Prosumers are agents that both consume and produce energy. With the growth in small and medium-sized agents using solar photovoltaic panels, smart meters, vehicle-to-grid electric automobiles, home batteries and other ‘smart’ devices, prosuming offers the potential for consumers and vehicle owners to re-evaluate their energy practices. As the number of prosumers increases, the electric utility sector of today is likely to undergo significant changes over the coming decades, offering possibilities for greening of the system, but also bringing many unknowns and risks that need to be identified and managed. To develop strategies for the future, policymakers and planners need knowledge of how prosumers could be integrated effectively and efficiently into competitive electricity markets. Here we identify and discuss three promising potential prosumer markets related to prosumer grid integration, peer-to-peer models and prosumer community groups. We also caution against optimism by laying out a series of caveats and complexities.

/pdf/electricity-market-design-for-the-prosumer-era-32fg272x6c.pdf

Electricity market design for the prosumer era

Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

https://www.mdpi.com/1424-8220/12/12/16838/pdf

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

ライブラリー Annual Energy Outlook 2000

Non-intrusive load monitoring (NILM) is a popular approach to estimate appliance-level electricity consumption from aggregate consumption data of households. Assessing the suitability of NILM algorithms to be used in real scenarios is however still cumbersome, mainly because there exists no standardized evaluation procedure for NILM algorithms and the availability of comprehensive electricity consumption data sets on which to run such a procedure is still limited. This paper contributes to the solution of this problem by: (1) outlining the key dimensions of the design space of NILM algorithms; (2) presenting a novel, comprehensive data set to evaluate the performance of NILM algorithms; (3) describing the design and implementation of a framework that significantly eases the evaluation of NILM algorithms using different data sets and parameter configurations; (4) demonstrating the use of the presented framework and data set through an extensive performance evaluation of four selected NILM algorithms. Both the presented data set and the evaluation framework are made publicly available.

/pdf/the-eco-data-set-and-the-performance-of-non-intrusive-load-2pfbak70jd.pdf

The ECO data set and the performance of non-intrusive load monitoring algorithms

Dynamic load management, i.e., allowing electricity utilities to remotely turn electric appliances in households on or off, represents a key element of the smart grid. Appliances should however only be disconnected from mains when no negative side effects, e.g., loss of data or thawing food, are incurred thereby. This motivates the use of appliance identification techniques, which determine the type of an attached appliance based on the continuous sampling of its power consumption. While various implementations based on different sampling resolutions have been presented in existing literature, the achievable classification accuracies have rarely been analyzed. We address this shortcoming and evaluate the accuracy of appliance identification based on the characteristic features of traces collected during the 24 hours of a day. We evaluate our algorithm using more than 1,000 traces of different electrical appliances' power consumptions. The results show that our approach can identify most of the appliances at high accuracy.

On the accuracy of appliance identification based on distributed load metering data

Circuit-level nonintrusive load monitoring can overcome some of the basic problems relative to whole-house NILM, enabling sophisticated energy-monitoring applications. The two proposed approaches demonstrate the effectiveness of this technique. Here, we describe two approaches for circuit-level NILM that enable detailed, practical household energy monitoring: a heuristic-based approach and a Bayesian approach. Both address the limitations of previous NILM algorithms by considering steady-state power use in addition to step changes in steady-state power use and by using circuit-level energy measurements. Using the historical steady-state energy use pattern for each device lets us more precisely classify each edge and eliminate some cases of indistinguishable step changes. By measuring energy use at the circuit level, we can overcome the inability of whole-house NILM to monitor small or variable-power devices. This is because there are fewer devices on each circuit and high-powered devices (such as a stove, hot-water heater, air conditioner, and clothes dryer) each receive dedicated circuits and won't overshadow lower-powered devices (such as LVs, radios, and cell phone chargers).

Circuit-Level Load Monitoring for Household Energy Management

Smart homes hold the potential for increasing energy efficiency, decreasing costs of energy use, decreasing the carbon footprint by including renewable resources, and transforming the role of the occupant. At the crux of the smart home is an efficient electric energy management system that is enabled by emerging technologies in the electricity grid and consumer electronics. This paper presents a discussion of the state of the art in electricity management in smart homes, the various enabling technologies that will accelerate this concept, and topics around consumer behavior with respect to energy usage.

/pdf/electric-energy-management-in-the-smart-home-perspectives-on-306lzybwby.pdf

Electric Energy Management in the Smart Home: Perspectives on Enabling Technologies and Consumer Behavior

We present a forecast for systems-focused applications of non-intrusive load monitoring (NILM), which meet the needs of homeowners, the technology sector, the service sector, and/or utilities. We discuss both near- and long-term applications.

/pdf/nilm-applications-for-the-energy-efficient-home-3xjk9ua2pk.pdf

NILM Applications for the Energy-Efficient Home

Energy consumption in buildings comprises a significant fraction of total worldwide energy consumption and is strongly influenced by occupant behavior. To explore the quantitative effect of particular occupant actions on building energy consumption, we have evaluated eight energy-saving behaviors, as well as the use of an in-home display (IHD), in 10 homes over the course of ten weeks. The results showed maximum savings ranging from 0%-20% attributed to the IHD, indicating that real-time feedback combined with actionable suggestions can motivate substantial conservation. By examining the data along with pre and post-experiment surveys, we conclude that automation is necessary to ease the more tedious tasks such as "unplug when not in use" and "unplug the TV," where fewer than half of the highly capable and motivated participants performed the actions.

Lieko Earle

Papers

Circuit-Level Load Monitoring for Household Energy Management

Electric Energy Management in the Smart Home: Perspectives on Enabling Technologies and Consumer Behavior

NILM Applications for the Energy-Efficient Home

Building the case for automated building energy management

Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S. energy usage