This page describes MAISY Utility Customer Energy and Hourly Loads Databases detailed characteristics
Click Here to view a summary description of these databases.
|Database Size||Database Format|
|Database Maintenance||Raw Data Sources|
|Database Structure||Geographic/Segment Options|
|Energy Use Data||Electricity kW Data|
|Non-Energy Data Items - Residential||Summary kW Load Profile Data|
|List of Commercial Database Variables||List of Residential Database Variables|
Contact Us For Infomation on Industrial Energy and Hourly Loads Databases
Additional 2020 MAISY Utility Customer Database Detail
Recent Residential Database ExtensionsRecent data item additions include:
15-minute load detail are added to hourly kW load detail a standard option.
As of 2020, whole building and end use load data in any time segment from 0.5 seconds to 5 minutes is available on a project basis.
The Database Development Process Each database development includes four steps:
Comparison With Other Data Sources: The only other US data sources drawing on individual utility customer data and encompassing the entire country is the Department of Energy EIA's RECS (Residential Energy Consumption Survey) and CBECS (Commercial Buildings Energy Consumption Survey) surveys. While RECS and CBECS data are useful for some national and regional policy analysis and for regional or national sector characterizations, small sample sizes, large standard errors, lack of geographic detail, errors in modeled end-use energy use data, dated information, and limited coverage provide a questionable basis for using these data sources for most market and sales analysis, product development and design and other applications.
For example, While the Department of Energy’s CBECS survey documentation reports 95% confidence intervals of +/- 8 percent for total US commercial buildings electricity consumption for its 2003 survey; drilling down to individual buildings (e.g., office, retail, etc.) yields 95% confidence intervals greater than +/- 25 percent for half of the sixteen building types. Drilling down to smaller geographic areas provides even less accuracy. For example, the 95% confidence interval for major fuels consumption in the West census region is +/- 67% for more than half the sixteen building types (e.g. food sales is +/ 84%). Applying these data means that, for example, food sales fuels consumption likely range is between 0.012 and 1.84 times the CBECS estimate. Drilling down for more detail results in even greater confidence ranges rendering results from these national surveys of limited use for geographic areas smaller than the nation as a whole.
The Critical Issue of Sample Size in Drill Down Accuracy The reason that accuracy of the CBECS and RECS degrades so quickly when drilling down to regions, building types or other variables is a reflection of the small number (~5,000 to ~8,000) customer records in these data sets. More than 7 million MAISY customer records support data accuracy even when drilling down a dozen or more levels (e.g., single family dwelling unit/square feet >2000/income >$100,000/ householder age < 65/etc. ). For example, a special extraction of residential customer records for a major manufacturer provided 50,000 statistically representative customer records for the Los Angeles metropolitan area and the same number for the San Francisco metropolitan area.
By reconciling and integrating a variety of customer data sources, determining population characteristics, and applying a robust sample design, MAISY databases are able to deliver reliable detailed utility customer energy use and characteristics for small geographic areas and detailed customer specifications. MAISY databases are compiled from many data sources including:
Caution: Pitfalls Using Department of Energy energy Use and OpenEI energy and hourly load data for utility customer market-oriented analysisUS Department of Energy residential and commercial databases provide information on a small sample of US utility customers as well as engineering modeled estimates of 8760 hourly loads developed by NREL, National Renewable Energy Laboratory and provided on the OpenEI Web site.
Many of our clients come to us after frustrating attempts at applying these data for their market-related analysis. Small sample databases (typically 5 - 9k customers for the entire US) end up being of little use when assessing market segments whether defined by geography (e.g., states or metro areas) or customer characteristics (e.g, business type, income level). Confidence intervals of +/- 50 are not unusual for these segments. The OpenEI commercial and residential engineering model-based hourly 8760 load results reflect a limited set of model input assumptions that typically do not reflect the population and cannot reflect the diversity of customers in market segments of interest.
MAISY Utility Customer Energy Use and Hourly Load Databases have been developed specifically to serve the information needs of energy-related organizations in addressing real-world energy market-related issues. Having served nearly 200 clients ranging from start-ups to Fortune 50 companies, we understand market-based analysis needs. With databases of more than 7 million utility customers including hourly data based on metering studies, MAISY databases provide superior market intelligence with unequaled accuracy.
This limited Department of Energy data are free so we understand why some companies start their market information development with these data sources; however, considerable wasted time and costs are typically associated with an attempt to use these data for meaningful market-related information. We encourage you to click on the link below for additional discussion or feel to contact us for additional discussions.
Click Here to see more information regarding cautions using Department of Energy sources and advantages of using MAISY database data.
15-minute electric loads are also available and are based on metered electricity use data. As one would expect, 15-minute data shows considerably more variation than hourly data (15-minute loads reflect an average of kW demand over 15 minutes rather than over an hour).
Relationships between 15-minute and hourly loads depend on a variety of factors including the presence of electric space heating, water heating, air conditioning as well as dwelling unit and household characteristics (e.g, number of household members).
15-minute/hourly relationships are illustrated below for two days for a California utility customer with annual electricity use of 10,083 kWh. kW load data are provided for each of the 96 15 minute intervals in the day. The blue lines reflects the hourly kW for each of the 4 15-minute time intervals within each hour. The red lines reflect the 96 15-minute kW loads in the day.
Load detail can also be provided as day-type /month summaries (week day, weekend day, peak day for each of the 12 months) or other time-specified intervals for electric, natural gas and oil energy use.
Load data are weather-adjusted to reflect normal hourly weather data. Users can access and evaluate hourly loads for individual customer records or for any grouping of customers defined by database variables (e.g., heating fuel, business type, square feet, number of children, etc.) The large number of customers in the databases and the database design permits users to develop hourly load information for detailed customer types and market segments based on relevant customer characteristics.
Click Here to See More Detail on 8760 MAISY Hourly Loads Detail and Client Hourly Load Applications
Click Here to See More Detail on all MAISY kW Loads Detail (hourly , 15-minute, etc.) and Client Hourly Load Applications
Click Here to See Options for Custom Hourly Loads Databases
For example, if the population of customers includes a 10 percent electric space heating saturation, a random sample of 2000 would provide only about 200 electric space heating customers. However, with 20 commercial building types, the confidence interval around building-information would be quite high especially when one drills down to evaluate electric-heated buildings in different size categories.
We know that electric space heating customers are of interest to our clients' applications so we boost the number of electric space heating customers pulled from the master MAISY database to ensure that users can conduct multiple drill downs on electric space heating customers with confidence. We apply the same criteria with other important customer variables. MAISY database record weights automatically adjust for this oversampling so that total customers, energy use and other customer segment characteristics always correctly reflect population values.
Customer segment database information supports new technology product development and market assessment, marketing and sales market sizing and evaluations, and other applications that utilize information on markets and market segments.
In addition to electric load data, additional information is provided for each segment including number of customers and average or typical characteristics of customers in each segment. The table below illustrates typical detail associated with both Commercial and Residential Databases.
Segment definitions (e.g., ranges of floor space, peak kW, annual kWh, household income, geographic areas, etc.) are determined in collaboration with JA clients to meet technology development and/or marketing needs.
Example Questions That Can Be Answered With MAISY Data:
- How do hourly or 15-minute load profiles vary across customer segments and what are the impacts on product design and system cost?
- How do customer financial benefits vary across customer segments?
- Which customer segments should marketing and sales campaigns focus on to offer the greatest customer energy bill savings?
- What customer characteristics are most closely associated with the greatest energy bill savings?
- What operational strategy maximizes customer electric bill savings given utility rate structures and incentives?
- How many potential customers are associated with different customer segments?
- What are the implications of load profiles on technology performance, control strategies, lifetime, and other operating and maintenance issues?
- Apply segment-specific load profiles in marketing material targeted to individual segments
- Apply segment-specific load profiles in direct sales contacts to illustrate operation and economic benefits to prospective customers
- Scale load profiles using customer annual or monthly energy use to develop customer hourly load estimates
Deciding on Segment Versus Individual Customer Databases
Segment versus Customer Data - How to determine the best customer information development strategy
Jackson Associates also develops custom database interfaces to facilitate and automate client-specific analysis needs. MAISY EnergyApps provide analysis based on user-specified customer segment characteristics of interest such as SIC/NAICS code, floor space, operating hours, ZIP codes etc. (Commercial) or income, demographics, ZIP codes, etc. (residential) along with application detail such as cost of service characteristics, pricing products and so on. EnergyApps are available to support REP, ESCO, combined heat and power analysis, demand response, energy efficiency and other applications.
Jackson Associates has demonstrated exceptional ability to provide client-oriented analysis software as demonstrated with its patented drill-down technologies that has been licensed by all major business-analytics software companies including Microsoft, SAS, and other companies.
EnergyApps extend the level of customer information and analysis detail significantly with internal EnergyApp modeling and analysis. For example, if the user specifies a zip code in the user interface, weather adjustments are applied to heating, air conditioning and ventilation energy use and hourly loads.
The following links provide additional MAISY database information Additional information on MAISY EnergyApps
What about other load-profiling systems that offer 12, 36 , 75 or some other limited number of fixed customer segments? To represent 13 commercial business types; electric, gas and oil heat; small, medium and large buildings requires 117 prototypes or "typical" buildings. Add in age categories and more than 200 "fixed prototypes" would be required, well beyond the scope of these "fixed" systems. With MAISY, customer and segment selections provide hundreds of possible definitions with nearly unlimited choices of customer characteristics. Only MAISY provides the detail and flexibility required to reflect the extensive, accurate customer and segment detail required in today's energy markets.
Relying on "prototype and typical" is similar to analyzing a "typical" family which consists of two adults and 0.6 children - it may reflect an average but it may also provide misleading results when used to understand customers and markets, to develop programs to fit the needs of individual customer segments, to evaluate the profitability of serving these customers or to evaluate markets for new technologies.
Sources of load profile data which rely on fixed customer segments (e.g. large, medium and small offices) typically develop hourly load data with engineering models (e.g., DOE2, OpenEI NREL) of a single "prototype" building. The aggregate nature of these representations misses the variation that exists among individual buildings within these segments, hiding important market information. For instance, a particular electric rate structure may provide a competitive profit based on an entire segment's single prototype load profile; however, analysis of subsets of the segment (which can be performed with MAISY but not with the "prototype or typical" load profile approach) may reveal significant diversity in profit levels across customer sub-segments such that some customers are provided power at a loss while profit margins on other customers result in cream-skimming targets for other suppliers.
Similarly, evaluating markets for new technologies or potentials for energy efficiency initiatives requires consideration of the full range of customers within a market or utility service area. The average load profile may reflect little potential hiding the fact that a significant portion of the market with different load characteristics provides great potential. For more information on this topic see Avoiding "Prototype" and Average Load Data Aggregation Errors .
ZIP Area Utility Customer Databases - ZIP-level averages
ZIP Databases With Solar Data - ZIP data plus PV data and future PV installations forecasts.
Storage/PV Marketing Databases & Analytics - Support software and analytics developed specifically for storage/PV applications
Individual Customer Weather Risk Analysis - Risk assessment for REPs/ESCOs
New California Residential Database - A new CA Database with information on 400,000 California utility customers
OTHER MAISY LINKS
Products and Services
Smart Grid Analysis
Energy Budgets at Risk