Breaking the energy efficiency gridlock

There’s a tremendous opportunity to reduce energy consumption in commercial buildings – which currently accounts for more than 40 percent of the nation’s total. According to Pike Research, energy efficiency projects could eventually save $40 billion in annual commercial building energy spending each year; however, at present investment rates, only a small part of that value will be captured.

Why are projects with positive returns going unfunded? Beyond access to financing, a large reason is that market participants – energy service providers and utilities, as well as building owners and investors themselves – rely on a manual process to identify which buildings to target and what energy conservation measures to target them with. This process is slow and expensive, leading to a suboptimal allocation of resources and effort, and in many cases, stakeholders taking no action at all.

A new breed of data analytics software solutions are aiming to break this gridlock.  By using sophisticated algorithms and rapid building modeling to analyze energy consumption data or information about a building’s systems, energy savings opportunities can be evaluated at low cost – with minimal human involvement or a need to install expensive hardware on-site.

Data analytics software for efficiency recommendations focuses on assessing three key areas:

1. Building prioritization:Opportunities to reduce energy consumption can vary greatly by building. In a recent study published by Retroficiency, we found that the top 20 percent of buildings within a portfolio had average savings opportunities of 43 percent, whereas the 20 percent of buildings with the lowest potential to save could only reduce spending by about 5 percent. 
   
   The challenge is identifying which buildings fall into each group. Traditional metrics such as an ENERGY STAR score and utility spending per square foot do not necessarily correlate to savings. Some data analytics solutions can accurately determine true savings potential with a very limited amount of data – such as a year’s worth of energy consumption interval data or a few key pieces of information about the building itself. Determining savings potential upfront in the process helps identify where to focus effort and resources.