Non-Invasive Behavioral Reference Group Categorization Considering Temporal Granularity and Aggregation Level of Energy Use Data

Within residences, normative messaging interventions have been gaining interest as a cost-effective way to promote energy-saving behaviors. Behavioral reference groups are one important factor in determining the effectiveness of normative messages. More personally relevant and meaningful groups are likely to promote behavior change. Using readily available energy-use profiles in a non-invasive manner permits the creation of highly personalized reference groups. Unfortunately, how data granularity (e.g., minute and hour) and aggregation (e.g., one week and one month) affect the performance of energy profile-based reference group categorization is not well understood. This research evaluates reference group categorization performance across different levels of data granularity and aggregation. We conduct a clustering analysis using one-year of energy use data from 2248 households in Holland, Michigan USA. The clustering analysis reveals that using six-hour intervals results in more personalized energy profile-based reference groups compared to using more granular data (e.g., 15 min). This also minimizes computational burdens. Further, aggregating energy-use data over all days of twelve weeks increases the group similarity compared to less aggregated data (e.g., weekdays of twelve weeks). The proposed categorization framework enables interveners to create personalized and scalable normative feedback messages

Development of an Energy Saving Strategy Model for Retrofitting Existing Buildings: A Korean Case Study

The building sector accounts for approximately 40% of national energy consumption, contributing to the environmental crisis of global warming. Using energy saving measures (e.g., improved thermal insulation, highly energy-efficient electrical and mechanical systems) provides opportunities to reduce energy consumption in existing buildings. Furthermore, if the life cycle cost (i.e., installation, operation and maintenance cost) of the measures is considered with their energy saving potential, it is possible to establish a cost-effective energy retrofit plan. Therefore, this research develops an energy saving strategy model considering its saving potential and life cycle cost of the measures for reducing energy consumption in existing buildings. To test the validity of the proposed model, a case study is carried out on an educational facility in South Korea, in response to its overconsumption of energy. The results demonstrate that in terms of energy saving and life cycle cost, the optimal energy retrofit plan is more cost-effective than the existing plan. Also, the break-even point for the optimal energy retrofit plan is within five years, and then revenue from energy saving continually occurs until 2052. For energy retrofit of existing buildings, using the proposed model would enable building owners to maximize energy savings while minimizing the life cycle cost

Preliminary Service Life Estimation Model for MEP Components Using Case-Based Reasoning and Genetic Algorithm

In recent decades, building maintenance has been recognized as an important issue as the number of deteriorating buildings increases around the world. In densely populated cities, building maintenance is essential for ensuring sustainable living and safety for residents. Improper maintenance can not only cause enormous maintenance costs, but also negatively affect residents and their environment. As a first step, the service life of building components needs to be estimated in advance. Mechanical, electrical, and plumbing (MEP) components especially produce many maintenance-related problems compared to other components. In this research, a model was developed that applies the genetic algorithm (GA) and case-based reasoning (CBR) methodologies to estimating the service life of MEP components. The applicability of the model was tested by comparing the outputs of 20 randomly selected test cases with those of retrieved similar cases. The experimental results demonstrated that the overall similarity scores of the retrieved cases were over 90%, and the mean absolute error rate (MAER) of 10-NN was approximately 7.48%. This research contributes to the literature for maintenance management by not only presenting an approach to estimating the service life of building components, but also by helping convert the existing maintenance paradigm from reactive to proactive measures

Estimating the performance of heavy impact sound insulation using empirical approaches

With an increasing demand for quieter residential environments, impact sound insulation for floating floors is gaining importance. However, existing methods for estimating the performance of heavy impact sound insulation are limited by their inability to comprehensively analyze various types of floating floors, as well as difficulties mathematically determining the input force of the reference source for heavy impacts. To overcome these limitations, this study proposes empirical models for estimating the sound insulation performance of floating floors under heavy impacts. The proposed models are then validated; the model with the highest accuracy exhibits an average estimation error of 2.73 dB at 50–630 Hz. The proposed models exhibit better accuracies than existing analytical models for frequencies below 100 Hz, where the estimation errors of the analytical models were large. Thus, the proposed models may help reduce errors in analytical estimates or when estimating a single numerical quantity for sound insulation rating during the design stage of multifamily housing