One–or multiple–daily temperature response curves can be analyzed or benchmarked, or group weekday and weekend curves together by selecting Open and Closed. Baseline benchmarks are an equal-weighted summary of all historical data.
47% of a typical commercial office building’s energy use is spent on space heating and cooling and ventilation, according to the U.S. Department of Energy’s 2011 Building Energy Data Book (which offers nearly 300 pages of blissful reading for the building nerds among us). It is for this reason–that such a significant chunk of a building’s energy use is put to these weather-related uses–that significant savings and useful insights can be had by analyzing the building’s temperature response curves.
Understanding of building response rates enables a variety of control functions to be optimized: equipment start and stop times, peak-demand shaving, demand response, pre-conditioning, and dynamic set-back (“floating”) of specific zones. Response rates are key inputs into anticipatory (predictive) control actions. In so far as people are sensitive to temperature change rather than absolute temperature, anticipatory control can also improve perceived thermal comfort and occupant satisfaction. – Ziqian Dong et al.
In partnership with the New York Institute of Technology and the City College of New York, Ziqian Dong and her co-authors, of the paper titled “Simplified Characterization of Building Thermal Response Rates,” sought to model a building’s zonal response rates as a way of understanding its temperature response and to better inform proactive HVAC control strategies. The two-year project involved building a custom database, semi-annual data extraction from trend logs of specific BMS data points, and asynchronous statistical analysis.
Snapmeter’s new Performance analysis Temperature Response report performs this sort of analysis at the push of a button (albeit not at the zone-by-zone fidelity of the Ziqian Dong study). The solid lines (as pictured above) reflect the average kW value that can be expected for this building at each temperature level at each time of day. The expected kW value is based on this building’s actual energy use data over the past 12 months, and the calculation prioritizes more recent data over older data. The width of the line–along the x-axis–reflects the temperature range experienced by this building throughout the year. It doesn’t get to 90 degrees at 4:00am in San Francisco.
While it’s possible to show the expected kW value for 200 degrees, that output is not useful. Dotted baseline lines are an equal-weighted average off all of this building’s historical data, with no prioritization on data recency. The vertical line (in this example, at 60.5 degrees) reflects this building’s balance point, which is the point where Snapmeter has deduced the building begins mechanical cooling.
By comparing one of the solid lines to its dotted baseline, you can see how energy use has changed, recently, in this building compared to its longterm trend. If this is a typical office building, where weekends are shutdown, you’d want to see the averaged Open days, or any give weekday individually selected, near the averaged Closed line at night and in the early morning.
As the building’s energy use picks up throughout the course of the day, you can compare it to the averaged Closed days in order to visualize the building’s typical operational load. This chart will also reflect the building’s typical start-up and shutdown times, in general or for each day of the week.
Cooling is indicated when the line slopes up and to the right, while heating is indicated by a line that slopes down and to the right.
Temperature response is one of the many ways to fine-tune your building’s energy use, and as discussed by Ziqian Dong and her co-authors, it can have a direct impact on energy costs as well as occupant satisfaction.
