Monitoring, Analyzing, and Improving the Indoor Scene with Environmental Data LoggersFebruary 1, 2001 By: Hugh J. Flye, Onset Computer Corp., Gregg M. Daly, Onset Computer Corp.
Data loggers are fast becoming the tools of choice among performance contractors, service technicians, and engineers responsible for diagnosing and evaluating interior environments. These inexpensive devices, widespread both in residential and commercial buildings, collect information about heating, ventilation, air conditioning, and refrigeration system performance; energy efficiency and use; indoor air quality; power consumption; and electric light demand. Data loggers can help establish a baseline, determine energy-saving opportunities, recommend system changes, and measure the results.
What Are Data Loggers?
Data loggers are small, stand-alone, battery-powered devices that are equipped with a microprocessor, memory for data storage, and sensors. In addition to a wide range of other applications, they are used to monitor and record measurements of:
- Relative humidity (RH)
- Light levels
- ON/OFF and OPEN/CLOSED state changes
- Trending over extended periods of time
Data loggers with external input capabilities can gather data from probes, transducers, sensors, and gauges. They can interface with PCs or laptops, and operate through software designed to activate the logger and view and analyze the collected information.
Data loggers can be used for untended, around-the-clock monitoring and recording of building and system conditions, providing a more complete and accurate picture of the system's overall performance than is possible with simple spot checks. Problem areas and unexpected condition changes can be captured and stored for future study and evaluation. Because of their small size and low cost, data loggers can be placed in areas where the installation of permanent, hard-wired digital systems would be difficult and expensive.
To understand how data loggers can make the task of gathering information much easier and more efficient, consider case studies of two real-world applications: monitoring electric light consumption and monitoring indoor air quality.
Example 1: Electric Lights
Monitoring lighting use is an important step in evaluating a building's overall demand for electricity. This type of information is easy to gather with on/off status loggers, which incorporate a sensor that responds to the presence or absence of artificial light. When the lights are in use, the loggers record this on state with a time and date stamp; when the lights are turned off, that event is recorded as well. Upon later analysis of the data you can very easily determine the total run time, percentage of time the lights were in use, and peak hours of operation. You can then use this information when making recommendations for lighting systems, performing baseline and post-retrofit studies, and estimating the electrical demand and cost for lighting use.
For the first case study, an on/off data logger was installed in an overhead light fixture in a hotel room, where it unobtrusively monitored the status of the lights over a one-week period (see Figure 1).
Figure 1. This data file plots the actual electric light use and duration, with the ON states represented as 1 and the OFF states as 0.
The collected data were then filtered to show the average light use as an overall daily percentage (see Figure 2).
Figure 2. The average use of a hotel room's ceiling light fixture is plotted as an overall percentage on a number of consecutive days.
An analysis of the data shows that on 5 May 1997 the lights were on ~17% of the day (4.08 hr.); on 7 May 1997, the service period was ~53% (12.72 hr.). With this information in a spreadsheet program, along with light bulb wattage and cost of energy per kilowatt hour, it is a fairly simple matter to determine the total cost of electricity associated with electric lighting:
|bulb wattage ÷ 1000 • cost/kWh = cost/hr.||(1)|
Once you know the cost per hour, you can determine the total cost of operation:
|cost/hr. • total hr. use = total cost||(2)|
For example, assume that the study monitored a 100 W incandescent bulb. Divide this wattage by 1000, and you find that the bulb uses 0.1 kW/hr. to operate. Multiply-ing this by the cost of electricity (e.g., $0.08/kWh), yields a figure of $0.008/hr. to operate this bulb. So, for the 12.72 hr. that the light was in use on 7 May 1997, it consumed ~$0.10. Although $0.10 seems insignificant at first, keep in mind that the hotel probably has hundreds of lights and the rooms are probably occupied much of the year. Returning to Figure 2, if you take an average percentage of the total daily use for seven days (1–7 May), you find that this 100 W bulb was used ~32% of the total time or 53.76 hr. for the week.
|7 days • 24 hr. = 168 hr.
168 • 32% = 53.76 hr.
|53.76 hr./wk ÷ 7 days/wk = 7.68 hr./day||(4)|
Knowing that the average use per day is 7.68 hr., you see that the total average cost per day is ~$0.06:
|7.68 hr. • $0.008/kWh = $0.06||(5)|
The yearly average cost to operate this one light bulb is $21.90:
|$0.06/day • 365 days/year = $21.90 annual cost||(6)|
If you estimate that the use is more or less the same in each of the hotel's 150 rooms, say, then the total cost to operate those 150 100 W bulbs is ~$3285 for the year. If the light fixtures actually have two 100 W bulbs, the total cost doubles to $6570/year. It's easy to see that $0.06 per day quickly can add up to a sizable sum.
Putting the Data to Work
Access to these data, as well as the capability to view, analyze, and print the information, make it easier to recommend energy-saving ideas and changes. Simply replacing the 100 W bulbs with 75 W bulbs, for example, results in a 25% savings in total lighting costs. Switching to energy efficient bulbs will also help reduce power consumption without sacrificing lumens. Although this particular study is based on averages, a more realistic picture of the overall electric light use can be obtained by monitoring every room and light fixture for actual use and cost of operation.
There are other types of state loggers as well. Motor ON/OFF loggers, for example, monitor and record electric motor and fan use. State loggers can also monitor compressors and large pumps. Contact closures, passive relay switches, and power can be monitored for run-time information.
Example 2: Indoor Air Quality
Data loggers are commonly used to monitor the quality of indoor air and the performance of ventilation systems. Some of these units have built-in sensors to measure temperature variations, CO2 levels, and RH. Loggers with external input capabilities can be used with external sensors placed in areas that are hard to reach. There are also hybrids of these two types, with both built-in sensors and connections for external detectors and probes.
You can place external temperature sensors inside air vents and/or attach them to heating coils and pipes. You can compare indoor and outdoor temperature and RH readings. To evaluate a ventilation system, you can monitor CO2 levels when a building is occupied and unoccupied.
In the second case study, a data logger with built-in sensors and external input capabilities was used to monitor and record temperature, RH, and CO2 data in an office space over a period of one week. The office was ~4000 sq. ft. and was occupied by 20 people, Monday through Friday, from 8:00 a.m. to 5:00 p.m. There was one rooftop fan unit to service this part of the building. The building itself was a converted strip mall that housed four other open bay areas of similar size.
The data logger had built-in temperature and RH sensors and an external CO2 monitor with a voltage output signal corresponding to the level of ambient CO2. The logger was programmed to monitor and record data from all three channels (Temperature, RH, and Voltage) at 30 min. intervals, simultaneously. Upon activation, the CO2 monitor and data logger were left untended in the office area to record the data for the length of the study period. At the end of the period, the data logger was recovered and data were offloaded from the logger back into the computer and then plotted.
The temperature data are plotted in Figure 3A. According to the timeline, the logger began recording on 10 May 2000 at 6:00 a.m. and stopped on 17 May 2000 at 6:00 a.m. The graph also shows that temperature conditions stayed roughly the same throughout the seven days. Every day, temperatures rose steadily from 6:00 a.m. until 5:00 p.m., when they began to drop. The thermostat in the office was programmed with these same timer and temperature settings, which this graph helps to verify.
Figure 3A. The office temperature data reveal the expected rise in temperature during office hours and the decrease after hours.
Figure 3B shows the RH data. By analyzing temperature and RH data you can begin to form an overall picture of the indoor climate conditions. You can monitor and verify setback strategies as well, and present solid graphical data to support recommendations for retrofits and system changes.
Figure 3B. Relative humidity in the office environment fluctuates from a low of 31% to a high of 48% over a one-week period.
The CO2 data are plotted in Figure 3C. Note that the information is plotted and scaled in increments of voltage instead of CO2, but you can very easily convert the readings into ppm CO2. According to the manufacturer's specifications for the monitor, 1 mV = 1 ppm CO2. Therefore 1 V = 1000 ppm, 2 V = 2000 ppm, and so on. You can also export the data file to other spreadsheet and graphing programs, where you can use formulas to convert the voltage data into the correct units—psi, cfm, mph, pH, etc.—for the type of sensor being used.
Figure 3C. Carbon dioxide levels are charted in voltage units and converted to parts per milion. As does the office temperature, levels of CO2 rise during the day and subside after office hours.
The graph in Figure 3C reveals interesting information about CO2 levels as well as occupancy in the office. Notice that on Wednesday, 10 May 2000, at 7:00 a.m. the CO2 level begins to rise from an initial level of ~550–575 ppm. The trend continues upward and peaks at 1350 ppm at 5:30 p.m., when people are leaving the building at the end of the workday. Over the course of the evening and early morning hours of 11 May 2000, CO2 levels gradually decrease to ~625 ppm. The pattern continues until 6:00 p.m. Friday, when the level falls to and remains at 600 ppm over the weekend. On Monday, 15 May, the CO2 begins its daily rise once again. If you go back to the temperature data in Figure 3A, you will also see that the building temperatures also were generally lower over the weekend.
Upon completion of this study and analysis of the collected data, the building engineers tried several approaches to bringing CO2 levels down to a more acceptable level. At first, the rooftop fan was run on a full-time basis throughout the work day. Subsequent monitoring showed that this had little, if any, effect on the CO2. Eventually they decided that economizers should be installed to properly control the airflow into and out of the building. Post-retrofit monitoring showed that CO2 levels dropped to an acceptable range.
These studies are only a small sampling of the many ways in which data loggers can be used to monitor and verify energy-saving opportunities. In the light bulb study, engineers were able to use the collected information to make retrofit recommendations and identify ways to save money and energy. After changes were made, post-retrofit studies were performed to verify that savings were actually being achieved. Analysis of the CO2 data led to ventilation system changes and the installation of necessary air handling equipment.
Data loggers can also monitor the outputs of pressure gauges, AC current transformers, signal conditioners, flow sensors, and many other types of equipment. System events and condition changes that might otherwise go unnoticed can be captured for later analysis and evaluation. And you can place loggers in out-of-the-way and hard-to-reach areas to collect data when you are not there. By using data loggers to monitor and collect information, you can spend more time analyzing and evaluating the data, and implementing changes that have a positive effect on the overall system performance.
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