Understanding the Influence of Occupant Behavior on ...

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Understanding the Influence of Occupant Behavior on Energy Consumption Patterns in Commercial Buildings G. Kavulya1 and B. Becerik-Gerber2 1

PhD student, Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, California, USA 2 Assistant Professor, Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, California, USA ABSTRACT Years of research have investigated factors that contribute to high-energy consumption in buildings. These efforts have primarily focused on consumption by building systems such as HVAC systems, lighting systems and appliances. Moreover, research efforts using simulation tools have been directed at influence of occupant behavior and how occupants interact with building systems and other energy consuming devices. Simulation tools can be rigid in depicting occupant behavior and therefore continued research to understand diversity and complexity of occupant behavior and appliance interaction is required. This research, examines occupant behavior in an office environment by sensing their daily activities and their interactions with energy consuming devices. Visual observation is used to detect occupant activities while non-intrusive appliance load monitoring is used for energy monitoring. Results from a five-week long period tracking daily activities of occupants of an office building and their energy consumption patterns are presented. This research has identified and tracked five commonly used office appliances and how they contribute to energy consumption. The objective of this study is to leverage the correlation between consumption and occupant usage data to create occupant awareness of how much energy they waste. The results of this study show that even though occupants seemed oblivious, turning off appliances when not in use can realize 38% energy savings. In addition, findings of this study indicate that there is a need for energy awareness and literacy campaigns to positively modify occupant behavior as a way to reduce energy consumption. INTRODUCTION Buildings account for approximately 40% of the total energy used in the United States (EIA 2009), and about 2 to 20% of it is wasted through inefficient appliances consuming energy without performing their principal function, a concept known as “electricity leakage (Chakraborty and Pfaelzer 2011). Although heating, ventilation and air conditioning (HVAC), and lighting systems account for 29% and 27% of commercial building’s energy consumption (EIA 2009) respectively, ubiquity of office equipment and other appliances like PCs, network equipment, servers, to name a few, are increasingly becoming a major player in energy consumption (Agarwal et al. 2009).Computers by themselves consume 3% of the total energy used in commercial buildings (EIA 2009). Research efforts have also been dedicated to

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understanding the influence of occupant behavior and their interaction with appliances using simulation tools (Hoes et al. 2009). However, continued research can be pivotal in minimizing the rigidity of simulation tools in modeling diversity and complexity of occupant behavior. To reduce energy consumption of appliances, a study (Kim 2009) proposed using operating systems that support power management such as stand by modes. In addition, power meters are used to provide a basic audit of appliance consumption. These power meters accumulate or average power readings, which can be displayed as volts, current, apparent instantaneous power, actual power, and power factor over a period of time. Unfortunately, most conventional power meters cannot establish usage patterns since they are not designed to disaggregate energy consumption by end use (McLauchlan and Bessis 2011). Though newer versions of power meters can disaggregate energy consumption, however, they are relatively expensive (Darby 2006). On the other hand, there is a growing interest to make occupants aware of their energy consumption, for example buildings - fitted with smart meters to provide real-time access to power readings (McLauchlan and Bessis 2011) . Previous research (Hart 1989; Jin et al. 2011) have explored non-intrusive load monitoring (NILM) to achieve a building’s power decomposition down to equipment level. Power decomposition at appliance level is used to extract appliance signatures. As shown in these studies (Hart 1989; Jin et al. 2011), signatures can provide measurable parameters of the total load, which can provide useful information about the operating state of individual appliances. With its potential, correlating occupant activities with consumption patterns can enrich the current NILM approaches. In this research, appliance electricity loads are monitored and then linked with occupant activities extracted from visual observations. The objective of this study is to leverage the correlation between consumption and occupant usage data to create occupant awareness of how much energy they waste. The end goal is to inspire occupants to be more energy efficient, by providing access to their energy usage data. In the reviewed literature, occupant activities are deduced based on a database of signatures collected over the load-monitoring period. If deduction of occupant activities is not translated correctly, the generated results can be misleading and also might result in high computational costs (Berges et al. 2008). To minimize the challenges of deduction, and questionnaire discrepancies, this research used visual observations to track and link occupant activities to monitored electricity loads. This research examined occupant behaviors in an office environment by observing their daily activities and their interactions with their energy consuming devices. By tracking occupants’ daily activities in an office building and their energy consumption patterns, the most significant contributors to variability in energy consumption trends were identified. Appliance profiling was achieved by automating electricity capture to produce unique power signatures and occurrence patterns of power loads and is presented in the following section. RELATED WORK Hart et al.(Hart 1989) explored NILM to monitor electricity consumption in buildings by experimenting with appliances, which switch on and off independently.




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The essential steps for an automated capture and naming of appliance signatures was introduced by Hart et al. (Hart 1989) and since then other pattern recognition techniques that provide the best match with the measured signal, have emerged. A recent study (Berges et al. 2008) sought to improve NILM further through development and simplification of techniques and algorithms, which comprise of feature extraction, event detection, and pattern recognition. Appliance signature acquisition has enabled the emergence of calibration free systems. For example, Lisovich et al. (Lisovich et al. 2010) show how to extrapolate activity information from power-consumption data. Tzeng et al. (Tzeng et al. 1995) utilizes statistical methods to derive appliances’ usage patterns from electricity readings. These studies (Lisovich et al. 2010; Tzeng et al. 1995) point out challenges of developing a NILM that can disaggregate power intelligently without a priori determined appliance signatures and matching these signatures to occupant activities. Another challenge that may be more dominant in residential than commercial buildings is access to power flow. In commercial buildings research teams can partner with facilities management to gain access whereas in residential buildings research teams need to get a buy in from individual residential occupants. Hart et.al have (Hart 1989) demonstrated that the nature or operating state of individual appliances can be learned from signatures, which are quantifiable indicators of energy loads. Though learning from these signatures can provide clues of appliance usage, this paper adds the occupant dimension by invoking daily activities in commercial buildings. Visual observation can accurately detect occupant activities without the computational costs and steep learning curves associated with inference approaches. TEST BED DESCRIPTION Load monitoring becomes more meaningful if it is paired with real time occupant activities that contribute to the consumption footprint. Different kinds of activities occur in office buildings on a daily basis. Some examples of daily office activities that involve interactions with electrical equipment and therefore may impact electricity consumption are printing, faxing, charging cell phones, use of reading desk lamps, and using computers. With visual observations, recorded occupant activities and monitored electricity expenditure can be used to extract patterns of appliance use. These patterns are useful in correlating occupant activities to their impact on the total building electricity consumption. This paper used visual observations, to track and record activities of office occupants, while energy consumption by desktop computers, laptops and printers was monitored continuously. Setup. The experiments were carried out in an office space (shown in Figure 1) with 5 occupants for 5 days a week and 9 hours a day. Desktop computers usage was tracked in five workstations marked with letters A to E. The experiment was designed as a five-stepped-process, namely hardware configuration, sketch upload, calibration, data capture and visual observation. Hardware Configuration. Load monitoring was carried out using a load monitoring apparatus consisting of a breadboard, an Arduino microcontroller, voltage and current sensors as shown in figure 2. The load monitoring setup was assembled to measure



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voltage by an AC-AC power adapter and to measure current by a clip attached on the current transformer (CT) sensor.

Figure 1: Office Layout Arduino Sketch Upload. An Arduino sketch, an open source piece of code compatible with Arduino microcontrollers, was uploaded to the load monitoring apparatus. The Arduino sketch converts the raw data from its analog input into values and then outputs them to a serial monitor. The energy monitoring system was configured to calculate real power, apparent power, power factor, root mean square voltage (Vrms) and root mean square current (Irms). All calculations were done in the digital interface of Arduino sketch, connected to a computer and data logger to make use of the data. The output signal from the CT sensor was conditioned to an analog input signal between 0-5v, which is the base voltage for an Arduino microcontroller.

Calibration. In order to get accurate measurements, the load monitoring apparatus was calibrated using an off the shelf wattmeter. Calibration was done through a twostep process; power factor calibration and voltage and current calibration. Power factor calibration normalizes phase displacements from the CT sensor, power adapter SmartDraw !- purchased copies print th and multiplexed ADC readings. The voltage and current calibration was Buy done to without a watermark . document Visit www.smartdraw.com or call 1-800-768-3 reflect domestic plug meters. Arduino software takes advantage of phase calibration and digital high pass filter to eliminate potential offsets that may negatively impact the results. Load Monitoring and Data Capture. The load monitoring required extracting signatures of appliances used by office occupants. All appliances were pretested using an off the shelf wattmeter to establish their typical energy signatures. Once these signatures were derived, the next step was to monitor usage patterns and extract signatures of different types appliances in use. For data collection, occupant workstations were instrumented with a prototype system assembled from various off the shelf components, which together logged appliance power consumption every second. Figure 2 graphically depicts a simple energy load monitoring apparatus that was used to measure electrical energy usage. For analysis purposes, data was downloaded and saved into a repository every one hour of monitoring. Each entry in the serial monitor data log from left to right shows real power, apparent power, power factor, Vrms and Irms as shown in figure 2.




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This data was logged every second, which helped to detect finer patterns that may not possible with conventional hardware and software methods.

Figure 2:Load Monitoring Apparatus Visual Observations. Visual observations were employed since it can produce detailed physical activities of occupants at workstations A, B, C, D & E which can then be used to identify, compare and contrast with data obtained by the load monitoring apparatus. Occupants were covertly observed for 1 week to establish their general schedules. Afterwards their use of office appliances was monitored for 2 weeks. The typical occupant appliance usage activities observed were use of desktop computers, computer monitors, laptops, and printers. Table 1: Occupants’ Observation

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Single user appliances accessible to all occupants, specifically desktop computers, were used for associating occupant activities with energy consumption. Desktop computers were used for comparison purposes; because they were available at each workstation and are known to have low event generation rate and predictable states of energy loads i.e. active, stand by and off. The main variable was the influence of occupant activity on operating schedule of energy loads. Table 1 represents average usage of desktop computers, computer monitors, printer and laptops. The numerals shown in parenthesis represent potential savings that could



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have been realized over the monitoring period if the occupants shut off appliances when not in use. RESULTS AND DISCUSSION The data was processed to extract features of energy loads. These features indicate physical states of appliances, for example, active, stand by or off. From the data, transient behavior (change from one state to another) and steady-state durations (equilibrium) were observed as shown in figure 3. As shown in table 1, with exception of laptops, which were turned off 1% of the overall monitoring period, all other monitored appliances remained on even when not in use.

Figure 3: Appliance signatures Due to the space limitations, this paper only focuses on the use of desktop computers. Transient events enable identification of occupant interaction with desktop computer and therefore allow matching of occupant activity and energy loads with appliance state. The processed data was tagged to represent both the appliance type and power events as indicators of occupant activities. After tagging, these patterns are then mapped to the occupant activities detected using visual observations. To determine the operating schedules of individual appliances, instances of change in power measurements from one steady state to another where compared with visual observations. All occupants kept their computers running when using laptops or even when not at the workstation. As shown in table 1, desktop computers at all workstations averaged 76% and 24% stand by and active, respectively. As shown in figure 4, workstation C had the highest stand by time, 30% followed by workstation E. Workstation D had the lowest stand by time while workstation, A and B had 8% each of standby time. As observed in figure 4, a desktop computer in stand by state consumes almost 50% of energy consumed when in active mode. From the results, about 38% of energy could have been saved if occupants turned off their computers




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when not in use. In addition to energy wasted from computer use, other appliances such as printer and laptops contribute to energy saving when not in use.

Figure 4: Comparison of occupants’ desktop computer use. CONCLUSION AND FUTURE WORK This paper used a combination of NILM and visual observations to extract extra features, which were then correlated with occupant activities to identify energy consumption and potential waste. Using an Arduino prototype board, appliance signatures were extracted such as real power, power factor Vrms and Irms. Both active and stand by modes of appliances were computed for energy consumption calculations. Based on the findings, it is evident occupants were not usually aware of how much energy can be saved they turned off or completely unplug appliances from power sources. Secondly, providing occupants with info on their energy consumption patterns at fine granular levels has a potential of reducing energy through behavior modification. The next step is to develop a sensor base actuator agent to send requests to occupants for behavior modification as a way to reduce energy consumption. ACKNOWLEDGEMENTS Authors would like to acknowledge the support of the Department of Energy through the grant # DE-EE0004019. Any opinions and findings presented in this



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paper are those of the authors and do not necessarily reflect the views of the Department of Energy. REFERENCES Agarwal, Y., Weng, T., and Gupta, R. K. (2009). "The energy dashboard: Improving the visibility of energy consumption at a campus-wide scale." 1st ACM Workshop on Embedded Sens. Sys. for Energy-Efficiency in Buildings. 55-60. Berges, M., Goldman, E., Matthews, H. S., and Soibelman, L. (2008). "Training Load Monitoring Algorithms on Highly Sub-Metered Home Electricity Consumption Data." Tsinghua Science and Technology, 13 406-411. Chakraborty, A., and Pfaelzer, A. (2011). "An overview of standby power management in electrical and electronic power devices and appliances to improve the overall energy efficiency in creating a green world." J. of Renew. & Sus. Energy, 3(2),. Darby, S. (2006). "The effectiveness of feedback on energy consumption." Technical Report, Environmental Change Institute, University of Oxford, . EIA, . (2009). "Use of Energy in the United States Explained." US Energy Information Administration, . Hart, G. W. (1989). "Residential energy monitoring and computerized surveillance via utility power flows." IEEE Technol.Soc.Mag., 8(2), 12-16. Hoes, P., Hensen, J. L. M., Loomans, M. G. L. C., de Vries, B., and Bourgeois, D. (2009). "User behavior in whole building simulation." Energy Build., (3), 295-302. Jin, Y., Telebakemi, E., Berges, M., and Soibelman, L. (2011). "A time-frequency approach for event detection in non-intrusive load monitoring." SPIE, . Kim, J. H. (2009). "A study on the estimation methodology for the stand-by energy savings of televisions using learning curves and diffusion models." Transactions of the Korean Institute of Electrical Engineers, 58(2), 239-241. Lisovich, M. A., Mulligan, D. K., and Wicker, S. B. (2010). "Inferring personal information from demand-response systems." IEEE Secu. & Privacy, 8(1), 11-20. McLauchlan, N., and Bessis, N. (2011). "Towards Remote Monitoring of Power Use: A Case for Smart Meters." PARELEC, IEEE Computer Society, 133-8. Tzeng, Y. M., Chen, C. S., and Hwang, J. C. (1995). "Derivation of class load pattern by field test for temperature sensitivity analysis." Electr.Power Syst.Res., 35(1), 25-30.
