OpenEEmeter

OpenEEmeter is an open-source

Python library that implements standardized methods for measuring and quantifying energy efficiency and demand flexibility impacts using meter data (energy analytics).

• Implements open, transparent methods for estimating energy savings and load impacts from meter data (energy analytics).
• Provides baselining and counterfactual modeling for energy consumption to support measurement and verification of efficiency programs (measurement and verification).
• Enables consistent quantification of demand-side resource performance for utilities, program administrators, and regulators (grid planning and operations).
• Supports reproducible analysis workflows for demand flexibility and energy efficiency portfolios (data science and analytics tooling).
• Aligns with LF Energy’s focus on collaborative, open-source tooling for the energy transition ecosystem (open-source energy software).

More About OpenEEmeter

OpenEEmeter is an open-source project under LF Energy that focuses on standardized, transparent methods for calculating energy efficiency and demand flexibility impacts using meter-based data. It is designed for organizations that run or oversee demand-side management programs, including utilities, energy service providers, and public agencies, and need a consistent, reproducible way to estimate savings and load impacts from large volumes of consumption data.

At its core, OpenEEmeter provides algorithms and workflows for baselining and counterfactual modeling (measurement and verification). These methods estimate what a customer’s energy use would have been in the absence of an intervention, such as an efficiency retrofit or a demand response event, and compare it to actual metered usage. This enables program operators to quantify realized savings or load reductions at the customer, project, or portfolio level using standardized techniques and open code.

The project is implemented as a Python library (data science and analytics tooling), which makes it suitable for integration into analytics pipelines, data platforms, and custom applications. It is designed to operate on interval and billing data from advanced metering infrastructure or traditional meters, applying statistical and modeling approaches documented in the project’s materials. Enterprises can incorporate OpenEEmeter into batch or streaming workflows that process large sets of customer accounts, using it as a core calculation engine within broader program management, reporting, or settlement systems.

OpenEEmeter is positioned within LF Energy’s portfolio as a tool for quantifying demand-side resources (grid planning and operations). By enabling consistent calculation of savings and load impacts, it supports use cases such as resource planning, regulatory reporting, performance-based program design, and valuation of demand flexibility. The open methodology allows regulators, implementers, and independent evaluators to review, audit, and reproduce calculations, which can support alignment among stakeholders around program results.

In enterprise environments, OpenEEmeter can be deployed alongside data warehouses, customer information systems, and grid analytics platforms. Its Python-based design allows use in containerized or cloud-native architectures and integration with common data science ecosystems. Within a technical taxonomy, OpenEEmeter fits into meter-based measurement and verification, demand-side resource analytics, and open-source energy data tooling, providing a shared, code-based implementation of methods for meter-derived savings and load impact estimation.