Appliance Level Research Articles

ДОМАШНІ РОБІТНИКИ В РАДЯНСЬКІЙ УКРАЇНІ В 20-30-ТІ РОКИ ХХ СТ.

The article is considered the home workers as separated social class in first in Ukrainian history. This research is based on the using of new archival documents and the results of census 1920-1930 years. The author argues that the number and condition of workers in Ukraine is depended on many factors, among of it’s the low level of development home appliances and urban infrastructure, economic instability, dwelling overcrowding and suppressed status of women in the USSR. In the article reveals the economic position of these social groups on the basis of archival documents. The author proves that the salary played a secondary role. The most important part of the income of the young maid was not cash – but housing, food and clothing. The numerous of this social group depended on opportunity employers provide favorable conditions. The main reason for reduce of home working market was economic crisis covered in the Soviet Ukraine which began in industrialization and collectivization.

Appliance Activity Recognition Using Radio Frequency Interference Emissions

Over the past few decades, with rapid growth in infrastructure, there has been tremendous growth in energy consumption. Along with this, more and more electronic appliances are added to the existing infrastructure every day. Furthermore, the existing energy bills just provide an aggregate number of units consumed but fail to provide any actionable details of appliance level usage. With the quest for long-term energy sustainability and to reduce this ever-growing energy consumption, research groups across the globe have started looking into energy disaggregation as a means of providing feedback. Some promising techniques such as non-intrusive appliance load monitoring have been adopted to provide detailed energy breakdown to the end consumer. Despite all these efforts, energy attribution to the electrical activities still seems to be a farfetched goal, especially in shared spaces. In this paper, we have analyzed the possibility of using radio frequency (RF) emissions from electronic appliances to detect electrical activity. Besides their known operation, these appliances are known to radiate high-frequency noise in the ambient environment, also called RF interference (RFI). Hence, by utilizing these RFI emissions from electronic appliances, electrical activity from the appliance can be detected in multiple frequency bands and at varying distances. An eight-fit Gaussian mixture model and $k$ -peak finder are used for feature extraction from RFI data, followed by appliance activity recognition using $k$ -nearest neighbor-based classification. Appliance detection is performed with a mean accuracy of 71.9% across seven-class classification problem. Finally, the characteristic features of RFI observed from these appliances are discussed.

CO2 laser and/or fluoride enamel treatment against in situ/ex vivo erosive challenge

ABSTRACTObjective This in situ/ex vivo study investigated the effect of CO2 laser irradiation and acidulated phosphate fluoride gel (APF) application, separately and in combination, on enamel resistance to erosion.Material and Methods During 2 experimental 5-day crossover phases, 8 volunteers wore intraoral appliances containing bovine enamel blocks which were submitted to four groups: 1st phase - control, untreated and CO2 laser irradiation, 2nd phase - fluoride application and fluoride application before CO2 laser irradiation. Laser irradiation was performed at 10.6 µm wavelength, 5 µs pulse duration and 50 Hz frequency, with average power input and output of 2.3 W and 2.0 W, respectively (28.6 J/cm2). APF gel (1.23%F, pH 3.5) was applied on enamel surface with a microbrush and left on for 4 minutes. Then, the enamel blocks were fixed at the intraoral appliance level. The erosion was performed extraorally 4 times daily for 5 min in 150 mL of cola drink. Enamel loss was measured profilometrically after treatment and after the in situ phase. The data were tested using one-way Repeated Measures Anova and Tukey's test (p<0.05).Results CO2 laser alone (2.00±0.39 µm) did not show any significantly preventive effect against enamel erosion when compared with the control group (2.41±1.20 µm). Fluoride treated enamel, associated (1.50±0.30 µm) or not (1.47±0.63 µm) with laser irradiation, significantly differed from the control.Conclusion The APF application decreased enamel wear; however, CO2 laser irradiation did not enhance fluoride ability to reduce enamel wear.

Sparse Optimization for Automated Energy End Use Disaggregation

Retrieving the household electricity consumption at individual appliance level is an essential requirement to assess the contribution of different end uses to the total household consumption, and thus to design energy saving policies and user-tailored feedback for reducing household electricity usage. This has led to the development of nonintrusive appliance load monitoring (NIALM), or energy disaggregation, algorithms, which aim to decompose the aggregate energy consumption data collected from a single measurement point into device-level consumption estimations. Existing NIALM algorithms are able to provide accurate estimate of the fraction of energy consumed by each appliance. Yet, in the authors’ experience, they provide poor performance in reconstructing the power consumption trajectories overtime. In this brief, a new NIALM algorithm is presented, which, besides providing very accurate estimates of the aggregated consumption by appliance, also accurately characterizes the appliance power consumption profiles overtime. The proposed algorithm is based on the assumption that the unknown appliance power consumption profiles are piecewise constant overtime (as it is typical for power use patterns of household appliances) and it exploits the information on the time-of-day probability in which a specific appliance might be used. The disaggregation problem is formulated as a least-square error minimization problem, with an additional (convex) penalty term aiming at enforcing the disaggregate signals to be piecewise constant overtime. Testing on household electricity data available in the literature is reported.

A Conditional Demand Analysis Based AMR Metered Load Disaggregation: a Case Study

There is growing effort to harness the load flexibility potential from household loads. Nevertheless, the true flexibility potential has not yet been quantified properly. Although the load profiles of limited number of appliances can be attained through appliance level measuring units, the application of the approach to quantify the use profile of appliances for aggregated number of households is cost prohibitive. This study applies a conditional demand analysis (CDA) technique on automatic meter reading (AMR) system metered hourly consumption data linked to detailed statistical survey data for thousands of households from different geographical location in Finland. The study principally assesses the applicability of the CDA technique in an effort to disaggregate household consumption to individual appliances. The results of the analysis include the share of major appliances from household hourly consumption on workdays and holidays of the four seasons of the year. The limitations of the application of the CDA method on hourly-metered consumption data are investigated. Furthermore a use case scenario is presented to demonstrate to what extent the disaggregated results can be useful for demand response programs. Finally, for a more accurate CDA analysis, the study recommends relevant questions to be included in the survey questioner with regard to specific appliances.

The dynamics of behavior change: Evidence from energy conservation

Little is known about the effect of message framing on conservation behavior over time. In a randomized controlled trial with residential households, we use advanced metering and information technologies to test how different messages about household energy use impact the dynamics of conservation behavior down to the appliance level. Our results, based on 374million panel observations of kilowatt-hour (kWh) electricity consumption for 118 households over 9 months, show that differences in behavioral responses due to message framing become more significant over time. We find that a health-based frame, in which households consider the human health effects of their marginal electricity use, induced persistent energy savings behavior of 8–10% over 100 days; whereas a more traditional cost savings frame, drove sharp attenuation of treatment effects after 2 weeks with no significant savings versus control after 7 weeks. We discuss implications for the design of effective information campaigns to engage households in conservation behavior.

Fine-grained appliance usage and energy monitoring through mobile and power-line sensing

To promote energy-efficient operations in residential and office buildings, non-intrusive load monitoring (NILM) techniques have been proposed to infer the fine-grained power consumption and usage patterns of appliances from power-line measurement data. Fine-grained monitoring of everyday appliances (such as toasters and coffee makers) can not only promote energy-efficient building operations, but also provide unique insights into the context and activities of individuals. Current building-level NILM techniques are unable to identify the consumption characteristics of relatively low-load appliances, whereas smart-plug based solutions incur significant deployment and maintenance costs. In this paper, we investigate an intermediate architecture, where smart circuit breakers provide measurements of aggregate power consumption at room (or section) level granularity. We then investigate techniques to identify the usage and energy consumption of individual appliances from such measurements. We first develop a novel correlation-based approach called CBPA to identify individual appliances based on both their unique transient and steady-state power signatures. While promising, CBPA fails when the set of candidate appliances is too large. To further improve the accuracy of appliance level usage estimation, we then propose a hybrid system called AARPA, which uses mobile sensing to first infer high-level activities of daily living (ADLs), and then uses knowledge of such ADLs to effectively reduce the set of candidate appliances that potentially contribute to the aggregate readings at any point. We evaluate two variants of this algorithm, and show, using real-life data traces gathered from 10 domestic users, that our fusion of mobile and power-line sensing is very promising: it identified all devices that were used in each data trace, and it identified the usage duration and energy consumption of low-load consumer appliances with ∼87% accuracy.

Short-term residential electric load forecasting: A compressive spatio-temporal approach

Load forecasting is an essential step in power systems operations with important technical and economical impacts. Forecasting can be done both at aggregated and stand-alone levels. While forecasting at the aggregated level is a relatively easier task due to smoother load profiles, residential forecasting (stand-alone level) is a more challenging task due to existing diurnal, weekly, and annual cycles effects in the corresponding time series data and fluctuations caused by the random usage of appliances by end-users. Exploring the available historical load data, it has been discovered that there usually exists an interesting trend between the data from a target house and the data from its surrounding houses. This trend can be exploited for improving the forecast accuracy. One can define several different features for each house, including house size, occupancy level, and usage behavior of appliances. While the number of such features can be large, the main challenge is how to determine the best candidates (features) for an input set without increasing the forecasting computational costs. With this objective in mind, we present a forecasting approach which combines ideas from Compressive Sensing (CS) and data decomposition. The idea is to provide a framework which facilitates exploiting the existing low-dimensional structures governing the interactions among residential houses. The effectiveness of the proposed algorithm is evaluated using real data collected from residential houses in TX, USA. The comparisons against benchmark methods show that the proposed approach significantly improves the short-term forecasts.

A framework for allocating personalized appliance-level disaggregated electricity consumption to daily activities

Residential and commercial buildings account for more than 74% of total annual electricity consumption in the United States. Studies have shown that occupants’ awareness of their behaviors in consuming electricity encourages them to change their unsustainable behaviors and improves the sustainable ones. As behaviors impact the ways that daily activities are performed, in order to develop a personalized appliance level model of an occupant's behavior, precise activity recognition is required. In this paper, we introduce a novel framework to allocate personalized appliance-level disaggregated electricity consumption to daily activities. In our framework, using ontology-based approach, the input appliance usage data is first separated into categories of non-overlapping activity events. The separated data sets are then segmented to detect activity segments, which are next mapped into activity classes using a trained classification model. To evaluate the performance of our presented framework, an experimental validation was carried out in three test bed apartment units. Results of validation showed a total F-measure value of 0.97 for segmentation and an average accuracy of 93.41% for activity recognition. Following the activity recognition, the approximate electricity consumption associated with the recognized activities was estimated and the results of each test bed unit were compared with the others.

Analysis of trends in seasonal electrical energy consumption via non-negative tensor factorization

This paper looks at the extraction of trends of household electrical seasonal consumption via load disaggregation. With the proviso that data for several home devices can be embedded in a tensor, non-negative multi-way array factorization is performed in order to extract the most relevant components. In the initial decomposition step the decomposed signals are incorporated in the test signal consisting of the whole-home measured consumption. After this the disaggregated data corresponding to each electrical device is obtained by factorizing the associated matrix through the learned model. Finally, we evaluate the performance of load disaggregation by the supervised method, and study the trends along several years and across seasons. Towards this end, computational experiments were yielded using real-world data from household electrical consumption measurements along several years. While breaking down the whole house energy consumption into appliance level gives less accurate estimates in the late years, we empirically show the adequacy of this method for handling the earlier years and the estimates of the underlying seasonal trend-cycle.

Water Nonintrusive Load Monitoring

Resource conservation decisions require detailed consumption information. This paper presents sensors and signal processing techniques that use pipe vibration signatures to non-intrusively identify water consumption at the appliance level. The method requires as little as one easily installed vibration sensor. This method provides a no-fuss retrofit solution for detecting the operation of a building's water consuming appliances. In addition, flow rate is nonintrusively obtained from a conventional water meter via a new, high sensitivity strap-on magnetic sensor. Together, these two sensors track load operating schedule and water consumption in a building, demonstrated here at three different field test sites.

Quantifying economic benefits in the ancillary electricity market for smart appliances in Singapore households

The adoption of smart grid systems has been accelerating around the world, with many pilot projects initiated throughout the developed world. However, there has been a lack of studies quantifying appliance level economic benefits that are available to the end consumers. Through coupling historical market information with an agent based residential load model, this study has quantified the economic benefits of residential smart grid participation in ancillary electricity markets. In this study, we looked at the energy market of Singapore. The regulation and reserve ancillary electricity markets were examined and the associated economic benefits from market participation were assumed to be fully passed on to the consumers. A lesson is that the potential returns for consumer investment in smart appliance technology could be very low. No matter which market that the aggregator participates in, the corresponding credits that a single appliance can earn for the consumer through ancillary markets may not be attractive enough for the consumer. Although there could be other cost saving options such as dynamic electricity prices which could be complementary to such schemes; this result highlights the fact that economic benefits alone may not be attractive enough for smart appliance adoption in the current local policy climate.

Decision-Making Impacts on Energy Consumption Display Design

AbstractPolicy makers are considering smart grid technologies such as energy consumption displays (ECD) as a means of changing the behavior of energy consumers. This article provides a review of consumer energy management behavior, presents decision-making models, and considers consumer–technology interface (CTI) display guidelines in ECD design. Existing research finds the underlying drivers of energy management behavior to be existing motivators and sociodemographics. An analysis of ECD research demonstrates a preference for an energy consumption graphic, consumption rates in monetary units, the ability to provide detailed and historical information at the appliance level, and the avoidance of unknown parameters. Integrating CTI guidelines with ECD preferences and decision-making approaches yields two ECD models. The article presents two prototype ECD models that serve different decision-making approaches. The first ECD prototype is designed to serve routine or predisposition decision makers, presenting...

The Dynamics of Behavior Change: Evidence from Energy Conservation

Author(s): Asensio, Omar I; Delmas, Magali A | Abstract: Little is known about the effect of message framing on conservation behavior over time. In a randomized controlled trial with residential households, we use advanced metering and information technologies to test how different messages about household energy use impact thedynamics of conservation behavior down to the appliance level. Our results, based on 374 million panel observations of kilowatt-hour (kWh) electricity consumption for 118 householdsover 9 months, show that differences in behavioral responses due to message framing become more significant over time. We find that a health-based frame, in which households consider thehuman health effects of their marginal electricity use, induced persistent energy savings behavior of 8-10% over 100 days; whereas a more traditional cost savings frame, drove sharp attenuationof treatment effects after 2 weeks with no significant savings versus control after 7 weeks. We discuss implications for the design of effective information campaigns to engage households inconservation behavior.

Distribution network reliability improvements in presence of demand response

Demand response (DR) as a key integral part of the future smart grid is gaining a great and still growing focus of attention in nowadays electric power industries. However, many potential benefits of DR, although they have been envisioned to be significant, have not been yet thoroughly and quantitatively investigated. DR provides network operators with the opportunity to mitigate operational limit violations by load modification in place of load shedding when network reliability is jeopardised. This paper aims to comprehensively assess the potential impacts of DR on major attributes of service reliability in a residential distribution network. For doing so, firstly, load profiles for major residential appliances are extracted from grossly metered consumptions. Secondly, the flexibility associated with individual load profiles is estimated using the statistical data gathered through surveys and questionnaires. Thirdly, for every contingency, appliance level load profiles are modified based on their flexibilities such that the least possible interruption cost is realised. The obtained results are finally combined to calculate service reliability indices. The proposed framework is applied to Finnish distribution system and the obtained results demonstrate the efficiency and applicability of the proposed approach in real-world systems.

Saving power to conserve your reputation? The effectiveness of private versus public information

Environmental damage is often an unseen byproduct of other activities. Disclosing environmental impact privately to consumers can reduce the costs and/or increase the moral benefits of conservation behaviors, while publicly disclosing such information can provide an additional motivation for conservation - cultivating a green reputation. In a unique field experiment in the residence halls at the University of California – Los Angeles, we test the efficacy of detailed private and public information on electricity conservation. Private information was given through real-time appliance level feedback and social norms over usage, and public information was given through a publicly visible conservation rating. Our analysis is based on 7,120 daily observations about energy use from heating and cooling, lights and plug load for 66 rooms collected over an academic year. Our results suggest that while private information alone was ineffective, public information combined with private information motivated a 20 percent reduction in electricity consumption achieved through lower use of heating and cooling. Public information was particularly effective for above median energy users.

Development of physical-based demand response-enabled residential load models

In order to support the growing interest in demand response (DR) modeling and analysis, there is a need for physical-based residential load models. The objective of this paper is to present the development of such load models at the appliance level. These include conventional controllable loads, i.e., space cooling/space heating, water heater, clothes dryer and electric vehicle. Validation of the appliance-level load models is carried out by comparing the models' output with the real electricity consumption data for the associated appliances. The appliance-level load models are aggregated to generate load profiles for a distribution circuit, which are validated against the load profiles of an actual distribution circuit. The DR-sensitive load models can be used to study changes in electricity consumption both at the household and the distribution circuit levels, given a set of customer behaviors and/or signals from a utility.

Towards automated appliance recognition using an EMF sensor in NILM platforms

Non-Intrusive Load Monitoring (NILM) has been studied for a few decades now as a method of disaggregating information about appliance level power consumption in a building from aggregate measurements of voltage and/or current obtained at a centralized location in the electrical system. When such information is provided to the electricity consumer as feedback, they can then take the necessary steps to modify their behavior and conserve electricity. Research has shown potential for savings of up to 20% through this kind of feedback. The training phase required to allow the algorithms to recognize appliances in the home at the beginning of a NILM setup is a big hindrance to wide adoption of the technique. One of the recent advances in this research area includes the addition of an Electro-Magnetic Field (EMF) sensor that measures the electric and magnetic field nearby an appliance to detect its operational state. This information, when coupled with the aggregate power consumption data for the home, can help to train a NILM system, which is a significant step forward in automating the training phase. This paper explores the theory behind the operation of the EMF sensor and discusses the feasibility of automating the training and classification process using these devices. A case study is presented, where magnetic field measurements of eight appliances are analyzed to determine the viability of using these signals alone to determine the type of appliance that the EMF sensor has been placed next to. Various dimensionality reduction techniques are applied to the collected data, and the resulting feature vectors are used to train a variety of common machine learning classifiers. A vector subspace obtained using Independent Component Analysis (ICA), along with a k-NN classifier, was found to perform best among the different alternatives explored. Possible reasons behind the findings are discussed and areas for further exploration are proposed.

A prediction system for home appliance usage

Power management in homes and offices requires appliance usage prediction when the future user requests are not available. The randomness and uncertainties associated with an appliance usage make the prediction of appliance usage from energy consumption data a non-trivial task. A general model for prediction at the appliance level is still lacking. This work proposes to improve learning algorithms with expert knowledge and proposes a general model using a knowledge driven approach to forecast if a particular appliance will start during a given hour or not. The approach is both a knowledge driven and data driven one. The overall energy management for a house requires that the prediction is done for the next 24h in the future. The proposed model is tested over the IRISE data and using different machine learning algorithms. The results for predicting the next hour consumption are presented, but the model works also for predicting the next 24h.

An Algorithm for Intelligent Home Energy Management and Demand Response Analysis

A home energy management (HEM) system is an integral part of a smart grid that can potentially enable demand response applications for residential customers. This paper presents an intelligent HEM algorithm for managing high power consumption household appliances with simulation for demand response (DR) analysis. The proposed algorithm manages household loads according to their preset priority and guarantees the total household power consumption below certain levels. A simulation tool is developed to showcase the applicability of the proposed algorithm in performing DR at an appliance level. This paper demonstrates that the tool can be used to analyze DR potentials for residential customers. Given the lack of understanding about DR potentials in this market, this work serves as an essential stepping-stone toward providing an insight into how much DR can be performed for residential customers.