Is disaggregation the holy grail of energy efficiency? The case of electricity

Abstract

This paper aims to address two timely energy problems. First, significant low-cost energy reductions can be made in the residential and commercial sectors, but these savings have not been achievable to date. Second, billions of dollars are being spent to install smart meters, yet the energy saving and financial benefits of this infrastructure -without careful consideration of the human element -will not reach its full potential. We believe that we can address these problems by strategically marrying them, using disaggregation. Disaggregation refers to a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or wholebuilding, energy signal. In this paper, we explain how appliance level data affords numerous benefits, and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data. We review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. Research, technology, and policy recommendations are also outlined.

The mitigating effect of strategic behavior on the net benefits of a direct load control program

“…While there are several methods for estimating baseline consumption patterns and load comparisons, we use a regression-based baseline modeling approach. The wide scale adoption of smart grid meters and availability of high-resolution, hourly or 15-minute energy consumption data has contributed a great deal to improvements in regression-based baseline models (Carrie Armel et al 2013, Newsham et al 2011, Santin and Itard 2010, Santin et al 2009). Mathieu et al (2011) find that the regression-based baseline model performs better than most models used in evaluating DR programs.…”

Ten questions concerning integrating smart buildings into the smart grid

“…Indeed, such descriptive datasets have been shown to inform and empower a wide variety of energy stakeholders, from building operators to utilities and policy makers [69]. Moreover, disaggregate data can also enable personalized and automated recommendations [70], such as indicating which rooms within a given building use the most for HVAC and lighting relative to their actual occupancy.…”

Nilmtk

“…The field of non-intrusive load monitoring was founded 25 years ago when Hart proposed the first algorithm for the disaggregation of household energy usage [1,12]. However, the majority of research prior to 2011 had been evaluated using either lab-based or simulated data and hence the performance of disaggregation algorithms in real households had remained unknown.…”