Five examples show how to find the best buildings for battery storage investments, understand where to focus on peak demand management, and quickly analyze a year's worth of data.
If you need to understand the general shape of your building’s load curve, the fastest way to do this is by studying the Load duration and Hours at load interactive Gridium charts available in Performance analysis. This analysis is a powerful summary of an entire year’s worth of energy data–it can help you decided what buildings are best suited for battery storage investments, where the juiciest peak demand management opportunities exist, and if your baseload best practices are working.
The load duration curve displays a cumulative percentage relationship: as you trace the cursor over the line, you will see a demand kW reading and a percentage reading. For example, a 10% at 600 kW reading tells you that 10% of the time, the meter demand is reading 600 kW or greater.
The hours at load graph adds a new layer of detail. How many hours over the past year were spent in a particular 20 kW demand bucket? Trace your cursor over the bars on the graph for an individual hours reading in each kW bucket.
A steep load duration and a single bump
For Building A below, its Load duration curve is really step. 2% of the time is spent above 274 kW. This is mirrored in a long tail in the Hours at load chart, showing 6 hours are spent at ~520 kW, 7 hours at ~360 kW, and 2637 hours at ~140 kW. This building qualifies for consideration of a battery storage project and some further demand management efforts.
An “on-peak” trend with a long tail
This building has an “on-peak” demand cluster around 220 kW, for 366 hours, and a baseload trend between 50 and 60 kW, for 2321 hours total. While is this is good, there remains a long tail of high peak demand, tipped with just 13 hours at 490 kW.
A dual peak trend with three kW demand clusters
This building is particularly interesting–not much of any high tail in the load duration curve, although there are two clusters of “on-peak” behavior, with a third cluster for baseload energy use. 216 hours around 700 kW, another 692 hours around 590 kW, and 1296 hours around 300 kW. A spin through this building’s calendar heatmap would be an easy way to determine if the two separate peak trends are due to seasonality.
A peak cluster and a clear baseload trend
This building is close to optimal. That said, and just like most buildings, it has a peak demand management savings opportunity–1% of the time clocks in above 2920 kW, even though its typical on-peak reading is clustered around 2200 kW. Its baseload performance is fairly steady, clocking 2332 hours at ~700 kW.
Not like the others, thanks to solar
This is a fun one to see–Building E has solar arrays and energy generating cogen assets on site that offset the on-peak data cluster visible in the prior examples. While there are flatter Load duration curves out there, this meter is fairly stable.
Let us know if you have any questions.
Great data visualization to understand a complex issue quickly!
Thanks Darin, that was the goal!