An archive for NILM papers with source code and other supplemental material. This repo provides four weight pruning algorithms for use in sequence-to-point energy disaggregation as well as three alternative network architectures. Supplemental material on comparability and performance evaluation in NILM. Metrics to assess the generalisation ability of NILM algorithms.
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Energy management using nonintrusive load monitoring
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Updated Apr 25, Python.Nonintrusive load monitoring NILMor nonintrusive appliance load monitoring NIALM is a process for analyzing changes in the voltage and current going into a house and deducing what appliances are used in the house as well as their individual energy consumption. Electric meters with NILM technology are used by utility companies to survey the specific uses of electric power in different homes. NILM is considered a low-cost alternative to attaching individual monitors on each appliance.
It does, however, present privacy concerns. Nonintrusive load monitoring was invented by George W. The basic process is described in U.
Patent 4, As shown in figure 1 from the patent, a digital AC monitor is attached to the single-phase power going into a residence. Changes in the voltage and current are measured i. A cluster analysis is then performed to identify when different appliances are turned on and off. If a watt bulb is turned on, for example, followed by a watt bulb being turned on, followed by the watt bulb being turned off followed by the watt bulb being turned off, the NIALM unit will match the on and off signals from the watt bulb and the on and off signals from the watt bulb to determine how much power was used by each bulb and when.
The system is sufficiently sensitive that individual watt bulbs can be discriminated due to the normal variations in actual power draw of bulbs with the same nominal rating e. The system can measure both reactive power and real power. Hence two appliances with the same total power draw can be distinguished by differences in their complex impedance. As shown in figure 8 from the patent, for example, a refrigerator electric motor and a pure resistive heater can be distinguished in part because the electric motor has significant changes in reactive power when it turns on and off, whereas the heater has almost none.
NILM systems can also identify appliances with a series of individual changes in power draw. These appliances are modeled as finite state machines.
A dishwasher, for example, has heaters and motors that turn on and off during a typical dish washing cycle. These will be identified as clusters, and power draw for the entire cluster will be recorded. NILM can detect what types of appliances people have and their behavioral patterns. Patterns of energy use may indicate behavior patterns, such as routine times that nobody is at home, or embarrassing or illegal behavior of residents.
It could, for example, reveal when the occupants of a house are using the shower, or when individual lights are turned on and off. If the NILM is running remotely at a utility or by a third party, the homeowner may not know that their behavior is being monitored and recorded.
A stand-alone in-home system, under the control of the user, can provide feedback about energy use, without revealing information to others. Drawing links between their behavior and energy consumption may help reduce energy consumption, improve efficiency, flatten peak loads, save money, or balance appliance use with green energy availability. However the use of a stand-alone system does not protect one from remote monitoring. From Wikipedia, the free encyclopedia. Proceedings of the IEEE.
Archived from the original on Retrieved Categories : Electricity meters Electrical engineering Surveillance.
Hidden categories: CS1 maint: archived copy as title. Namespaces Article Talk. Views Read Edit View history. Help Learn to edit Community portal Recent changes Upload file. Download as PDF Printable version.To browse Academia. Skip to main content. Log In Sign Up. Download Free PDF. Enhancing electricity audits in residential buildings with nonintrusive load monitoring Scott Matthews.
Enhancing electricity audits in residential buildings with nonintrusive load monitoring. Berges, Ethan Goldman, H.
Scott Matthews, and Lucio Soibelman Keywords: Summary energy conservation energy use Nonintrusive load monitoring NILM is a technique for de- green buildings ducing the power consumption and operational schedule of industrial ecology individual loads in a building from measurements of the overall information and communication voltage and current feeding it, using information and commu- technology ICT nication technologies.
In this article, we review the potential of technology assessment this technology to enhance residential electricity audits. First, we review the currently commercially available whole-house and plug-level technology for residential electricity monitoring in the context of supporting audits. We then contrast this with NILM and show the advantages and disadvantages of the approach by discussing results from a prototype system in- stalled in an apartment unit.
Recommendations for improving the technology to allow detailed, continuous appliance-level auditing of residential buildings are provided, along with ideas for possible future work in the field. The wattage may to be too time-consuming or too expensive to be based on a nameplate rating, which can dif- justify, particularly in single-family homes.
The fer from actual power levels, or by connecting a average consumer currently receives a monthly portable power meter to the equipment, which bill as an indicator of his or her consumption.
Thus, more granular much higher-resolution electricity consumption feedback on appliance-level electricity consump- data than monthly bills currently do. This cre- tion is needed to validate the effectiveness of a ates an opportunity to provide accurate and proposed opportunity.
We then discuss how nonintrusive load pact of less energy-consuming appliances and less monitoring NILMa technique for identifying effective conservation activities, and underesti- individual loads from the total power consump- mate the impact of more energy-consuming ap- tion of the building, can be used to support and pliances and more effective conservation activi- enhance the audit process.
Our goal is not to ties Kempton et al. Research on energy provide highly accurate consumption informa- metering has shown that targeted feedback can tion for individual appliances in the home, but be an effective way to remedy this problem, by rather to help auditors and building owners pri- providing specific and timely information Darby oritize by providing them relevant information. Results from a prototype NILM system currently Energy audits are one way to obtain accurate deployed in an occupied residential building in and objective assessments of how to achieve sav- Pittsburgh, PA, are used to support the claims.
An energy audit is a process by which a We conclude with a discussion of the advantages building is inspected and analyzed by an expe- of NILM and the necessary improvements, along rienced technician to determine how energy is with a description of possible future work. These audits, par- While electricity metering systems for com- ticularly when focused on electricity, can identify mercial and industrial buildings have been avail- two different types of conservation opportunities: able for many years—partly due to the higher equipment upgrades and altering usage patterns.
Metrics such as residential products have emerged. Most are either plug-load and in some cases a data port allows a connected or whole-house meters, with a few exceptions. See Motegi et al. Parker et al. Examples of commercially available meters in ; Darby for a survey of this field. How- these three categories are shown in table 1. Our ever, the value of such feedback is limited by goal is not to provide a comprehensive or ex- its lack of specificity. While the users might no- tensive review of the available technologies, but tice that the home is using a significantly higher rather to provide some context for our discussion amount of power at some point, they must test about how NILM algorithms can benefit the au- different appliances to see which one is respon- dit process.Skip to Main Content.
The practicality of the proposed deep convolutional neural networks-based approach comes from the minimum prerequisite information from the previously unseen customers. That means no submetered information for the target appliances in the NILM service subscriber's house is needed to provide appliance level identification and estimate under the proposed architecture. Our solution also includes a novel post-processing technique that boost the performance significantly on type II home appliances.
The effectiveness of the solution is evaluated on a public dataset to allow comparison with the previous works.
Article :. Date of Publication: 22 May DOI: Need Help?Electrical energy is the very foundation of the modern society. While the depletion of fossil fuels and unreliability of renewables has painted a hazy picture of our future energy supply, the need for efficient utilization of electrical energy and its conservation is greater than ever.
Energy management and conservation measures not only generate immediate results but they also ensure smooth operation of the overall system.
Nonintrusive load monitoring
In addition to the economic benefits, the burden on the transmission and distribution system is reduced. Building energy disaggregation at appliance level can serve as an important tool for energy planning and conservation schemes. In order to find the energy consumption of each individual load inside a building, there are two options:. NILM on the other hand, is a sophisticated and advanced technique that requires minimal hardware with no intrusion.
A single set of voltage and current sensors are installed on the main cable outside the building or voltage and current readings are taken from the main energy meter already installed. These current and voltage signals, representing the total consumption of the building, are then analyzed using sophisticated techniques to identify individual appliances inside the building and their energy consumption.
The basic methodology of NILM is illustrated in the figure below. The "load" in the block diagram represents the combined load of the whole building.
The first stage is the data acquisition stage, where the composite current and voltage signals are obtained. Then, certain algorithms are applied to extract useful information from these signals that can be fed to a classifier. This is the feature extraction stage. Once the features are extracted, a suitable machine learning based classification algorithm is used to classify the appliances inside the building. Supervised learning techniques are most commonly used and they will require a preset database of appliances.
Feature extraction and classification stages present the main challenge in developing an effective NILM system. Research is still ongoing in this field, and engineering researchers are slowly moving towards the development of an effective and accurate model.
Event based methods focus on changes in parameters of appliances when they change states. Appliances can be classified based on their operating states, typically categorized as:. Event based methods involve event identification and then keeping track of appliance operation times. The energy consumption is estimated by appliance operation duration. Non-event based algorithms disaggregate loads from the composite signal using various machine learning, optimization and probabilistic techniques.
Event based approach is the more popular approach. The pioneer research on non-intrusive load monitoring was done by Hart in s. His research was based on steady state power analysis due to its easy availability and extraction but his work discounted the nonlinear loads and hence was not completely effective.
Moreover, performance of this model further decreases as the number of appliances increases, especially if many appliances have similar power characteristics. Researchers have since come up with a possible solution to this drawback by analyzing the signals at higher frequencies and capturing the transient characteristics.
Distinct physical characteristics of different appliances cause them each to exhibit unique transient features. To use this property in NILM, proper mechanism is required for appropriate detection of these transients.
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