Portfolio Drift provides a portfolio-wide view of changes in your buildings' performance, helping you to focus attention on high-priority buildings.

To access Portfolio Drift, go to Apps in the side menu at left, then Portfolio Drift.

Portfolio Drift is a "tree map" graph that can be useful for showing how individual buildings contribute to the overall performance of your portfolio, and for tracking how building performance changes—or drifts—over time. The graph displays the quantity of a metric via the size of a cell, and percent change from the previous period via the color of a cell.

In the Portfolio Drift graph, you can configure:

Above: Graph of electricity peak demand this month compared to last month. Large cells denote buildings with higher average peak demand. 'Red' denotes buildings with increased peak demand, while 'green' denotes buildings with decreased peak demand.

Below the graph, several statistics are displayed in a table: the metric this week, the metric last week, percent change, and square feet. Select the 'Trend' button to open the corresponding building in the Trend Analysis app.

Above: Statistics corresponding to the graph.

EUI is expressed in units of kBTU/ft^2, and is computed by assuming that the rate during the selected period is consistent for an entire year. EUI requires building area to be entered on each building's Profile tab, and will be omitted for any building without its area defined.

Managers of large, diverse building portfolios have to overcome a lot of challenges. How do you locate facilities that are drifting from baseline performance and should be investigated further for possible problems, changes in usage patterns, or opportunities for improvement—especially when the age, size, and equipment of the buildings vary widely? Where can you see the greatest return on investment while making progress toward your energy reduction goals?

Portfolio Drift helps answer these questions by highlighting facilities that have deviated from their historical consumption patterns and emphasizing those with the largest impact on your portfolio. Facilities that are most important to focus on in greater detail will be immediately clear: size represents the contribution to portfolio performance and color represents the change in performance.

Changes in consumption patterns are not necessarily a bad thing, and changes can happen for predictable and expected reasons. For example, if your portfolio of buildings included educational facilities, you might expect an increase in consumption as the school year begins after the summer break. A change in all of the facilities in tandem suggests that something on a broader scale has changed for the portfolio, like seasonality of weather or occupancy. The Portfolio Drift app helps you identify typical and non-typical use patterns at a glance.

Once an optimized schedule is established, chances are you want it to stay that way. Portfolio Drift highlights individual buildings with the largest percent change in certain key metrics, over the specified time period. Buildings that have deviated from their historic consumption patterns may warrant further investigation.

Individual buildings that show a noticeable deviation from historic use may warrant further investigation. In Figure 2, Mountain View Office C is showing a 28% increase in consumption today compared to the last 30 days. The portfolio as a whole shows a relatively positive or neutral change in consumption, but Mountain View Office C and the hard-to-see Mountain View Office A (red, at top left) stand out from the pack. This should raise a red flag and lead to a more detailed examination of consumption patterns, changes in occupancy schedules, or possible equipment or building automation failure.

While potential problems are called out clearly in Portfolio Drift, so are indicators of positive change. In Figure 2, Mountain View Lab A is showing an 11% decrease in consumption today compared to consumption over the last 30 days. What is being done differently in this facility? Is the change due to weather or occupancy patterns, or has there been a change in equipment or controls configuration that might be replicable in other buildings?

It’s also valuable to understand how buildings vary in their importance, depending on the metric you consider. In Figure 2, we see that a single facility, Mountain View Office D, accounts for about one-third of the portfolio’s average use. But in Figure 3, we see that, on the basis of load factor, there’s a great deal of parity between facilities, and Mountain View Office D represents less than one-eighth of the portfolio’s performance. This suggests that when load factor of each building is taken into account, building energy use is similar to the rest of the portfolio, and a load factor-shedding strategy will need to consider other dimensions (such as age or primary facility use) to find stand-out opportunities for improvement.

Likewise, Figure 4 shows us the same portfolio’s performance according to baseload. Mountain View Office C shows a significant increase. While Mountain View Office C didn’t stand out when we examined average use, something has changed in this facility or in its circumstances to increase the minimum load. This is an opportunity to check for changes in occupancy (such as new occupancy in previously empty space), equipment, controls settings, or outside factors like weather.