Overall Findings
The graph below (Figure 10) summarizes findings for the differences in percentage of total annual energy savings potential associated with implementing suggested behaviors in code compliant (indicated in a lighter blue) and high performance (indicated in a darker orange) homes. In eight out of twelve behaviors, the potential for energy savings was different between the two homes. Specifically, length/frequency of showers, plug loads, and appliance purchasing were more impactful in high performance homes, while thermostat setpoints, equipment and appliance tuning/maintenance, weatherization, furniture location, and landscaping were all more impactful in code compliant homes. This supports the notion that some types of behavior should be prioritized differently in code compliant or high performance homes. Additionally, the behaviors under the HVAC, Preventative/Upgrades, and Design Choices categories had an equal or greater impact in the code compliant home, whereas, whereas behaviors under the Usage Patterns and Appliances categories had an equal or greater impact in the high performance home.
55
Figure 10. Energy Savings Potential (%) Due to Effective Behavior Management in a Code Compliant Home v. a High Performance Home.
It is interesting to note that some behaviors (ie. appliance purchasing and furniture location) that scored low in the smart home automation candidacy ranking assessment were found to have medium or high impacts on energy total annual energy savings. While these behaviors may not be good candidates for smart home automation, they should be included in considerations for prioritizing behavior management without smart home automations.
In terms of broader categories, two showed uniform candidacy for smart home automation throughout every behavior within the category: usage patterns, with an “excellent” ranking, and preventative/upgrades, with a “good” ranking. These categories may benefit from a smart home automation strategy that integrates all of the behaviors within each category.
56 Research Questions Revisited
Throughout the course of this study, each of the research questions laid out in Chapter 1 were addressed. In the Chapter 2, the fundamental differences between current code
compliant homes and high performance homes was determined to be based on the voluntary standards that high performance homes adhere to, above and beyond the mandated
regulations that code compliant homes must meet. In Chapters 2 and 4, the literature revealed twelve well-documented behaviors that impact energy performance. The literature also contained varying levels of detail regarding the potential for each of these behaviors to be monitored by smart home systems. Finally, in Chapter 4, an analysis was conducted based on several evaluation characteristics and secondary characteristics to determine the differences between these behavior impacts in code compliant and high performance homes. These findings are listed in the sections above.
Significance of the Results
This study establishes a method for systematically prioritizing which energy-
impacting occupant behaviors are most applicable to manage using smart home automation technologies. It also distinguishes secondary characteristics that should be considered to further guide the decision to pursue management of a specific behavior, and weighting of the importance of these characteristics can be tailored to individual homeowner priorities. For example, light switching and plug loads are both excellent candidates for smart home automation, but plug loads have a significantly higher potential for energy savings in both types of home, so plug loads should be prioritized before light switching in order to maximize energy savings.
57 The tables developed in this paper are user-friendly enough to be utilized by
homeowners to assist in evaluating their priorities for home renovations. The methodology behind the calculations used in determining rankings is laid out in a modular and transparent fashion, which allows builders, designers, code officials, and other members of the building industry to replace any pieces of data they wish with their own values, whether they are found elsewhere in the literature or calculated themselves. Furthermore, other behaviors that may be of interest can be easily added to the table, and evaluated using the same system.
Potential for Future Research
While this study has laid the groundwork for prioritizing occupant behaviors in home energy management, there are a number of ways that this topic may be further investigated. This research has been focused on comparing energy saving impacts in code compliant and high performance homes, but there is also a need for comparing behavior management priorities for existing home retrofits and new construction. This could be a worthwhile addition in future versions of Table 2.
The state of the data related to increases in energy savings due to effective behavior management is lacking and dated, which compromises the current validity of the results. Additionally, because of the variety of sources used in data collection for calculations, the exact scope of each calculation is somewhat vague. In future versions of these tables, more
current and transparent studies should be used in calculating the total annual energy savings
associated with implementing suggested behaviors, and if such studies do not exist, then that is an excellent path for future inquiry. Another potential route for generating more accurate
58 data might be to use computer modeling programs to develop occupancy use schedules and apply them to each type of home.
Some behaviors, like Appliance Purchasing, were found to have no current
technological aids for use in smart homes. One valuable path for future research is the design and production of smart home compatible solutions for these behaviors. For example, an application-based interactive database of energy star rated appliances that connects to Apple and Android home platforms might be an affordable solution to Appliance Purchasing.
Lastly, this study does not consider an important factor: occupant acceptance. There are a number of technological solutions available on the market that may not be desired or used effectively by some occupants. For instance, timers on showerheads that limit flow after a programmed period may not be received well by occupants, and this may significantly impact the success of that behavior management strategy, as well as that behavior’s true candidacy for smart home automation. In future research, this factor should be explored alongside the secondary characteristics, and incorporated into an overall weighted priority for implementation ranking, using candidacy ranking calculations and secondary characteristics for a more complete picture of behavior management implications and incentives.
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