How the Open Energy Efficiency Meter Works
OPEN STANDARD, OPEN SOURCE, OPEN DATA
Building the Open Energy Efficiency Meter with Open Standards, Source Code, and Data allows implementers, contractors, and utilities in other states to easily adopt the same platform and methods. Transparency is critical to instill confidence in savings and enable markets to manage risk and invest in this important resource. The OpemEEmeter should in many ways serve as the equivalent of a “standard weights and measures” for residential energy efficiency.
While the initial OpenEEmeter pilot leverages best practices from existing EM&V approaches and standards, it is recommended that the calculations behind the EE-Meter be synchronized across States, and that in the future, the EE-Meter calculations should be developed and maintained through a consensus standard setting process such as ANSI. The guidance provided by this team can be used as a draft for this standards development process.
The OpenEEmeter is designed to work with public open source software tools, databases, and XML protocols including:
OPEN EE METERING COMPONENTS
Collect Project Data Project-level information on the homes, installed measures, and gas and electric savings predictions from participating audit software tools will be exported as HP-XML 2.0 files and periodically submitted to the OpenEEmeter system, either through an SFTP server upload, or another secure data exchange method. OpenEEmeter will also have an API endpoint that allows vendors and contractors to submit the HP-XML files directly from their software at runtime.
Collect Usage Data Utilities will conduct the calculation or transfer (either manually via SFTP, automatically via green button connect, or through a third-party data intermediaries) monthly gas and electric usage for program participants, including 15 months historic data and then on-going data post-retrofit (for a minimum of 18 months). Usage data can either be monthly billing data or smart meter interval data.
Collect Weather Data Hourly temperature data taken from NOAA’s National Climatic Data Center (NCDC) will be pre-processed (with missing values imputed), and normalized using both the TMY3, and in California CZ2010 weather normals. All pre-processed weather data and callable weather normalization functions will be available to vendors through Open EE Meter’s RESTful API.
Data Cleaning, Standardization, and Quality Checks Before computing normalized savings estimates, OpenEEmeter runs a series of data quality checks and outlier analyses on each of the main data inputs to each EE Meter calculation, flagging accounts for specific failures in data quality. Each savings calculation will be screened for reliability based on whether or not it meets criteria for quantifiers such as the number of meter readings, the date range, or upper and lower thresholds for heating degree days and cooling degree days.
Weather Normalized Energy Savings Calculation An automated system performs pre/post retrofit house-level weather normalization using standard methods for fitting energy use to heating/cooling degree days. The balance point temperature for generating the heating/cooling degree days is set by searching over a range of possible balance point temperatures (using an optimization method which scales to hundreds of thousands of accounts). After model selection and estimation, a series of post-estimation checks on regression fit, uncertainty, and ratios of base-to-variable load will ensure that the normalized savings estimates are reasonable and valid. All the specific functions and methods for normalization will be well documented and available through Open EE Meter’s public code and documentation repository. For a home that uses electricity and natural gas, there would be three percentages for electricity and three for gas; some of the values will be zero, like gas cooling.
Realization Rate Calculation For each home, a realization ratio is calculated (realized savings/projected savings) using ConsumptionByEndUse and the SavingsByEndUse from the HPXML file. The savings of record would be calculated by applying the percentage savings by end-use and fuel type to the annual weather normalized end-use consumption by fuel type. Realization rates (average billing analysis savings / average projected savings) for both gas and electricity by software tool, contractor, and implementor are calculated once there are at least 30 homes with usable results. OpenEEmeter will also report 95% confidence intervals for each of the realization rates. The reported realization rates will have a set of minimum criteria, including number of homes, size of projected savings (e.g. >50 therms/yr), standard error size, etc.
Ongoing Analysis Normalized savings, realization rates, and other results will be accumulated and updated over time as more homes participate and more data are collected post-retrofit. Each of these metrics will be recalculated with each new data update to near real-time feedback.
Dashboards and Views to Data A critical component of OpenEEmeter is the views to the data which it will provide to a variety of market actors. OpenEEmeter will have custom views with varying degrees of access for regulators, utilities, implementors, contractors, software vendors, and general market participants. Each of these views will maximize the usefulness of the data to the relevant sector while also ensuring privacy, security, and accuracy. Views (like the sample below) will help each stakeholder understand their progress over time, and how they compare to their peers on the core program metrics.
The MIT License is a free software license originating at the Massachusetts Institute of Technology (MIT). It is a permissive free software license, meaning that it permits reuse within proprietary software provided all copies of the licensed software include a copy of the MIT License terms and the copyright notice. Such proprietary software retains its proprietary nature even though it incorporates software under the MIT License. The license is also GPL-compatible, meaning that the GPL permits combination and redistribution with software that uses the MIT License.
PUBLICLY AVAILABLE CODE
GitHub is a web-based Git repository hosting service, which offers all of the distributed revision control and source code management (SCM) functionality of Git as well as adding its own features. Unlike Git, which is strictly a command-line tool, GitHub provides a web-based graphical interface and desktop as well as mobile integration. It also provides access control and several collaboration features such as wikis, task management, and bug tracking and feature requests for every project.
Python is a widely used general-purpose, high-level programming language. Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java. The language provides constructs intended to enable clear programs on both a small and large scale.
Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library. CPython, the reference implementation of Python, is free and open-source software and has a community-based development model, as do nearly all of its alternative implementations. CPython is managed by the non-profit Python Software Foundation.
OPEN EE ENERGY DATA PLATFORM
The Open EE Energy Data Platform is a cloud-based platform that handles data connection, collection, cleaning, and scalable, secure storage with an API that allows OEE applications, as well as other 3rd-party applications to have access to reliable, structured energy use and project data from a variety of sources.
Connector: Standard database connections for facilitating integration with enterprise systems Collector: Data collection from authorized data sources like Green Button Connect. Resolver: Data quality layer performing quality checks, cleaning, dedup, and entity resolution. Scalable Storage: Built on Apache Cassandra to scale efficiently over large, distributed datasets. Stable API: API allows for applications to securely access to Open EE Energy Data Platform.
Includes housing characteristics, suggested retrofit measures, installed retrofit measures, predicted savings (preferably including measure-level predictions), pre-post test values, subsidy amounts, and contractor information & software vendor information.
HOME PERFORMANCE - XML
Home Performance XML (HPXML) is a data transfer standard for the home performance industry. This repository is where the development of the schemas happens. The HPXML Schema Documentation provides a graphical representation of the schema’s data elements. BPI-2400 STANDARD FOR ENERGY SAVINGS PREDICTION CALIBRATION TO ENERGY USE HISTORY BPI-2400-S-2011: Standardized Qualification of Whole House Energy Savings Estimates Standard specifies a process for the calculation of standardized estimated savings: the difference (delta) between the modeled energy usage before and after an upgrade using approved building energy use simulation software. The process uses actual home energy bills to estimate savings, and provides a set of standardized operating conditions to be used in the final calculation of estimated savings.
BUILDING ENERGY DATA EXCHANGE SPECIFICATION
The Building Energy Data Exchange Specification (BEDES) is a dictionary of terms, definitions, and field formats which was created to help facilitate the exchange of information on building characteristics and energy use. It is intended to be used in tools and activities that help stakeholders make energy investment decisions, track building performance, and implement energy efficient policies and programs.
BEDES is not a software tool, database or schema. It is a dictionary that provides common terms and definitions which different tools, databases and data formats can share.
Monthly billing data must indicate whether the read was actual or estimated. If estimated, it must indicate estimation method. Open EE Meter will also have methods that allow for the use of 15-minute, 30-minute, hourly, and daily interval data from smart meters, which will allow increased accuracy and near real-time estimates.
All electric users have meters that measure how much energy they use. This metered data is used by energy service providers to calculate how much to charge the consumer. Green Button is all about making that data available for other purposes such as planning and analysis.
Standard Green Button data, contains no personally identifiable information, only measured interval usage information. The data representation has been fully reviewed by the cyber-security teams at the National Institute of Standards and Technology from this perspective.
WEATHER DATA Daily average temperatures for all available weather stations with consistent measurements. Additionally Open EE Meter will include 10-year and 30-year temperature normals from CZ2010 or TMY3.