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Journal ArticleDOI

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

01 Jan 2013-Energy Policy (Elsevier)-Vol. 52, pp 213-234
TL;DR: In this article, a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or whole-building, energy signal is presented. And the authors 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.
About: This article is published in Energy Policy.The article was published on 2013-01-01 and is currently open access. It has received 549 citations till now. The article focuses on the topics: Efficient energy use & Smart meter.

Summary (5 min read)

1. Introduction

  • The authors face several looming energy problems at this junction in history, yet taken together they may offer a unique opportunity for resolution.
  • The second problem the authors face is that 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.
  • This group estimated that costs per million households are likely to be $198-272M, while operational savings are likely $77-208M, and consumer-driven savings are likely $100-150M.
  • As of June 2011, approximately 20 million smart meters had been deployed in the U.S.

2.1. Benefits to the Consumer

  • Approximately fifty studies have investigated the effects of providing consumers with feedback on their electricity consumption, as illustrated in Figure 1 (for reviews, see Darby 2006; Fischer 2008; Neenan & Robinson, 2009; Faruqui, Sergici, & Sharif, 2009; Siddqui, 2008; Ehrhardt-Martinez, Donnelly, & Laitner, 2010).
  • Engagement channels such as existing real-world community programs and online social networks can be tapped into at low cost to foster widespread use of the recommendation system (Fuller et al., 2010; Sullivan, 2011; Rogers, 1995; Gladwell, 2000).
  • Media and incentive programs can continue to engage people once they use the recommendation system, so that they will continue to take energy saving actions.
  • Second, reducing energy use on these large appliances may be difficult (not malleable) because they are typically only replaced when they break or during a remodel.

2.2. Research and Development Benefits

  • Innovations in energy efficiency would be accelerated with end use specific information.
  • Start-up companies and corporate engineers, academics, and garage dilettantes could all make use of data collected on the actual energy consumption of different appliances and electronics to strategically focus their efforts.
  • Currently, such data is surprisingly sparse and dated.
  • As an example, redesign and strategic automated rules developed using such an approach have the potential to produce 15-50% energy savings on computer and office equipment (Kazandjieva et al., 2010) and servers (Tolia et al., 2008) during idling periods.
  • This discrepancy is large in conventional buildings, but, perhaps more importantly, in “green” buildings, where there are likely to be the best opportunities for building efficiency learnings.

2.3. Utility and Policy Benefits

  • Energy sensor data, particularly appliance specific data, has the potential to improve energy efficiency marketing – by improving market segmentation, diversifying programs, and transforming program development and evaluation.
  • Further, there is strong proof of a program’s effectiveness if consumers save energy on the specific behaviors targeted by a program, but not other behaviors.
  • These opportunities significantly improve the objectivity and rigor of program evaluation.
  • Most existing models of energy demand are constructed upon a very sparse representation of human behavior and decision making, in part because rich data has not been available to date.
  • Appliance specific data could also spur innovation.

3.1 Options for Acquiring Appliance-Specific Data

  • This section provides an overview of different technologies capable of providing appliance specific data, and their respective pros and cons, particularly those relevant to cost-effectiveness and diffusion potential (see Table 2).
  • This approach has 15 Particularly those in states which provide incentives to utilities for verified savings from energy efficiency programs.
  • In addition, these hardware solutions may have some technical and feasibility issues that are not possible to ameliorate 16 , and consume resources to manufacture and energy to operate in addition to that which will be consumed anyhow by smart meters.
  • Furthermore, smart meters may be the main option for acquiring gas data.
  • Disaggregated appliance information can guide strategic application of control devices, and the two can leverage policies such as time of use pricing and demand response events to encourage efficient timed automation and remote control.

3.2 Business Case for Disaggregation

  • This section characterizes the cost versus benefit of disaggregation technology (assuming that smart meters are already deployed by the utility and that disaggregation was not considered in the business case 17 ).
  • The authors focus on the consumer benefits from residential energy use savings, although other benefits are described in Section 2.
  • The benefit per kWh is avoided generation and distribution cost that ranges from $0.06 to $0.10.
  • Furthermore, whereas programs targeted at 17 Although note that some smart meter business cases rest upon consumer energy saving benefits that have yet to be achieved, as discussed in Section 1, so that disaggregation could help realize these benefits.

4. Disaggregation Algorithms and Their Requirements

  • This section surveys different types of disaggregation algorithms and their performance, as well as their data requirements.
  • The survey of algorithm types draws from about 40 academic peer-reviewed empirical studies as well as interviews with smart meter professionals and algorithm developers 20 .
  • Some of the companies currently working in this space include High Energy Audits, PlotWatt, Bidgely, Desert Research Institute (DRI), Navetas, General Electric, Intel, and Belkin 21 .
  • An extensive review of the work and a description of the interview questions are included in the appendices.
  • Zeifman and Roth (2011) also recently surveyed this literature; their focus is on comparing algorithmic approaches.

4.1. Patterns: Classification of disaggregation algorithms and data requirements

  • Disaggregation refers to the extraction of appliance level data from an aggregate, or whole- building, energy signal, using statistical approaches.
  • 21 Others may include Verdigris Technologies, Detectent, EcoDog, GridSpy, Check-It Monitoring Solutions, and EMME.
  • Power level resolution preferences vary depending on frequency, based on their interviews with disaggregation algorithm developers.
  • Also, algorithms utilizing data of lower frequency require longer durations to get the same number of data points, so that an algorithm using hourly data may require a week to months of data, and one using MHz data can produce results essentially in real time.

4.2. Open development questions

  • Table 4 also raises questions for future work related to algorithm performance and requirements.
  • To date, the data to develop the algorithms has been collected by academic researchers using laboratory grade sensor hardware, and the cost to sample at 1 Hz versus 10 kHz is similar, so that there has been no reason to limit the sampling rate below 10 kHz.
  • The 1 Hz – 2 kHz range is of particular interest, given the potential benefit in appliance recognition, and the fact that smart meter hardware may currently be capable of getting this but not 10 kHz data.
  • The second approach that performed beyond its class utilized a competition strategy among multiple algorithms within the system (Berges et al., 2009, 2010).
  • Thus, different appliances might be recognized by different algorithms.

5. Smart Meter Hardware Capabilities

  • Figure 4 shows a generalized block diagram of a smart meter and its key components.
  • Ideally such a data set would capture variability over appliances as well as operating conditions, including a diversity of geographic regions, housing stock, and demographic groups.
  • Definitions for accuracy and their formulas should be agreed upon because their diversity currently makes comparing algorithms very difficult.
  • It would be beneficial to determine which appliances are most important to target with disaggregation.
  • Every manufacturer’s products will vary slightly, although the block diagram in Figure 4 is intended to be generic enough to capture the common components and architecture.

5.1. Metrology Card

  • The Metrology card (also referred to simply as the meter) of the smart meter samples the main power line on the load side, measures the instantaneous voltage and current at a certain sampling frequency and uses various calculations to generate the average real power, reactive power, power factor, power quality and several other parameters.
  • In particular, it takes the sampled voltage and current values and calculates average power.
  • Regarding frequency, interviewees indicated that data leaving the signal processor is likely to be 1-10 Hz, but is likely capable of several kHz.
  • These issues can be addressed with firmware upgrades to the Flash memory (provided there is enough memory available) C. Memories (RAM and Flash): Read-Only Access Memory (RAM) is typically used by the processor for intermediate storage during various operations and is not used for storing any results of the signal processing.
  • 26 Recall the fact that, from Nyquist theorem, the authors need samples at 120N Hz rate to reconstruct the N-th harmonic.

5.2. Network Interface Card

  • The second part of the smart meter is the Network Interface Card (NIC).
  • HAN can also refer to the network that enables communication of these devices with one another absent the meter, although that is not the focus of this paper.
  • The meter communicates with the in-home devices using the ZigBee PRO standard in most previous and currently planned deployments in United States.
  • 27 While this paper references ZigBee PRO as the prevalent HAN communication standard between Smart Meters and in-home devices, several other technologies are under development and standards consideration.

6.1. Gap Between Algorithm Data Requirements vs. Smart Meter Hardware Capabilities

  • Now the authors look into whether there is a gap between the data requirements of disaggregation algorithms and the current data providing capabilities of smart meters.
  • The authors look at the three key features of the data described earlier: (a) Type of power; (b) Power level resolution; and (c) Frequency of the data.
  • The authors specify hardware imposed data constraints and compare these to the algorithm requirements.

6.1.1. Type of Power

  • Reactive power, in addition to real, is useful in disaggregation, as it helps differentiate loads sampled at lower frequencies.
  • Typical meters in the market provide real power, and are generally capable of providing reactive power, in that reactive power is generally available internally to the meter and can be brought out with a firmware upgrade.

6.1.2. Power Level Resolution

  • Developers working at the higher frequency ranges (>1 s) and attempting to disaggregate a wider range of appliances want power level at 0.1W or better resolution.
  • The power level resolution depends on the resolution of the A/D converter and the maximum current supply capability of the meter.
  • Most residential meters in United States provide up to 200A (some go up to 320A) which requires an A/D converter of 20-bit resolution or higher to meet the 0.1W data resolution requirement (also see Footnote 25).
  • Developers working at lower frequencies were satisfied with power reported at 10W or equivalent magnitude.
  • Current meters are typically constrained to 10W to meet the billing requirements, although the meter should be capable of supplying 0.1W power level resolution if required.

6.1.3. Frequency of the Data

  • Frequency is the most uncertain in terms of algorithmic requirements and hardware capabilities.
  • The 1s - 2 kHz range is where harmonics would begin to become available.
  • C. Replace ZigBee with WiFi or low power WiFi, on next generation meters.
  • Given connections are made 1-4 times per day, storing raw data in the meter and sending it through the WAN increases memory and bandwidth requirements.

6.2. Where to Perform Algorithm Processing?

  • The previous two sections focused on whether smart meter hardware is capable of supplying adequate data for disaggregation.
  • 30 This option could take several different hardware configurations.
  • In the short term the best option is likely to compress data on the meter, and then use Options 1 or 2 above for performing the disaggregation.
  • This option also allows smart meters to be connected to the anticipated “internet of things” for additional consumer applications.
  • Then a large variety of third party devices could be plugged in, directly sampling the power waveform data at the desired frequency; and further storing, processing, and communicating through any networks – for example, the HAN, internet, or cell network.

6.3. Cost to Support Disaggregation

  • The smart meter changes suggested in the above sections can be classified into two categories – firmware upgrades and hardware modifications.
  • When done in conjunction with regular firmware upgrades (typical periodic upgrades done every quarter), the cost for firmware upgrades related to enabling disaggregation can be minimal.
  • The hardware modifications required vary depending on the sampling frequency used for disaggregation.
  • It is possible that the cost and power consumption for a WiFi chip may be incrementally higher compared to a ZigBee chip, but the incremental cost and power is unlikely to approach that of an extra ZigBee to WiFi gateway required to support HAN functionality.
  • The cost and energy consumption comparison of WiFi or Low Power WiFi in the meter versus ZigBee receiver in a router has yet to be determined.

7. Recommendations and Conclusions

  • In conjunction with the common dataset, it would be beneficial to: (a) Establish performance metrics, such as common definitions of accuracy to enable the comparison of algorithms.
  • Facilitate testing of compression and disaggregation algorithms on actual smart meters, to evaluate capabilities.
  • Be capable of supporting disaggregation inside smart meters.
  • Large public expenditures are going towards the smart grid, and there is great potential for innovation and consumer benefit, but this is likely to go unrealized without greater transparency.

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Citations
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Journal ArticleDOI
06 Dec 2012-Sensors
TL;DR: 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.
Abstract: 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.

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Cites background or methods from "Is disaggregation the holy grail of..."

  • ...The high end NICs can read, write and report data up to 1 kHz, however changes are required in the meter hardware to support sampling rate greater than 5 kHz [10]....

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  • ...In addition, low-cost metering solutions offer limited functionality as they are equipped with low resolution Analog to Digital (A/D) converter and small size on-chip Flash memory used by the processing unit for storing results after various operations [10]....

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Journal ArticleDOI
TL;DR: This work presents UK-DALE: an open-access dataset from the UK recording Domestic Appliance-Level Electricity at a sample rate of 16 kHz for the whole-house and at 1/6 Hz for individual appliances, which is the first open access UK dataset at this temporal resolution.
Abstract: Many countries are rolling out smart electricity meters. These measure a home's total power demand. However, research into consumer behaviour suggests that consumers are best able to improve their energy efficiency when provided with itemised, appliance-by-appliance consumption information. Energy disaggregation is a computational technique for estimating appliance-by-appliance energy consumption from a whole-house meter signal. To conduct research on disaggregation algorithms, researchers require data describing not just the aggregate demand per building but also the `ground truth' demand of individual appliances. In this context, we present UK-DALE: an open-access dataset from the UK recording Domestic Appliance-Level Electricity at a sample rate of 16 kHz for the whole-house and at 1/6 Hz for individual appliances. This is the first open access UK dataset at this temporal resolution. We recorded from five houses, one of which was recorded for 655 days, the longest duration we are aware of for any energy dataset at this sample rate. We also describe the low-cost, open-source, wireless system we built for collecting our dataset.

581 citations


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15 Mar 2016
TL;DR: Potential contributions that cyber-physical systems can make to smart grids, as well as the challenges that smart grids present to cyber- physical systems are outlined.
Abstract: Smart grids are electric networks that employ advanced monitoring, control, and communication technologies to deliver reliable and secure energy supply, enhance operation efficiency for generators and distributors, and provide flexible choices for prosumers. Smart grids are a combination of complex physical network systems and cyber systems that face many technological challenges. In this paper, we will first present an overview of these challenges in the context of cyber–physical systems. We will then outline potential contributions that cyber–physical systems can make to smart grids, as well as the challenges that smart grids present to cyber–physical systems. Finally, implications of current technological advances to smart grids are outlined.

487 citations


Cites background from "Is disaggregation the holy grail of..."

  • ...ber of critical tasks performed by human operators based on the raw data presented and past experiences [30]....

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  • ...The intermittent availability of RE requires consideration of the entire operational regime to deal with the associated problems such as storages and variable power quality [30]....

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Journal ArticleDOI
TL;DR: A comprehensive survey of smart electricity meters and their utilization is presented focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interest.
Abstract: Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social, and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics, which deals with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interests. Furthermore, the paper highlights challenges as well as opportunities arising due to the advent of big data and the increasing popularity of cloud environments.

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TL;DR: In this paper, a nonintrusive appliance load monitor that determines the energy consumption of individual appliances turning on and off in an electric load, based on detailed analysis of the current and voltage of the total load, as measured at the interface to the power source is described.
Abstract: A nonintrusive appliance load monitor that determines the energy consumption of individual appliances turning on and off in an electric load, based on detailed analysis of the current and voltage of the total load, as measured at the interface to the power source is described. The theory and current practice of nonintrusive appliance load monitoring are discussed, including goals, applications, load models, appliance signatures, algorithms, prototypes field-test results, current research directions, and the advantages and disadvantages of this approach relative to intrusive monitoring. >

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  • ...Diagnostics can be performed, for example, to achieve auto-commissioning – recommended adjustments to the building operation to improve performance and efficiency – and fault detection – notification if an appliance should be fixed because it is consuming more energy than it should due to a malfunction (Hart, 1992)....

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  • ...In the basic form presented in Hart (1992), the NALM method is able to disaggregate some simple appliances (minimum load 150 W) that have a finite number of states (e.g., ON/OFF), for which Hart reports accuracies of 85%33....

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2,618 citations

Frequently Asked Questions (17)
Q1. What are the contributions in "Is disaggregation the holy grail of energy efficiency? the case of electricity" ?

This paper aims to address two timely energy problems. In this paper, the authors 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. The authors review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. 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. The authors believe that they can address these problems by strategically marrying them, using disaggregation. 

Transmitting events/transitions instead of raw load profiles could significantly improve the frequency of data available toHAN devices, as band-with is currently the bottleneck. 

the use of the receiver operating characteristic (ROC) curve is likely to be beneficial in showing the tradeoff between sensitivity (probability of Type II error or false negatives) and specificity (probability of Type The authorerror or false positives). 

Determining how much energy is consumed by different appliances is a first step, and automated recommendation and action systems next steps, to realizing savings. 

Diagnostics can be performed, for example, to achieve auto-commissioning – recommended adjustments to the building operation to improve performance and efficiency - and fault detection - notification if an appliance should be fixed because it is consuming more energy than it should due to a malfunction (Hart, 1992). 

Smart meters, given their widespread roll-outs, and ability to circumvent cost and effort barriers, offer an opportunity for quick, sweeping market penetration of sensing hardware required for disaggregation. 

More granular data can also improve their understanding of energy consumption patterns, and this can be used to improve the representation of behavior in energy models. 

Regarding the size of the market, as of June 2011 approximately 20 million meters had beendeployed in the U.S. with more planned 19 ; furthermore, software based disaggregation can also be run with millions of already deployed AMR (Automatic Meter Reading) meters containing Itron technology. 

In summary, disaggregation may be the lynchpin to realizing large-scale, cost-effectiveenergy savings in residential and commercial buildings. 

Another way of addressing the sampling, storage, and processing issues discussed here would be to build meters in the future with a serial port and a power supply. 

Improving wattage granularity by enhancing A/D converter resolution (details in Section 6.1.2) would also improve recognition, particularly of smaller electronics, which is of increasing importance given plug loads are the fastest growing segment of electricity use (Ecos, 2006, 2011). 

This is because energy savings resulting from a given behavior are likely to be swamped in an aggregate energy signal, particularly because consumers are likely to change only a small number of behaviors at any given time. 

The uptake of energy efficient appliances and electronics may increase because consumers know where energy efficiency improvements need to be made in their homes, or which appliances should be repaired or replaced. 

Public utility commissions, utilities, and meter manufacturers should consider therecommendations above when contemplating policy rulings and technology specifications for current and future smart meters. 

22Note that the number of appliances or end uses to be disaggregated for gas, water, and transportation is fewer than with electricity, which makes disaggregation easier. 

As a preliminary indication of the size of potential savings, pilot studies with plug monitors in commercial buildings have identified institutional rules and automation that saved a total of between 15-40% of electricity consumed across the dozens of devices monitored (Mercier & Moorefield, 2011; Houk, 2010). 

Any of these configurations could also send data to the cloud for disaggregation (instead of performing disaggregation on the HAN device/router/home computer), as an intermediate step before sending information to the consumer displays.