporting risks created by changes in mortgagor geography and other customer characteristics. It explains how index-based emissions-intensity calculations can remove biases that simple aggregate measures may introduce.
April 4. Extended U.S. Department of Energy RECS databases with end-use hourly loads show how residential smart-grid technologies can offset part of near-term demand growth. The Arizona example suggests potential peak-demand reductions of as much as 10%.
February 21. This paper describes an intuitive Excel agent-based framework using the extended 2020 RECS database, including whole-building and end-use 8,760 hourly loads for more than 18,400 households.
February 6. The two-page note presents state-level whole-building profiles and illustrates how individual household loads can improve targeting for water-heating and other load-management programs.
July 18. The note shows why annual emissions-intensity comparisons based on simple aggregate emissions can be misleading and explains the value of index-based calculations.
June 21. A concise guide to calculating financed emissions for residential mortgage and commercial real-estate portfolios, consistent with PCAF methodologies and then-proposed SEC guidance.
April 18. Analysis using actual mortgagor data shows why accurate estimates require at least ZIP-level detail and why unbiased annual intensities should reflect changing customer-population characteristics.
March 28. Ten-year forecasts identify five states as “yes” and 31 as “no,” based on the observed 2019–2022 pace of solar and wind additions and modeled water-heating emissions.
March 16. The study estimates that such bans would increase carbon emissions in 36 states by an average of 6% and increase household energy costs by $176 per year; the paper includes results for all states.
February 16. State rankings identify New Hampshire as the most affected, followed by Maine, Vermont, Connecticut, Massachusetts, Rhode Island, New York, New Jersey, California and Illinois.
January 24. Estimated 2023 mortgage delinquencies of 90 days or more caused by four-decade-high inflation are provided by state and county.
Earlier publications on utility forecasting, smart-grid investment, distributed energy, energy efficiency and emissions.
Contact Jerry Jackson, President, Jackson Associates, to discuss the research or a related utility analysis.