What you should know about energy data
Utility bill data or interval data? Utility-owned smart meters or building-installed meters? Parsing the advantages and disadvantages of various energy data types.
Access to energy data is a foundational enabler for organizations that want to reduce utility costs. Energy data is most useful to energy, facility and sustainability professionals and senior decision-makers also may want to understand how energy impacts the bottom line.
Over the past few years, more utility smart meter installations and cheaper metering hardware have made it easier to capture energy data. But there are a variety energy data types and multiple ways to collect it. This article will explain each and highlight the advantages and disadvantages.
Utility bill data vs. interval data
Utility bill data and interval data are the two primary types of energy use information. Utility bill data comes from the energy provider or a third-party vendor that gets the data directly from the utility. Interval data can come from a variety of sources: the energy provider can provide this data after installing a smart meter; or interval meters can be purchased and installed by the building owners.
While utility bill data is less granular than interval data — monthly totals instead of 15-minute increments — it satisfies some energy management use cases and it can be used to deliver incremental energy cost savings.
Utility bills typically arrive every 30 days and include total energy consumption (in kilowatt-hours, kwh) over the entire billing period, the peak demand value that occurred during this time (in kilowatts, kw), and a few other metrics based on the particular utility.
These bills generally are reliable and include energy cost data. Utilities automatically convert the kilowatts and kilowatt-hours into dollars based on complex cost structures called rate tariffs. The dollar-based information on utility bills is seen as an advantage because energy units are not well understood by non-energy professionals.
For example, if a CFO learns that he or she has wasted a million kilowatt hours in a month, it may mean very little. If this CFO learns that it was a $100,000 loss, the reaction will be far different.
Because utility tariffs are complex and can change every few months, few vendors are able to accurately convert demand and consumption into dollars. But the utility does this for free on the bill. For organizations just beginning to take a deeper look at energy consumption across a building portfolio, monthly bill data may be sufficient to identify some operational improvement opportunities.
Moreover, Energy Star-based benchmarking regulations in some cities just require utility bill data. Conversely, organizations that build energy management programs around utility bill data soon may realize that the lack of granularity prevents them from identifying deep operational and retrofit opportunities. In other words, utility bill data is just a start.
Interval energy data typically is measured every 15 minutes, which provides just over 35,000 data points in a year (compared to just 12 for monthly utility bills). Interval data monitored over just a day can lead to immediate operational changes that may reduce cost on the current utility billing cycle.
There are a few common ways to capture interval energy data. If the building is equipped with a utility-owned smart meter, interval data for up to one year may be provided for free. Utilities often enable customers to download a spreadsheet of interval data and some provide an interface to allow a direct connection with a software solution.
At the same time, not all buildings have utility-owned smart meters. In 2014, the Edison Foundation’s Institute for Electric Innovation reported that 50 million smart meters (PDF) are deployed in the U.S., with 43 million being installed by Investor-Owned Utilities (IOUs) that typically serve major metropolitan areas.
Additionally, data from utility-owned smart meters normally is provided only after a two- to three-day delay. This means that utility-owned smart meters probably cannot be used for real-time decision making, such as avoiding peak demand on particularly high load days.
Finally, each utility may have its own processes and systems to make smart meter data available, so enterprises that have operations across dozens of utilities may be deterred by the need to integrate with dozens of systems. These enterprises also may find data gaps due to certain utilities that have not deployed smart meters. The White House’s Green Button initiative supports a data standard for sharing smart meter data with customers and vendors; it is a good resource to understand smart meter data availability. The Green Button is a step in the right direction, but still needs to see greater adoption by utilities.
... or just do it yourself
If smart meters have not been deployed or the data delay is an issue, enterprises can install their own meters. These meters are functionally similar to smart meters, but can be installed across the whole building (usually called a shadow meter, as it shadows the utility meter), or just specific spaces (by tenant or by floor) or end-uses (HVAC system, lighting). In all cases, granular interval data is captured, providing far more detail than utility bills.
These meters are dropping in price, according to Navigant Research’s 2015 report "Electric Submeters." Due to dropping hardware prices and higher demand, Navigant estimates that the global market for submeters — those installed by building owners and operators and not by utilities — will grow from $950 million in 2015 to $2.5 billion in 2024. While there may be a capital expense to purchase and install these meters, doing so leads to significant cost savings.
The Natural Resources Defense Council (NRDC), in a case study of three commercial buildings in Washington, D.C., found that real-time energy data resulted in a 13.2 percent reduction in consumption (PDF). The General Services Administration, in its guide on creating a business case for submeters, cites a more optimistic Department of Energy report which highlighted interval metering use cases that can reduce energy by up to 45 percent.
New analytical tools from a variety of smart building vendors significantly can amplify the value of the data by identifying actionable insights and significant savings opportunities. For example, automated fault detection and diagnostics (FDD) solutions analyze interval data from meters and trend data from HVAC equipment to identify specific building efficiencies that otherwise would go undetected for long periods. Without such analytical software, the sheer volume of interval and trend data would overwhelm most building operators.
Yet not all building stakeholders will need to install new meters or depend on utility-owned smart meters to capture interval data across a portfolio. In some cases, the meters are already in place, but are not connected to software solution or were installed years ago by the electrical engineer, unbeknownst to the current building operators. Some software vendors can use these existing meters to supply data, while others avoid the time and complexity of integration by installing their own new meters. Both vendor models have merit, and each building will have to decide which option is best.
In either interval data scenario — utility-owned smart meters or building-installed meters — the resulting data will be energy units: consumption (kwh) and demand (kw). The gap in using smart meters or interval meters is the conversion to dollars.
Most utilities that have installed smart meters and provide the interval energy data to customers still only provide dollar equivalents on the utility bill (when the data is aggregated for the entire month). Meters installed by buildings usually do not include software to convert the energy data to dollars. Energy savings still can be achieved without making a conversion to dollars, but many stakeholders are beginning to expect this capability so that they can provide reports to their colleagues and to make cost-benefit decisions on various energy projects. Given the complexity of most utility rate tariffs, converting energy data to cost requires a lot of back office work or a vendor that has built a solution, typically called a rate engine, in addition to maintaining a robust rate database. If this is a need, look for one of the few vendors that invests in a regularly updated rate database and has built a rate engine that can support all the complex calculation scenarios. In some cases, building owners may procure a software solution with a rate engine and contract with another firm to provide the updated rates. Another alternative is to use blended rates, simplifying each rate tariff into an average dollar per kilowatt-hour metric ($/kwh).
Each organization will have to decide which data type is most appropriate based on its needs.