Data analysis

Dutta et al. presented a simple design for energy meteringin situ by augmenting switching regulators [48], and Fonseca et al. built on top of this hardware platform to apportion energy costs of components in embedded network devices to individual activities [59]. This allows developers to quantify the effects of different approaches, but requires significant hardware and operating system modification.

Flinn et al. have also contributed a significant body of work in the area of measuring and reducing the power consumption of larger mobile devices, including quantifying the energy consumption of a pocket computer [51] and the PowerScope tool for profiling energy usage [55]. These tools produced profiles of energy usage by process and procedure which could be used to reduce the consumption of adaptive applications. For sensor-based applications that run continuously in the background of the sort that may contribute important data to a personal energy meter, it is important to know how varying the frequency of measurements or how data is stored and transmitted might affect battery life; this problem is investigated further in Section 4.5.

Significant efforts have been made to reduce the energy consumption of wireless commu-nication; while some system for measuring the power draw is required to evaluate these mechanisms, these have generally operated at a fairly coarse level. Pering et al. measured the voltage and current at the network interface cards, but sampled only every 10 ms and did not attempt to align the trace with specific actions [166]; similarly, Mohan et al.

looked at the overall power required by the sensors for their pervasive application but did not investigate any further [145].

A new mechanism for decomposing power measurements of devices to determine the energy costs of relevant states and actions is required before the consumption of shared resources can be apportioned by a personal energy meter. Section 4.5 presents one suitable system.

of civil liberties; illicit printing presses could easily be discovered, and the sophisticated burglar could determine occupants’ schedules in advance of his break-in.

In 1999, Drenker and Kader from the EPRI described progress with what they now called the Non-Intrusive Appliance Load Monitoring System (NIALMS); encouraging results in a beta test in 1998 led to offering the tested products for commercial sale [47]. They worked in conjunction with utility companies, each of which fitted several homes with both NIALMS and conventional device-level monitors. The study only investigated ‘large’

appliances as previously described, but the identification success rate was almost 100%

for those with only two states. For some appliances (heaters and pumps) the calculated total energy used was within 4% of the true value; for air conditioners, fridges and freezers it was around 13%. They claim that the commercial system has improved accuracy, and the technology is being extended to multistate loads.

NILM has a number of key advantages: only a single meter is required, minimising the time, cost and intrusiveness of the installation, and is a very promising technique. How-ever, there are known shortcomings: uncertainty and undetected error, time and effort in calibration, and a restricted set of target appliances. It works remarkably well for large loads (over 150 W) that have few power states — either on or off, or with very simple operating states such as high, medium and low. Low powered loads and those with a large number of device states (like a dishwasher) or continuously variable energy usage (like an electric cooker) are very difficult to extract from whole-house measurements. Fur-thermore, the technique breaks down when applied to commercial facilities which may contain dozens or hundreds of indistinguishable devices. Most NILM systems also rely on processing data in batch using a day or more of stored data, making them unsuitable for real-time use. Laugman et al. explained and illustrated NILM techniques in more detail, extending them to noisy and commercial environments, and surveyed the systems developed over a period of 20 years as well [130].

Marchiori and Han tried to reach a compromise between building-level non-intrusive load monitoring and individual device-level metering by disaggregating measurements taken at circuit level. This has the advantages that there are fewer devices on each circuit and high-powered devices such as ovens tend to be installed on dedicated circuits so will not interfere with lower-powered ones; the trade-off is an increased hardware cost. They used an algorithm inspired by NILM but with probabilistic level-based, rather than edge-based, disaggregation. This makes it better suited to monitoring devices with complex state or continuously varying demands. Commercial meters were used, though any system (such as those described in Section 2.3.1.2) could provide the input data; provided some devices have a control system, and can therefore be turned off remotely, the system can perform automated training. In a trial with three devices (including a PC with variable power consumption) on a circuit the system achieved an average error after 24 hours of about 5%; however, in situ household trials have not yet been conducted.

The team behind the ACme monitors also tried using them to understand better where energy was being used in their building [105]. They used the concept of additive load trees, where each node represents the total consumption of its children; instead of metering every device directly, they evaluated the feasibility of reducing the number of sensors required by taking advantage of this additive property to infer the consumption of devices where the consumption of their siblings and parents are already known. While this is straightforward when only one node in a generation is not monitored, it becomes harder when there are several, and probabilistic techniques to distinguish between loads are tried, along with additional hardware such as light and vibration sensors. The authors also discussed extrapolating the consumption of a group of devices from a sample of the population and early attempts to disaggregate load spatially and by individuals as well as functionally. Their apportionment method was fairly simplistic, allocating consumption of owned devices directly to their owners but dividing the consumption of shared devices amongst everyone whose ‘home coordinates’ fall within the same enclosing space as the device. Nevertheless, they highlighted an important area for ongoing research.

ViridiScope combines data from magnetic, acoustic and light sensors monitoring signals emitted from appliances with measurements from a home’s main power meter to learn and estimate device-level power consumption [116]. The authors used secondary indicators to infer a device’s state rather than sensing its consumption directly, propose an automating sensor calibration framework and demonstrate its use in a two-bedroom apartment where it attained an accuracy of around 90%. The system can also support directly metered appliances, and groups together all uninstrumented devices as a single ‘ghost appliance’.

Although using indirect sensors eases the deployment task because they do not need to be installed inline with power cables, it is still necessary to deploy a sensor for each significant device in the building; furthermore, an extensive, albeit somewhat automated, calibration process is required.

Rowe et al. also attempted contactless sensing of appliance state transitions, using a method very similar to ViridiScope [182]. They found that the calibration burden to estimate power consumption of each device directly was too onerous, and instead focussed on detecting state transitions as an input for NILM to quantify consumption. This can help address some of the key challenges with NILM, namely the need for appliance-specific training and the problem identifying temporally-close transients. EMF detector sensor nodes as developed by the authors can be used in a continuous training process and to resolve ambiguities. They built on ViridiScope by moving filtering into the sensors instead of transferring raw data to a separate PC for processing.

Jung and Savvides considered the problem of disaggregating total power consumption by device based on on/off state sensing and propose a possible solution using load trees which can estimate its own accuracy [107]. They also proposed an algorithm for optimally placing additional power meters to increase the accuracy to a desired level. The method was evaluated in two case studies, with approximately 20% average prediction error.

Patel et al. investigated activity sensing based on detecting and classifying electrical events on a residential power line [164]. They relied on the electrical noise created by the abrupt switching of electrical devices and use machine learning techniques to recognise the patterns caused by individual appliances. Their focus was on understanding occupant activity, rather than power consumption, but the same data could be used as an input to any of the disaggregation methods described already; the technique only works for resistive and inductive loads and so will not help distinguish between electronic devices or others with switched mode power supplies. An evaluation in six separate homes found it achieved approximately 90% classification accuracy.

Some of the same authors subsequently introduced ElectriSense, which complemented the previous system by sensing the electromagnetic interference caused by switched mode power supplies [74]. This technique has the significant advantage that calibration can be performed once for a device and then used across homes, rather than requiring per-home calibration. Experimental trials in seven homes and one six-month deployment showed a classification accuracy of around 94%.