Improving Demand Side Management Program Credibility with Data Loggers
A conversation with Mark Stetz, P.E., CMVP, CRM founder of Stetz Consulting
In any Demand Side Management (DSM) or energy-efficiency project, the first step is identifying potential efficiency measures and estimating what level of savings might exist. These expected savings are often based on how we assume the building operates. But utilities don’t pay incentives based on expectations; they pay incentives based on results.
For example, a company might estimate lighting energy savings of $2,500 per year based on expected operating hours. The available savings may be $6,000 per year based on actual operating hours. The difference between expected and actual hours may be attributed to factors such as lights burning for nighttime cleaning crews, changes to the operating schedule based on business activity, or a lack of understanding of how the building operates.
When utilities offer programs, they are offering incentives for energy efficiency, not for new equipment. They want to verify the savings claims. The utilities are making investments in their customer’s facilities and need to confirm that the investments are sound.
Portable data loggers are one of the many tools we use in DSM projects to understand building energy performance and to substantiate efficiency claims. As energy-efficiency and measurement & verification specialists, we go into facilities, deploy data loggers on selected equipment, and make calculations based on the data collected. This information is used to evaluate project performance and indicate what incentives the customer is eligible to receive.
In the same way that utilities verify customer claims, state governments oversee utility company programs to ensure that their program claims are valid. This is necessary because most utility incentive programs are funded from ratepayer dollars. Data loggers provide DSM program credibility – and they do it in an independent and unbiased way.
In one project, we were looking at a hotel that had installed thermostats with occupancy sensors in a portion of its rooms to reduce operating hours of air conditioning and heat pumps. We were hired to measure the efficiency improvements and prove the reduced runtimes.
Our first step was to develop a monitoring plan using data loggers to measure room temperatures and AC system compressor runtimes. We deployed the loggers throughout the hotel, collected data for two weeks, and then offloaded and analyzed the data.
Ultimately, we discovered that there were no significant operational differences between the hotel rooms equipped with the new thermostats and occupancy sensors, and the standard rooms they were being compared to. The thermostats and occupancy sensors were expected to reduce energy use, but in this case we could not find any evidence of savings. This, in turn, suggested that the new thermostats had not been properly commissioned – the timeouts and sensitivity of the thermostats had been overridden – and even though the absence of any energy savings was not what the customer wanted to hear, it was information they could act upon. Without this information, both the customer and the utility would have assumed that the new controls were saving energy simply because they were expected to.
When evaluating air-conditioning systems, we often look at rooftop unit upgrades. The questions we ask include what is the air conditioning load, and how does it relate to outside temperature? In these cases, we use data loggers to track power consumption of the packaged units as a function of temperature. This gives us the relationship between temperature and energy use and allows us to make reliable savings estimates. From this information, we can usually project the energy use and savings for an entire year based on only a few weeks of observation.
Retro-commissioning of a building is often part of a utility DSM program. This is the practice of tuning up a building’s operating systems so they function as intended. While many facilities have energy management systems (EMSs) to control the heating and cooling equipment, their sensors and actuators often fall out of calibration over time. Therefore, you cannot use the EMS to verify itself; you need an independent method to verify the accuracy and operation of the EMS system.
In our retro-commissioning work, we often look at problems with economizers. For example, if damper actuators are open when they should be closed, the building may be bringing in 100% outside air when it’s 90 degrees outside. This unnecessarily increases cooling loads and energy use. We typically deploy temperature loggers, such as Onset HOBO U12 loggers, to measure outside air, return air, and mixed air temperatures. These loggers sample at 15-minute intervals for one or two weeks. We use this data to calculate economizer performance by looking at the ratios between temperature differences. This is a relatively simple way of observing economizer behavior and diagnosing control or actuator problems.
While building energy simulation models may improve building design, the information data loggers collect can help improve building operation. In these troubled economic times, building managers and operators are taking a much closer look at how their buildings are performing, and are investigating HVAC performance, supply and return air temperatures, light usage patterns, and motor and pump runtimes. To improve building performance and reduce operating costs, one needs to identify the difference between how a building is expected to operate and how it actually operates. Data loggers provide the hard evidence to make informed decisions.