|MAISY Agent-Based Energy Use Models
MAISY Agent-Based End-Use Forecasting Models Are Currently Being Applied to Address Three Related Issues
- Annual Mid to Long-Term Energy Use Forecasts
- Electric, natural gas and fuel oil forecasts for 30 years
- Impacts of building and equipment standards
- Alternative forecasts based on fuel price assumptions and economic growth
- Utility and state-level forecasts
- Smart-grid and Renewable Cost/Benefit Energy and Hourly Load Forecasts and Analysis
- Programmable communicating thermostats
- Load control
- Customer information programs
- Pricing programs (TOU, peak-time rebate, etc.)
- Distributed Energy Resources including solar, combined heat and power, cool storage, etc.
- Utility and state-level forecasts
- New Technology Market Penetration and Cost/Benefit Analysis
- Renewable energy technologies
- Load control/demand response technologies
- Customer-focused conservation/demand response programs
- Annual Energy Forecast: December 2021 Indiana State Utility Forecasting Group (SUFG) application of CEDMS and REDMS for
the Indiana Public Service Commission. Five separate investor-owned
utility models are applied for each residential and commercial sector.
Click here to view SUFG's report documenting analysis and evaluation of the models (Chapters 5 and 6).
Quoting from the report:
"SUFG chose REDMS as the primary residential sector energy projection model for three reasons. First, the SUFG econometric model divides customers into two distinct classes depending upon the space heating fuel employed: electricity and other fuels. Over time the distinction between electric space heating and natural gas (or liquefied petroleum gas) space heating has blurred due to the emergence and acceptance of hybrid systems. Second, at least one major Indiana utility no longer offers a specific electric rate schedule to new customers that choose to use electricity for space heating. Also, at least one additional Indiana utility offers a restricted electric space heating rate which is dependent upon equipment efficiency criteria. Third, federal law mandated lighting efficiency standards which SUFG felt were best modeled in a direct end-use context. The standards called for a 30 percent improvement in lighting efficiency beginning in 2012 with a phased in efficiency improvement of 60 percent by 2020. Econometric methods work reasonably well to capture trends in efficiency over time, but the lighting standards were more aggressive than historical equipment standards in both the level and timing of the mandated efficiency improvements. For this reason, SUFG did not feel comfortable relying on the traditional econometric energy model and chose the direct end-use modeling approach."
"SUFG ... made CEDMS its primary commercial sector forecasting model for several reasons. First, based on experience with the model over several years, SUFG is confident it provides realistic energy projections under a wide range of assumptions.
- Annual Energy Forecasts and Hourly Electric Load Impacts of smart grid technologies and programs: Click here to access "Improving energy efficiency and smart grid program analysis with agent-based end-use forecasting models" published in Energy Journal .
- New Technology Market Penetration and Cost/Benefit Analysis: Click here to access "Are US utility standby rates inhibiting diffusion of customer-owned generating systems?" published in Energy Journal .
- Develop a statistically representative sample of residential and commercial utility customers from utility data or from the appropriate MAISY Utility Customer Energy Use and Hourly Loads Database. Relevant information on each sample customer pyschographic and firmographic (e.g. business type, floor space, demographics, income, etc.) is stored in each customer record.
- Update each customer record in each year of the forecast to reflect changes in customer pyschographic and firmographic characteristics. Include new customer records to reflect new construction activity and remove customer records to reflect demolitions. Reflect equipment aging and replacement and new equipment efficiency and fuel choices, behavioral responses to price changes, DSM and DR programs and incentives and other factors that impact equipment holdings and energy use.
- Apply customer weights to extend sample customer results to reflect all residential and commercial customers in the service area. Comile results for customer segments determined by policy and evalution needs including dwelling unit type income, demographics, dwelling unit age and size and other residential customer characeristics, business type, floor space, and other commercial customer characters.
- Some customers are removed from the sample reflecting the demolition of buildings and customers who leave the service area.
- New customers are added to the sample to reflect service area growth in the customer population.
- Each customer's equipment holdings, building thermal characteristics and equipment operation are modified, as required, to reflect end-use equipment replacement, new equipment purchase, building shell upgrades and price-induced changes in equipment operation.
- Each customer's day type and /or 8760 hourly loads are determined by summing across end-uses where end-use loads reflect changes identified in the previous item.
- A robust sample of customers is used in the models to support analysis and forecasts at any level of detail from technology-specific to total system results. Customer samples are stratified by psychographic and firmographic variables (including building vintage) as well as by rate class and climate zone. Sample weights associated with each sample customer will, when applied to customer characteristics, provide accurate estimates of number of customers, customer building, equipment and operating characteristics, energy use, and 8760 hourly electric loads.
- Utility service area electricity and natural gas forecasts including peak demand and hourly loads
- Utility service area cost/benefit analysis of alternative DSM, energy efficiency, smart grid and demand response programs
- Individual DSM program design
- Detailed rate structure analysis
- DG and combined heat and power market penetration and impact analysis
- Penetration and energy use impact of of individual DSM and energy efficiency programs
- DSM and energy efficiency technology market penetration potential analysis
- An easy-to-use Microsoft Excel software container (all input and output are customized for each client and provided in a comprehensive Excel Workbook)
- A simplified end-use structure reflecting needs and resources of energy forecasters and analysts in today's energy markets
- Parameters and characteristics developed from the MAISY utility customer databases reflecting more than seven million customers in the US and Canada
- Econometric and engineering relationships in a single comprehensive modeling framework
- Energy use forecasts based on behavioral responses to price signals and changes in equipment and building shell efficiencies
- Day-type and 8760 hourly loads, annual and monthly electricity, natural gas and oil use by end use and markets segment
- Detailed utility efficiency, demand response and smart rid programs and state/province incentives
- Modeling methodologies and databases of equipment, DSM and customers developed in more than thirty years of applications.
- Input, output and analysis characteristics customized to individual client needs.
- Use a statistically-representative sample of customers in a transparent way to determine energy use at any desired level of aggregation
- Apply technology and customer-detailed analysis of building and energy-using systems in a process that simulates choices actually made by individual customers. This process also avoids "double counting" problems common with other approaches
- Explicitly represents technology-detailed impacts of DSM , demand response and efficiency program measures
- Use stratified customer samples to support the evaluation of user-specified customer segments and to accomplish vintaging of buildings, equipment and program impacts
- Incorporate modeling and customer analysis methodologies developed in DSM, demand response, efficiency program, integrated resource planning, forecasting, market and technology analysis applications over the last twenty years
- Apply a scalable structure which can easily be modified or extended to incorporate new analysis requirements
- Provide an extension of widely-accepted analysis methodologies used by electric and gas utilities, states, regional organizations and federal government and other organizations
- Provide customized applications to meet specific needs of clients
- Take advantage of utility customer data available in the widely-used MAISY utility customer databases
- Provide more targeted information at less cost than alternative approaches
- Have been used in regulatory hearings and filings throughout the US and Canada
MAISY Agent-Based Forecasting Models
Jackson Associates (JA) has provided energy and hourly load forecasting models and forecasting services for electric utilities, state and federal agencies since 1982. Our modeling methodologies have advanced from econometric models to aggregate end-use models to state-of-the art agent based end-use models that provide more accurate and more granular modeling including incorporation of specific building and appliance efficiency standards.
JA models have been applied to provide short-term, mid-term and long-term energy, revenue and hourly load forecasts as well as analysis of energy efficiency, DSM, demand response, smart grid technologies, distributed generation technologies, customer acquisition and other energy-related analysis.
Jackson Associates can help meet your forecasting needs. Contact us to discuss modeling and forecasting options.
JA Agent-Based End-Use Models provide the most comprehensive and flexible energy modeling available. The agent-based microsimulation structure in current JA models combines end-use and technology detail available with traditional end-use models along with agent-based customer detail and behavioral responses so important in forecasting energy-energy efficiency and demand response program design and impacts. The remainder of this page describes the MAISY End-Use Forecasting System
Three references are provided to illustrate different applications of the JA end-use models.
JA MAISY End-Use Forecasting Model Summary
CEDMS (Commercial) / REDMS (Residential) End Use Model Summary
The agent-based microsimulation modeling process applied in CEDMS and REDMS models is intuitively appealing. Model development and application includes the following steps:
Agent-based microsimulation modeling is comparable to taking a survey of utility customers now and each year in the forecast period. Rather than waiting for future years to arrive, the model forecasts changes to each customer in the sample including new customers to develop estimates of future energy use, hourly loads and peak demand based on statistically-determined relationships. These relationships provide the capability to evaluate utility and government energy policies including standards, incentives, price responses and other factors.
JA MAISY End-Use Forecasting Model Detail
The concept of microsimulation is intuitively appealing in that results are based on technology-level analysis performed on individual customers comprising a statistically representative sample of all utility customers. Analysis results are statistically expanded to the population of customers to develop an accurate estimate of energy use and hourly loads. This process is similar to that used in surveys where responses from a sample of customers are used to develop reliable estimates of responses that would be provided if all customers were surveyed.
The sample of individual utility customers is developed from utility data or drawn from the MAISY Utility Customer Databases which have been developed with more than seven million residential and commercial utility customers throughout the US and Canada.
In each year of a model forecast period:
When run without any DSM, efficiency programs, demand response, technology product or other inputs, the models provide baseline energy use forecasts reflecting market-driven changes in equipment efficiency, equipment and fuel choice and equipment utilization.
JA models are applied to evaluate traditional utility DSM, demand response and efficiency program costs and benefits. Replacing existing equipment or building thermal measures with those that are technically feasible reflects technical potential. An economic potential run makes changes in individual customer equipment and building characteristics that are economically justified, and so on. Traditional cost/benefit tests and analysis at societal, utility and customer level are also provided as standard model outputs.
JA models provide energy use forecasts at detailed end-use, building and sector level. The models can also provide hourly load forecasts that range from peak demand to full 8,760 hourly loads. Additional energy, building and equipment detail (e.g., installations of small combined heat and power systems) can also be provided.
A schematic of the JA modeling process is shown below:
CEDMS and REDMS Modeling Schematic
JA end-use models have been applied in the following application areas:
Jackson Associates' agent-based microsimulation models determine technology purchase decisions for a statistically representative sample of residential, or commercial customers for any geographic area. Sample customers are drawn from the MAISY Utility Customer Databases. Information on building, equipment, operating hours, end-use energy use (including 8760 hourly end-use electric and thermal loads) and other data are available for each customer along with appropriate psychographic data (e.g., income, demographics) and firmographic data (e.g., business type detail, number of employees). Current year technology purchases are estimated for each sample utility customer, total purchases in the utility service area (or state) is calculated by applying weights to each of the sample customers and summing across all customers in the sample.
Energy, hourly load and equipment forecasts for future years are provided by updating the sample of customers for the first forecast year. A sample of new utility customers is added to the process to reflect new construction; customer weights in the existing sample are adjusted to reflect demolitions of existing buildings. The new customer sample reflects recently new customers drawn from the MAISY databases. The same process is completed for each year in the forecast period.
Energy use and equipment characteristics of each sample customer can change in each forecast year. Existing equipment wears out and is replaced. The efficiency and energy use for these end uses is changed to reflect new equipment. For new construction, efficiency and energy use and fuel choices are incorporated in individual sample customer records. Energy price changes cause changes in utilization of most end-use equipment (e.g., increasing natural gas prices result in thermostat changes). Utilization and fuel choices are modeled with econometrically derived parameters and efficiency changes are modeled with econometrically derived parameters and/or with information on efficiency possibilities and appliance and building standards.
MAISY agent-based microsimulation models begin with a comprehensive, basic characterization of energy use at the individual customer level. This comprehensive representation allows the models to reflect customer choices of new energy technologies, participation in incentive programs, purchase impacts of alternative equipment design and characteristics and responses to various marketing programs.
The genesis of the MAISY End-Use Forecasting System was at Oak Ridge National Laboratory where the first commercial sector end use model was developed by Jerry Jackson, now the president of Jackson Associates.
The model was used by the US Department of Energy and other federal agencies in energy forecasting and conservation analysis in support of the first National Energy Plan. This model, which for the first time, integrated engineering and econometric analysis in a single consistent methodology, served as the basis for a variety of end-use models including the California Energy Commission end use models.
While head of the Applied Research Division at Georgia Institute of Technology, Dr. Jackson and his team extended the model and provided the initial version of the COMMEND model to EPRI for distribution to its member utilities.
Jackson Associates (JA) was established in 1982 to provide proprietary commercial and residential end-use models, CEDMS and REDMS. Since 1982, JA has extended end-use modeling methodologies and customer database development to address a variety of energy, hourly loads, conservation, efficiency, DSM, demand response, smart grid, new energy technology, market analysis, and new product development issues. Current JA models employ the latest agent-based modeling techniques to more accurately reflect the impact of utility and other efficiency-related programs on technology choice and energy use.
End-use modeling is sometimes referred to as "bottom-up" modeling reflecting the fact that energy forecasts are developed from the sum of detailed components. For instance, residential energy use is modeled as the sum of energy use in end uses such as space heating, water heating, air conditioning, and other end uses for single family, multifamily, and mobile homes. This explicit representation of the basic determinates of energy use in each demand sector provides forecasts based on verifiable inputs and also supports the direct representation of conservation and demand response programs, building and equipment standards, new technologies and other important factors.
End-use models are the appropriate modeling methodology for applications that must reflect utility, state and federal energy efficiency initiatives and other activities that impact energy use. The end use modeling methodology is also applied in the US Department of Energy's NEMS model that is used to generate the US Department of Energy's Annual Energy Outlook forecast to the year 2025.
In the mid-1990s interest in end-use modeling came to a screeching halt. Competitive electric markets appeared likely in nearly all states, and regulatory and utility accommodations postponed rate cases for years. JA quickly found a new market for its energy data, information and modeling expertise. JA developed and provided the only commercially available utility customer databases based on statistically representative information from a sample of utility customers. Both state and utility databases are provided in the MAISY utility customer databases. This information on utility customer energy use and hourly loads took on added value as electricity providers considered new markets. In addition, technology companies including United Technologies, Ingersoll Rand, Toyota, Aisen, Ice Energy, Bloom, Sungevity, Sharp and others have utilized this data and JA modeling support for market analysis and product development.
When attention returned to utility and state energy efficiency programs, demand response and other DSM programs, JA incorporated the vast resources of the MAISY databases in its end-use modeling process. More than seven million US and Canadian utility customer records now support the JA residential and commercial end-use models.
JA End-Use Forecasting Model Characteristics Summary
End-use modeling practices have changed considerably since their development and introduction by Dr. Jackson more than thirty years ago. JA's state-of-the-art end-use Forecasting models include the following characteristics:
Customized Applications Provide Flexibility
Jackson Associates works with clients to identify specific forecasting and analysis needs and provides customized models to meet these needs.
JA End Use Model Advantages
JA residential and commercial agent-based microsimulation forecasting models provide a number of advantages compared to other approaches and other end-use models. The models: