Now that many things in an Internet of Things (IoT) world have sensors that can track and control their energy consumption as well as influence the energy use and monitoring of other things, reams of data about these things has been produced, stored, and culled over. Having access to this much data allows the consumer of the data an extraordinary opportunity to not only control their environment but reduce energy demand and the carbon footprint of the facility(ies) they are working to maintain and operate with many competing interests. This data benefits energy, facility and sustainability professionals along with C-suite decision makers who are looking to manage the bottom line on energy costs.
The challenged comes by at what point is the data too far into the weeds to be consumable, useful and manageable. Knowing what level of data is needed to get the most benefit and to whom it should be directed to is the key to success. Understanding the difference and being able to design technology solutions to meet these expectations is the smart way to reach energy reduction goals and maintain efficiency in real time.
If you look at power usage of plug loads — one of the fastest growing demands in any built environment — as an example, a facility director could look at demand in a few ways. The first way would be to simply look at the monthly utility bill while tracking usage of plug load devices within a given time period to see if the demand rises during curtain times of the year. A college environment would be a good case study in that students are now bringing in multiple devices and simultaneously looking to power/charge them. Additionally, the race to provide technologically rich collaborative educational environments means more plug load for projectors, computers, digital whiteboards and other in-room tech. The facilities department can look at the less granular data provided by the monthly statement to see what peak months may be related to student occupation and to develop a strategy to reduce demand during peak times of occupation. This may be automating systems such as AV solutions that shut down the system after so much time not being used or at night hours or to provide Wake-on-LAN/Sleep-on-LAN strategies for computers not in current use.
This method can result in energy reduction without requiring a heavy level of data being generated constantly over a specified interval. This interval data is another opportunity to reduce the plug load demand in our example. Here facilities can have smart plugs sending power demand data in near real time to a power management system than can load shed from areas with less usage balancing demand to areas of high usage. Additionally, the load shedding could be tied to plug load systems with the ability to shut down ore go into reduced power mode with little human intervention.
The difference between these two methods of energy use information relies on how the data is delivered. In the utility method, costs are determined by a third-party vendor or energy provider getting data from the utility. The pitfall is that often changes to the system are being made after the fact based on the previous month’s bill. Additionally, the facility has little to no control on the utility tariffs, it is difficult to convert demand response and current consumption into real savings on energy costs. Now if the facilities goal is an Energy Star benchmarking, then this may be the most efficient and cost effective way to manage use and costs. The interval data method however, can provide the facility with a greater potential for on-demand savings but at a greater first cost price point as more sensors can equate to more capital dollars being spent up front with a potential long-payback on the investment. A solid look at the value proposition vs. opportunity costs needs to be studied before any energy strategy is decided on.
A good place to start is through an energy benchmarking exercise that establishes a baseline of the current status of what data you are currently using and how is it impacting current energy consumption. The answer may be that you just get a monthly utility bill and try to turn the lights out at night or there is a mountain of sensors that are just sending data but not being leveraged right to be efficient.
This can provide you with an initial energy cost that will let you create an energy budget. Energy budgets can be a bit challenging depending on the method chosen to reduce energy consumption. Using the utility method often results in basing future costs on past performance. This can be a pitfall if an annual inflation rate which includes anticipated utility costs rising, additional (or a planned reduction) square footage, or a foreseeable rise in energy demand due to new plug load technology being rolled out. This creates a lot of variables that are crystal ball best guesses that may get you close but can train wreck you if a curve ball hits such as major failures in equipment that were unplanned maintenance.
Interval budgeting also adds complexity by providing opportunity to have too much monitoring. However, because of the data being close to real time demand response can help realign potential budget shortfalls. This can be leveraged through smart building technology interacting directly with the energy management system (machine-to-machine communication) or give feedback to facilities about equipment that might be causing problems and possibly need repair or replacement.
Either way provides opportunity for measurement and verification which can aid in calculating energy savings by leveraging the data. Quite often the best strategy is to consider a multi-tiered approach by using the monthly utility data in conjunction with the interval data to create a more holistic picture. Deciding which strategy is best for what is being measured and monitored is the challenge that is unique to each individual situation.