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OpenDSM (formerly OpenEEmeter) is an open source library used to measure the impacts of demand-side programs by using historical data to fit models and then create predictions (counterfactuals) to compare to post-intervention, observed energy usage.

Energy efficiency programs have traditionally focused on addressing long-term load growth and reducing customer energy bills rather than serving as reliable grid resources. However, as utilities work to decarbonize power generation, buildings, and transportation, demand-side programs (e.g. energy efficiency, load shifting, electrification, and demand response programs) must evolve into dependable, scalable grid assets. Ultimately, decarbonizing the power grid will require both supply and demand-side solutions. While supply-side production is easily quantified, measuring the impacts of demand-side programs has historically been challenging due to inconsistent and opaque measurement methodologies.

OpenDSM solves these problems with accurate, efficient, and transparent models designed to measure demand-side program impacts. OpenDSM gives all stakeholders full visibility and confidence in the results.

OpenDSM hosts a suite of modules that work together through a clear and consistent pipeline:

  • EEweather pulls weather station data critical for building models.
  • EEmeter creates long-term, building-level energy consumption models using billing, daily, or hourly resolution data. 
    • EEmeter is often used to measure the load impact of energy efficiency, load shifting and other programs or factors that cause an ongoing change to energy consumption.
  • DRmeter (Demand Response) creates short-term, building-level models with hourly resolution data.
    • DRmeter is commonly used to measure demand response programs
  • GRIDmeter uses data from non-participating customers to remove model errors in energy efficiency and demand response measurements. Errors can be due to improperly applying a linear function to a non-linear response or complex and dynamic external factors such as natural disasters, economic shifts, and public health events that would otherwise skew results.

Since 2016, the OpenDSM has been used to measure hundreds of energy efficiency, load shifting, electrification, and demand response programs. It has even been used to measure the variable impact of the COVID-19 pandemic on diverse building stock and different business sectors.

OpenDSM evolved from OpenEEmeter, which itself is a product of a decade of expert collaboration and open working groups through the CalTRACK specifications. Recurve originally developed OpenEEmeter and contributed it to LF Energy in 2019.

OpenDSM Videos

LF Energy OpenEEmeter measures the energy impacts of demand-side interventions in buildings. OpenEEmeter 4.0 provides enhanced performance of the daily model with dramatically reduced seasonal and weekend/weekday bias, along with increased computational efficiency. This webinar explores how OpenEEMeter: -Reduces seasonal bias in the daily model by 84% -Reduces weekend/weekday bias in the daily model by 95% -Runs up to 100x faster with monthly data, and 2 - 10x faster with daily data -Along with this release, the OpenEEMeter community is also publishing a detailed 4.0 model specification and results of thorough testing conducted across residential and commercial sectors and gas and electric fuels. Speakers: -Adam Scheer, Vice President of Applied Data Science, Recurve -Travis Sikes, Data Science Manager, Recurve -Jason Chulock, Lead Engineer, Recurve 1:1:56

LF Energy OpenEEmeter measures the energy impacts of demand-side interventions in buildings. OpenEEmeter 4.0 provides enhanced performance of the daily model with dramatically reduced seasonal and weekend/weekday bias, along with increased computational efficiency.

This webinar explores how OpenEEMeter:
-Reduces seasonal bias in the daily model by 84%
-Reduces weekend/weekday bias in the daily model by 95%
-Runs up to 100x faster with monthly data, and 2 - 10x faster with daily data
-Along with this release, the OpenEEMeter community is also publishing a detailed 4.0 model specification and results of thorough testing conducted across residential and commercial sectors and gas and electric fuels.

Speakers:
-Adam Scheer, Vice President of Applied Data Science, Recurve
-Travis Sikes, Data Science Manager, Recurve
-Jason Chulock, Lead Engineer, Recurve

YouTube Video UExLeUZmMUo5WGtwdXZlREVTT0hRQWNtVm1QMV9MUXZabC41NkI0NEY2RDEwNTU3Q0M2

Webinar: Unveiling OpenEEmeter 4.0

March 12, 2024 7:51 pm

26:22

YouTube Video UExLeUZmMUo5WGtwdXZlREVTT0hRQWNtVm1QMV9MUXZabC4yODlGNEE0NkRGMEEzMEQy

Impacts at the Meter How the OpenEEmeter is Being Used - Phil Ngo

December 3, 2020 10:37 pm

Phil Ngo describes the details of OpenEEmeter, an open source project within LF Energy. 45:42

Phil Ngo describes the details of OpenEEmeter, an open source project within LF Energy.

YouTube Video UExLeUZmMUo5WGtwdXZlREVTT0hRQWNtVm1QMV9MUXZabC4wMTcyMDhGQUE4NTIzM0Y5

LF Energy Webinar Series OpenEEmeter

July 9, 2019 1:16 am

LF Energy’s OpenDSM project (formerly OpenEEmeter) has recently approved and released a new model for measuring the energy impacts of demand-side interventions using hourly utility meter data. Developed via a collaborative effort of the OpenDSM working group, the new model replaces the previous hourly model (released with OpenEEmeter 3.0 and reflecting the CalTRACK 2.0 methodology) with an entirely new, modern, flexible, and extensible modeling framework. These improvements enable reliable application across a wide range of modeling contexts while avoiding many of the pitfalls that are commonly encountered with general-purpose models. The new OpenDSM hourly model has a number of advantages over the previous version, including reduced overfitting, improved handling of outliers, increased flexibility, a more developer-friendly API, and computing times that are faster by a factor of 4-5. Most notably, the model opens the door to improved reliability for meters with solar PV generation. While the prior hourly model performed well for solar meters when cloudiness was consistent from year to year, annual variability in cloud cover created the potential for bias in measured savings. As the subset of meters with solar PV continued to grow, this bias represented a growing source of risk for demand-side portfolios. By introducing the option to include solar irradiance as a predictive variable, the new OpenDSM hourly model drastically reduces the risk of measurement bias for solar meters. In this webinar, we will introduce the new OpenDSM hourly model, describe how it functions, and summarize key improvements. We will also detail the model’s performance on the 33,000 meter sample on which it was developed and tested. Speakers: Travis Sikes, Recurve Adam Scheer, Recurve 58:33

LF Energy’s OpenDSM project (formerly OpenEEmeter) has recently approved and released a new model for measuring the energy impacts of demand-side interventions using hourly utility meter data. Developed via a collaborative effort of the OpenDSM working group, the new model replaces the previous hourly model (released with OpenEEmeter 3.0 and reflecting the CalTRACK 2.0 methodology) with an entirely new, modern, flexible, and extensible modeling framework. These improvements enable reliable application across a wide range of modeling contexts while avoiding many of the pitfalls that are commonly encountered with general-purpose models.

The new OpenDSM hourly model has a number of advantages over the previous version, including reduced overfitting, improved handling of outliers, increased flexibility, a more developer-friendly API, and computing times that are faster by a factor of 4-5. Most notably, the model opens the door to improved reliability for meters with solar PV generation. While the prior hourly model performed well for solar meters when cloudiness was consistent from year to year, annual variability in cloud cover created the potential for bias in measured savings. As the subset of meters with solar PV continued to grow, this bias represented a growing source of risk for demand-side portfolios. By introducing the option to include solar irradiance as a predictive variable, the new OpenDSM hourly model drastically reduces the risk of measurement bias for solar meters.

In this webinar, we will introduce the new OpenDSM hourly model, describe how it functions, and summarize key improvements. We will also detail the model’s performance on the 33,000 meter sample on which it was developed and tested.

Speakers:
Travis Sikes, Recurve
Adam Scheer, Recurve

YouTube Video UExLeUZmMUo5WGtwdXZlREVTT0hRQWNtVm1QMV9MUXZabC41MjE1MkI0OTQ2QzJGNzNG

Webinar: Reliably Measuring Demand-Side Interventions in a Solar PV World with OpenDSM

September 26, 2025 8:05 am

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Project Special Interest Group: Data Standards and Tooling

Project Lifecycle Stage: Incubating

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