Use Gridium's weather-adjusted models to spot equipment problems before they get expensive
Recapping our most recent webinar, “Getting the most out of Snapmeter.”
Weather-driven vs. equipment-driven peaks
Your utility charges you not just for the amount of energy you use, but also for the rate of use. Managing your spend is like driving down the highway with a speed gun pointed at you at all times. Whatever top speed you hit during the billing cycle will show up on your bill.
You need to diagnose the underlying drivers of your peak demand to determine an appropriate energy management strategy. Most peaks are weather-dependent, driven by a combination of cooling load and occupancy patterns. To manage such peaks, you need two things: a reliable demand forecast and a peak curtailment strategy. In the best case, your curtailment sequence will be programmed directly into your BMS for ease of deployment.
Some buildings experience equipment-driven demand, in which peaks are determined by individual pieces of large equipment that cause spikes or step functions in the load curve. To detect these kinds of peaks, you need a weather-normalized model of building energy use that helps you separate anomalous behavior from the underlying noise. Remediating such issues usually requires either updating building sequences or a more capital-intensive approach.
Diagnosing operational issues with an energy model
Normally the only way to detect energy management issues is by looking at bill variances. Unfortunately, this approach is both crude and slow. Only large variances are likely to be noticed, and because most buildings perform year-on-year bill comparison, problem detection takes at least a full twelve months. Worst of all, bills give almost no insight into the nature of the variance.
The daily use and demand variances supplied by Snapmeter give a much more fine-grained snapshot of building drift, with a lag measured in days rather than years. Some tips for getting the most out of these variance reports:
- Take variances seriously. Snapmeter only reports variances that reach a threshold for statistical significance. This certainly doesn’t mean that every single variance indicates an underlying operational problem. Building energy use can move for all sorts of reasons, and much of the time such movement will be within the natural operating range of the building. That said, Snapmeter’s energy model does take into account normal operating variability, so large or repeated variance figures do generally indicate a meaningful change in behavior.
- Don’t blame weather. All reported variances are already weather-normalized. Most facility managers reflexively look to weather to explain shifts in building behavior. Snapmeter reports variances only after weather has been taken into account.
- Pay attention to use. Use variances tend to be smaller than demand variances, because the cumulative nature of energy use makes this figure fairly stable from day to day. This stability in turn means that use variances are more likely to be statistically significant even when they are small. Don’t ignore these use variances just because they are small! Because use is cumulative, small changes can be very expensive over time.
- Go to the charts. A variance may be a smoking gun, but you still need to find the body. To diagnose anomalous behavior, look at the load charts, and especially take care to compare unusual days with a baseline to see how the pattern of energy use might have changed.