Minimizing intrusiveness in home energy measurement

International Green Computing Conference

Rollins

2014

Self Cite

Predictability of home energy usage forms the basis of many home energy management and demand-response systems. While existing studies focus on designing more accurate prediction algorithms, a comprehensive energy management solution requires a broad understanding of prediction accuracy at different granularities, for example appliance and home, as well as different time horizons, for example an hour, day, or week into the future. In this paper, we undertake an analysis of predictability of power draw of appliances and whole-home energy consumption at four different time horizons: an hour, a quarter-day, a day, and a week in the future. Our analysis presents two research contributions. Our first contribution is a diverse dataset, GreenHomes, that includes appliance power draw and whole-home energy consumption data from seven homes across three states in the United States over a twoyear period. Our second and primary contribution is a set of insights into the predictability of home energy usage. We show that simple statistic-based algorithms perform as well as sophisticated machine learning algorithms and time-series based predictors. These simple algorithms can considerably reduce the computational need for large-scale predictive analysis of home energy data. We also show that appliance-level power draw is more predictable than whole-home energy consumption at shorter time horizons while home-level energy consumption is more predictable at longer time horizons. Finally, we show that there is large variation in predictability across homes. This variation may be attributed to home type and points to the need for personalized energy management systems.

Section: A Data Collection Systemmentioning

confidence: 99%

Predictability of energy use in homes

Lachut

International Green Computing Conference

Rollins

2014

Self Cite

“…Our home energy measurement system, Green Homes [7], [8], collects raw energy consumption data from several devices in each home, and uses a novel in situ approach for soliciting user annotations to describe activities performed. The result is a collection of traces that describe extremely varied usage.…”

Section: B Ackground and D Ata C Ollectionmentioning

confidence: 99%

“…In this section, we present an overview of the data collection system and features of our data set as they relate to the hypotheses tested in this work. Further details regarding the underlying system architecture and deployment can be found in [7], [8]. Figure 1 illustrates the architecture of the Green Homes system.…”

Section: B Ackground and D Ata C Ollectionmentioning

confidence: 99%

Using rule mining to understand appliance energy consumption patterns

Rollins

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)

2014

Self Cite

Managing energy in the home is key to creating a sustainable future for our society. More tools are increasingly available to measure home energy usage, however these tools provide little insight into questions such as why an appliance consumes more energy than normal or what kinds of behavioral changes might be most likely to reduce energy usage in the home. To answer these questions, a deeper understanding of the causal factors that influence energy usage is necessary. In this work, we conduct a broad study of factors that influence energy consumption of individual devices in the home. Our first contribution is collection of a context-rich data set from six homes across the United States. The second contribution of this work is a set of insights into key factors influencing energy usage derived by the novel application of a rule mining algorithm to identify significant associations between energy usage and four key features: hour of the day, day of the week, use of other appliances in the home, and user-supplied annotations of activities such as working or cooking. Our analysis confirms our hypothesis that, though most devices show a regular pattern of daily or weekly use, this is not true for all devices. Associations that relate use of two different devices in the same home are often stronger, and are observed for nearly 25% of device uses. Overall, we observe that the associations derived from the first five weeks of data in our data set are sufficient to explain nearly 70% of the device uses in the subsequent five weeks of data, and over 90% of the associations identified during the first five weeks recur in the latter portion of the data set. The associations identified by our approach may be used to to aid in end-user applications that heighten awareness and encourage energy savings, improve energy disaggregation algorithms, or even detect anomalous uses that may signal problems in aging-in-place homes.

“…Several commercial entities provide a fully implemented monitoring solution [2,9,4,7], however each deployment is isolated from every other and access to the proprietary API or building data is often difficult. Some research also combines energy monitoring methods like [22], which contains both plug and building level monitoring. The focus of that work was to show how to reduce the energy consumption of the metering hardware using different methodologies.…”

Section: Energy Monitoring Hardwarementioning

confidence: 99%

“…The focus of that work was to show how to reduce the energy consumption of the metering hardware using different methodologies. The three-layer architecture used by Emonix is very common for sensor networks, and is implemented in many energy monitoring research such as [16,13,22].…”

Section: Energy Monitoring Hardwarementioning

confidence: 99%

A Distributed Energy Monitoring and Analytics Platform and its Use Cases

Klingensmith

Willis

Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings

2013

We present Emonix, a distributed, low-cost system for monitoring and analyzing energy consumption patterns in buildings. Emonix is designed with our custom energy sensing hardware and integrated communication units to be efficiently mounted in breaker panels of buildings. In contrast to plug-based monitoring systems, this approach is less intrusive to users because it does not intrude on their physical space, yet it still provides fine-grained real-time energy data in both space and time. The Emonix hardware platform is open, modular, and extensible. It provides an accessible data and configuration API for users, and we believe it is useful to the broad community. To demonstrate the usefulness of this platform, we have deployed this infrastructure on two campus dormitories covering 60 rooms and 120 residents. We have been operating this infrastructure as an energy monitoring service for the residents for more than four months to help them understand their consumption patterns at different timescales. Our results indicate significant temporal variations in energy consumption patterns at different time scales, and that a small fraction of occupants can consume a disproportionately large amount of energy in such buildings.