The SEEM program is designed to model small scale residential building energy use. The program consists of an hourly thermal simulation and an hourly moisture (humidity) simulation that interacts with duct specifications, equipment, and weather parameters to calculate the annual heating and cooling energy requirements of the home. It is based on algorithms consistent with current American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE), American Heating and Refrigeration Institute (AHRI), and International Organization for Standards (ISO) calculation standards.

In order for the SEEM model to be used in RTF measure assessments, it must be calibrated to baseline and efficient-case consumption. Calibration for single family, multi-family, and manufactured homes are separate endeavors that utilize metered data from a sample of homes in the NW to estimate energy consumption. The most recent SEEM calibration files can be found below, along with supporting documentation necessary to run the SEEM model.

SEEM Supporting Files

SEEM Calibration Documentation

The calibration grounds SEEM heating energy estimates in empirical utility billing data with the goal of improving SEEM-based energy savings estimates for shell- and HVAC-related measures in detached single-family and manufactured homes. 

In the documentation, we use the term primary calibration for the calibration work products that were based on data from RBSA I (2011) and RBSA II (2016). These products took the form of post-processing adjustments that roughly align SEEM heating energy estimates with billing data estimates for RBSA homes. A fundamental limitation of the primary calibration is that a cross-sectional data set like the RBSA provides comparisons of different homes with different characteristics and different occupants, but it is not ideal for estimating energy changes caused by efficiency interventions that change characteristics within individual homes. Because of this limitation, the RTF approved the primary calibration for each building type and heating type with the understanding that approval only means that, for the RBSA data set, the identified adjustments align SEEM output with billing data as well as it is practical to do with relatively simple and transparent methods. This clarification was intended to recognize the limitations of the primary calibration and to emphasize that measure updates should weigh all available information sources, including the primary calibration, available program evaluation data, and judgment.  

After the primary calibration was approved, the RTF developed measure-specific calibrations on a case-by-case basis. In some cases, the RTF-approved measure analysis uses the primary calibration for the relevant building type with little or no modification. In other cases, the analysis may take an alternative approach based on available data sources and RTF judgment.  

Additional background on the primary calibration and measure-specific calibrations, including links to important meeting dates and approved work products, is provided in this document:  SEEM_Calibration_Overview.doc

Measure calibration details, including relationships with the primary calibration and calculation examples that illustrate how the calibration is applied in each measure workbook, are provided in this workbook:   CalibrationFactorSummary.xlsb.


SEEM Archive files 


SEEM, written at Ecotope, was developed by and for the Council and NEEA. SEEM is used extensively in the Northwest to estimate conservation measure savings for regional energy utility policy planners. It is the simulation engine used to provide heating and cooling energy savings estimates for the residential sector in the Council's Power Plan, for the Performance Tested Comfort System (PTCS) incentive program, the Northwest EnergyStar for Homes program, as well as numerous other utility program offerings. SEEM is also used to support state building energy code revisions including the Washington, Oregon, Idaho and Montana state energy codes.

To create a simulation, SEEM takes a number of input parameters including those for occupancy, equipment, ducts, envelope, foundation, and infiltration. The input structure makes the program flexible and allows it to model a diverse set of building construction types such as split-level, heated basements, slab-on-grade, and cantilevered floors. SEEM generates a number of outputs including building UA, heating load, heating equipment input requirements, cooling load, and cooling equipment input requirements.

SEEM offers a number of advantages over other simulation programs. The step-by-step hourly calculations accurately model both air temperature and mean radiant temperature using a state of the art algorithm. Next, heat pumps and air-conditioners are modeled on real performance data from manufactures’ catalogues. SEEM also provides the capability to use multiple control strategies and thermostat setups for the equipment. Further, SEEM closely tracks duct losses to user specified zones (inside, outside, crawl, attic) and accurately models their impacts. Additionally, SEEM contains a comprehensive below-grade heat loss algorithm to model building ground contact through slabs, crawl spaces, and basements. Lastly, weather data for the simulation comes from the widely used Typical Meteorological Year (TMY) datasets.