Active Collaborative Sensing for Energy Breakdown

  title={Active Collaborative Sensing for Energy Breakdown},
  author={Yiling Jia and Nipun Batra and Hongning Wang and Kamin Whitehouse},
  journal={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
  • Yiling Jia, Nipun Batra, K. Whitehouse
  • Published 2 September 2019
  • Engineering, Computer Science
  • Proceedings of the 28th ACM International Conference on Information and Knowledge Management
Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed sub-meters (sensors measuring individual appliance… 

Figures and Tables from this paper

Trending machine learning models in cyber‐physical building environment: A survey

Electricity usage of buildings (including offices, malls, and residential apartments) represents a significant portion of a nation's energy expenditure and carbon footprint. In the United States, the

Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass



Transferring Decomposed Tensors for Scalable Energy Breakdown Across Regions

The key intuition is that the heterogeneity in homes and weather across different regions most significantly impacts the energy consumption across regions; and if one can factor out such heterogeneity, one can learn region independent models or the homogeneous energy breakdown components for each individual appliance.

Matrix Factorisation for Scalable Energy Breakdown

This paper proposes a novel application of feature-based matrix factorisation that does not require any additional hard- ware installation and finds it to be more effective than five baseline approaches that either require sensing in each home, or a very rigorous survey across a large number of homes coupled with complex modelling.

Energy Disaggregation via Discriminative Sparse Coding

This paper develops a method, based upon structured prediction, for discriminatively training sparse coding algorithms specifically to maximize disaggregation performance, and demonstrates how these disaggregation results can provide useful information about energy usage.

Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing, review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

Systems and analytical techniques towards practical energy breakdowns for homes

This thesis proposes new methods that can provide an energy breakdown, without installing any sensor in the home, and are up to 37% more accurate compared to the state-of-the-art energy breakdown techniques.

How good is good enough? Re-evaluating the bar for energy disaggregation

Novel techniques that use unsupervised energy disaggregation to predict both household occupancy and static properties of the household such as size of the home and number of occupants are presented.

A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem

An up to date overview of NILM system and its associated methods and techniques for energy disaggregation problem is presented and the review of the state-of-the art NILm algorithms are reviewed.

Dataport and NILMTK: A building data set designed for non-intrusive load monitoring

A subset of the Dataport database in NILMTK format is released, containing one month of electricity data from 669 households, posing a challenge to the signal processing community to produce energy disaggregation algorithms which are both accurate and scalable.

A Temporal Motif Mining Approach to Unsupervised Energy Disaggregation: Applications to Residential and Commercial Buildings

This work exploits the temporal ordering implicit in on/off events of devices to uncover motifs (episodes) corresponding to the operation of individual devices, and reveals that motif mining is adept at distinguishing devices with multiple power levels and at disentangling the combinatorial operation of devices.