A first look at standardized analysis of building benchmarking data

Building benchmarking laws are proliferating across the country and are powerful policy tools used to drive the carbon footprint of specific jurisdictions down to zero along a set timeline. However, navigating the intricate landscape of these laws can be a daunting task for professionals in energy efficiency and building management. As these laws become increasingly prevalent, understanding their impact on compliance and fine risk is crucial. First, though, let’s review what these laws are and how they are standardized–or not–across the country.

What are building benchmarking laws??

Building benchmarking laws are being used to impose improvement standards on buildings and can also be used to require that tune-ups, retrocommissioning, or other upkeep measures be taken regularly. The laws are different from building energy codes in that they specify what must be done with respect to building energy use going forward and aren’t only focused on new construction building improvements. Below are some example building energy benchmarking laws, as well as some example energy codes. Each of the jurisdictions below requires building energy data to be organized and submitted through the Portfolio Manager developed by ENERGY STAR?, which is a joint program of the U.S. Environmental Protection Agency (EPA) and the Department of Energy (DOE).?

Example Building Energy Benchmarking Laws

1. Aspen, CO: Benchmarking & Disclosure and Building Performance Standards for commercial buildings over 5,000 ft2 and multifamily buildings over 15,000 ft2
2. Chicago, IL: Large buildings may be required to benchmark and report their energy use. This includes commercial, residential, and municipal buildings over 50,000 square feet
3. Montgomery County, MD: Benchmarking laws require building information, including energy data for all fuels used, to be reported annually.
4. California: The Building Energy Benchmarking Program mandates large commercial and multifamily buildings to report energy use annually to the California Energy Commission.

Example Energy Codes

1. International Energy Conservation Code (IECC): Widely adopted code in the U.S. that sets minimum energy efficiency standards for new and renovated buildings.
2. ASHRAE Standard 90.1: An American standard that provides minimum requirements for energy-efficient designs for buildings except for low-rise residential buildings.

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Collecting and submitting data, however, does not improve building performance–unless building owners and operators are accountable for meeting specific performance goals. This has led cities, states, and counties to create standards that buildings must comply with or be fined. These fines typically take specific structures based on ENERGY STAR score, Energy Use Intensity (EUI), weather-normalized or not, or greenhouse gas (GHG) emissions intensity. Some examples of rules that are specific to different jurisdictions are as follows.?

Examples of Jurisdiction-Specific Rules for Building Compliance

1. New York City: Requires energy audits and benchmarking every ten years. Buildings must track and verify their energy consumption using ENERGY STAR’s Portfolio Manager.
2. Montgomery County, MD: Buildings must provide detailed information, including address, building ID, gross floor area, occupancy, space types, and energy data for all fuels used. Data verification is required in the first year of reporting and every three years after that.
3. United States Government: The Federal Building Performance Standard requires federally owned buildings nationwide to cut energy use and electrify equipment and appliances by 2030.

How does building benchmarking data vary across different municipalities?

While these rules are all spelled out in the public domain, the evaluation of their consequences for any one particular building can be complicated. It is even more complicated to preview what the fines and penalties will be for any particular set of buildings, especially across municipalities. We have spent the past few months sorting through the regulations and cleaning jurisdiction-specific datasets and working on a way to unify the data, nationwide. Through our work ingesting and cleaning data across the country, we have found that nearly every city has built a different data structure, and has organized the data around their collection process in a different manner. While this is a complicated process that is marred by data quality issues, it is eased by the fact that all cities are structuring their data in a format that comports with the ENERGY STAR Portfolio Manager.?

A quick look at our data - aggregating multiple building benchmarking datasets

The datasets involved with building benchmarking laws vary significantly, as do the actual laws themselves, the jurisdictions they apply to, and the quality of their benchmarking data. Our dataset contains data from 78,832 distinct properties in 16 cities, and each city has a different square footage in the database. For this article, the amount of floorspace was computed by combining benchmarking data from all years, selecting unique building IDs for each year, and then adding up the floor space represented by those unique building IDs. This results in more floor space being represented than can be counted in any given year. This could potentially be an effect of buildings not consistently reporting for each year, and could potentially be due to building demolition, new construction, etc. As an example of how this can affect the reported floorspace, we show below a histogram of floorspace by year reported for New York City, where there is a total of 3.5 billion square feet of floor space in the most recent year’s data disclosure, yet if we sum up all the data from the most recent reporting year, we gain about 700 million square feet.?

This leads us to believe that there are improvements to be made in how we are tracking building benchmarking data, as well as how it is being reported, cleaned and utilized. Our upcoming blog posts will showcase the results of our data quality analyses when applied to data reported by various cities. Through the evaluation of this data, we will showcase the need for building owners to report good data. Human error can cause significant fines or fees to be levied against buildings.?

The primary metric for most jurisdictions, EUI, also varies significantly across the jurisdictions represented. While most of the results that are shown can be expected, it does highlight the individual challenges associated with each region.? As can be seen from the map above, EUI is higher in Chicago and the Northeast, where cold winter and fall temperatures drive up the heating load required for each building. Many jurisdictions have adopted “weather-normalized” EUI as an insightful additional EUI metric, which is useful for year-to-year comparisons; without normalizing for weather changes, comparing EUI across years or cities is comparable to comparing your cars gas mileage without considering what fraction of your trip was on the highway or cities. Of 16 select cities with energy benchmarking programs, 9 transparently report both EUI metrics (i.e., with and without weather normalization) and 7 cities only report one EUI metric. While weather-normalized EUI is a more useful metric for energy performance analysis, only one of the 7 cities explicitly weather-normalized their EUI metric, whereas the others are presumed not to normalize their EUI metric. We will explore specific EUI nuances of the individual cities in future blog posts.?

How is benchmarking data useful to organizations in electrification and energy efficiency industries?

Individual cities have put up different guides for the players in this ecosystem, of which there are many. Here is a list of players in this space, and ways in which they can use benchmarking data in order to improve their business processes. While this list is a summary, we think that the cities have done a great job putting together playbooks, in particular, the folks at Energize Denver, from which we have drawn inspiration in making this list.?

• Architects: Architects can utilize benchmarking data to design more energy-efficient buildings. By analyzing current energy use trends and standards in similar structures, architects can integrate sustainable design principles, such as optimal building orientation, energy-efficient materials, and renewable energy systems. This data helps architects not only meet but potentially exceed current energy benchmarks, leading to buildings that are both environmentally friendly and cost-effective over their lifespan.
• Building Owners: For building owners, benchmarking data is a valuable asset in identifying areas for improvement in energy efficiency. Understanding how their buildings perform in comparison to similar properties can guide investment decisions in retrofits or upgrades. This data is crucial for planning maintenance schedules, budgeting for energy-saving modifications, and enhancing the overall value and marketability of the property.
• Condominium Associations and Homeowners' Associations (HOAs): Condominium owners and HOAs can use benchmarking data to make informed decisions about energy use and potential improvements in their buildings. This information can support the case for investing in energy-efficient upgrades, which can lead to reduced common area energy costs, improved comfort for residents, and an increase in property values. Additionally, understanding energy performance can aid in better budgeting for energy costs and planning for future upgrades.
• Commercial Tenants: Commercial tenants can leverage benchmarking data to negotiate better lease terms or select energy-efficient buildings that align with their sustainability goals. Understanding the energy performance of a building can influence decisions about where to rent, as energy-efficient buildings can offer lower operating costs and a healthier work environment. This data empowers tenants to make choices that align with their financial and environmental priorities.
• Engineers: Engineers play a critical role in implementing systems and solutions that improve a building's energy use. Benchmarking data provides engineers with insights into how current systems are performing and where improvements can be made. This information is vital for designing high-efficiency HVAC systems, optimizing building automation systems, and implementing renewable energy solutions. Engineers can use this data to ensure that the systems they design and install are not only compliant with current standards but are also future-proof.
• General Contractors: For general contractors, benchmarking data is essential in understanding the energy standards and expectations for new construction or renovation projects. This data informs their approach to building practices, ensuring that construction methods and material selections contribute to the overall energy efficiency of the project. Knowledge of energy benchmarks can also give contractors a competitive edge by enabling them to offer solutions that meet or exceed energy performance goals.
• Property Managers: Property managers can use benchmarking data to manage buildings more efficiently. By understanding the energy consumption patterns and comparing them with similar properties, they can identify areas for improvement, plan for energy-saving retrofits, and engage with tenants about energy conservation practices. This data is also crucial for maintaining compliance with local energy regulations and avoiding potential fines.
• Tenant Brokers: Tenant brokers can use energy benchmarking data to provide clients with valuable insights into the energy efficiency of potential rental spaces. This information can be a deciding factor for businesses that prioritize sustainability and wish to reduce their operational costs. Brokers equipped with this data can better match clients with properties that align with their energy efficiency goals, enhancing client satisfaction and contributing to environmentally responsible leasing practices.
• Software Providers: Software providers can use energy benchmarking laws to factor into automated analysis of building economics for upgrades. There are many benchmarking platforms out in the world, and they can use our data tooling to integrate automated analysis of building benchmarking laws and their consequences.?

An Example of our Automated Analysis

Let’s look at some example data. We chose a Boston building with a pretty good data history for this analysis - five years worth. While we do this, we would like to shout out to the Boston BERDO team. They’re the folks that make the magic happen.?

For illustrative purposes, we will evaluate the Chestnut Hill Park Condominiums on Commonwealth Avenue. This building has excellent data quality records, as referenced by our data quality report card for the building:

This large multifamily building is over a century old and has a floor space of 158,387 square feet. Overall, the dataset is quite good as far as completeness of data is concerned for the city of Boston. Even so, this building has duplicate entries, which we addressed in our data cleaning.

Using the historical data, a range of forecasts can be constructed for the future state of energy use of the building. This is based on the most recent values and the trendline of the energy use from the years of reported data. This forecast can then be combined with the goals for the building type and the fines for not meeting goals and produce an estimate of the fines the building will face over time. The GHG forecast and fines associated are shown in the figure and table below. For the fines, we use two projection methods - the average of our machine learning predictions and “Do Nothing” - leave the building as is.?

The future of building energy benchmarking analysis is automated

This analysis will eventually need to be done for hundreds of thousands of buildings all over the US and should be used to calculate the economic analysis associated with upgrading buildings to meet benchmarking law standards. Automation is key to handling the vast amount of data and jurisdiction-by-jurisdiction bespoke calculations involved in building benchmarking analysis. Utilizing advanced data analytics tools can significantly speed up the process, allowing building owners and managers to quickly assess their compliance status and make informed decisions.?

Our FastBenchmark tool unifies the analysis for building performance standards, is available via API, and can export standardized reports that can be white-labeled for your clients. We work with software developers, renewable energy implementers, engineering firms, and other stakeholders in the industry. If you’d like to book a time to chat, please schedule it here - www.plentiful.ai/meet.?