Consumption Of Appliances Research Articles

Blockchain Enabled Distributed Demand Side Management in Community Energy System With Smart Homes

Existing work in energy demand side management focuses on the interaction between the utility grid and consumers. However, the previous technique is not focused on energy trading in local community of a renewable energy generation, distributed demand side management and not suitable for real-time environment. This paper presents a distributed demand side management system among multiple homes in community microgrid, with the integration of the internet of things smart meter and in the presence of renewable energy sources. The proposed energy consumption game is formulated for minimizing the cost of electricity in the individual home and the total cost of energy consumption in the whole community. The smart home users are playing game by optimizing their own daily energy consumption of appliances. The multiple participants include the self renewable generation of users, shared community microgrid and optional utility company. Each participant applies its best strategy to minimize energy consumption cost and users can maintain their own privacy of energy consumption. Moreover, the proposed scheme is distributed on blockchain, which provides a trusted communication medium between the participants. It enforces the autonomous monitoring of smart appliances and the billing of electricity consumption via smart contracts. Solidity smart contract is deployed to facilitate the execution of transactions without the involvement of third party in the smart community. Comparison of the results show that the proposed approach minimizes the total cost of energy consumption as well as each user's energy consumption cost.

Prepaid meter tariffing for actual power consumption in an average house hold: A case study of Nigeria DISCOs

Prepaid meters have many applications in generation, transmission and distribution of electric power in Nigeria, this makes their actual power measurement to be an important issue. In this work, actual power consumption of each load was determined and was used to study the time each load will use to consume 30.8 EEDC units when it is constantly connected to power supply. The analytical method was used to investigate the collected date for different appliance consumption rate with respect to time. This experiment was conducted in Achara Layout of Enugu South Local Government Area, Enugu state. Each of the load was subjected to test to determine the current and voltages across them. The actual power consumption of each load was calculated. The time for each actual power to completely consume 30.8 units was calculated. The corresponding amount in naira per day of the actual consumption of each load was calculated. The results show that, phone and lighting points will take longer time (18421.05 and 5589.8 hours) to drain the 30.8 units; while electrical pressing iron will take 8.95hours to drain the 30.8 units. It is also seen that lighting points and phones have the least and highest consumption cost per day. By optimal combination of these loads, it will make economic sense for the subscribers of prepaid meter, and hence reduce the phobia faced by the subscribers (if not total elimination). Keywords : Actual Power, Economic Sense, Prepaid meter, Nigeria DISCOs, Tariff.

A Novel Instrumentation Approach for Clustered Appliance Load Monitoring

This letter proposes a simple instrumentation approach for the clustered appliance load monitoring (CALM) in order to assess the individual power consumption of the household appliances. Test results reveal that the proposed CALM scheme is more accurate as compared to the existing non-intrusive load monitoring techniques, and utilizes substantially less number of sensors than the available intrusive load monitoring/distributed direct sensing approaches.

An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory

The feature of bidirectional communication in a smart grid involves the interaction between consumer and utility for optimizing the energy consumption of the users. For optimal management of the energy at the end user, several demand side management techniques are implemented. This work proposes a home energy management system, where consumption of household appliances is optimized using a hybrid technique. This technique is developed from cuckoo search algorithm and earthworm algorithm. However, there is a problem in such home energy management systems, that is, an uncertain behavior of the user that can lead to force start or stop of an appliance, deteriorating the purpose of scheduling of appliances. In order to solve this issue, coordination among appliances for rescheduling is incorporated in home energy management system using game theory. The appliances of the home are categorized in three different groups and their electricity cost is computed through the real-time pricing signals. Optimization schemes are implemented and their performance is scrutinized with and without coordination among the appliances. Simulation outcomes display that our proposed technique has minimized the total electricity cost by 50.6% as compared to unscheduled cost. Moreover, coordination among appliances has helped in increasing the user comfort by reducing the waiting time of appliances. The Shapley value has outperformed the Nash equilibrium and zero sum by achieving the maximum reduction in waiting time of appliances.

An improved non-intrusive load disaggregation algorithm and its application

The non-intrusive load monitoring (NILM) method is proposed to acquire the energy consumption of appliances in a building. Steady-state current decomposition is one of the most effective and applicable methods in NILM. Although many load features and decomposing models have been developed in the previous work, the harmonic vectors have been rarely discussed. In this study, weighted current harmonic vectors are proposed to increase the weight of the useful harmonic. The harmonic vectors are calculated geometrically so that all the information in features can be retained. A multi-objective particle swarm optimization based model is built to carry out the disaggregation task, where both summation and standard deviation of the errors are considered as objective functions. Moreover, to make the public datasets available in current decomposition, a current superimposing method is proposed. It generates aggregated currents from the currents of appliances running independently. Finally, the proposed model and two contrast models are performed on the WHITED dataset. The experimental results indicate that the proposed method is more robust and has higher current disaggregation precision than the involved comparing methods.

Experimental study on use of E-waste plastics as coarse aggregate in concrete with manufactured sand

Abstract E-waste is the fastest growing waste with an annual growth of 3–4% in the world and it is estimated that this will be increasing approximately to 55 million tonnes per annum by 2025. It comprises of different materials and chemicals such as lead, cadmium, mercury, beryllium, and plastics including polychlorinated biphenyls, poly vinyl chloride, and polystyrene, many of which are toxic and are likely to create serious problems for the environment and human health if not handled properly. One of the most important constituents of e-waste is plastics which accounts for 20%. India, with a population of over 1 billion, is a rapidly growing economy and showing increased rate in electronic appliances consumption and reuse of e-waste plastics not only helps in getting them utilized in concrete and other construction materials, it helps in reducing the cost of concrete manufacturing but mainly protecting the environment from possible pollution effects. Efforts have been made in the concrete industry to use e-waste plastics as a partial replacement of the coarse or fine aggregates. The present study is focused on replacing conventional coarse aggregate ranging from 0 to 12.5% with e-waste plastic for M 20 grade concrete using manufacturing sand. It has been found that the compressive strength of concrete replacing e-waste is high compared to conventional concrete with 10% of replacement, flexural strength is high compared to conventional concrete with 10% of replacement and the split tensile test is high compared to conventional concrete with 12.5% replacement.

Estimation of Target Appliance Electricity Consumption Using Background Filtering

Measurement of the electricity consumption of major appliances in different time segments is of crucial significance to demand-side management and energy conservation. Non-intrusive load monitoring (NILM) can infer the target appliances’ power use information by only collecting and analyzing the aggregate power data at the single power entrance point. Inspired by the success of deep neural network in other fields, some researchers have applied it to NILM with promising results. However, existing studies require labeled real aggregate data to train the networks, while time-synchronized measurement of the target appliance for labeling is hard to achieve in practice. This paper proposes to train networks with only synthetic aggregate data. Furthermore, a training data generation method via background filtering is proposed, and the obtained training data is used to train the network for estimating electricity consumption. This generation method only needs unlabeled real aggregate data and the target appliance’s operation curves, which reduces the difficulty of training data acquisition. The proposed estimation method achieves higher accuracy than current methods in tests on a public dataset which also demonstrates the effectiveness of background filtering.

Out to Dry: Change and Agency across Urban China

This PechaKucha will take the audience on an intimate, visual exploration of the evolving ways that clothing is dried outside across urban China as drying practices are forced to adapt to limitations by evolving regulation and perceptions of urban modernity. Increasingly, engaging in this social practice requires an act of agency against both municipal governments and one's own neighbors in China.While drying one's laundered clothing outdoors remains a standard part of rural life, both the legal and physical space for this practice has shrunk during China's massive urbanization. This practice is being replaced by bureaucrats' desire to stimulate domestic consumption (of appliances, in this case) and their desire to erase from cities what is considered a visual embarrassment of laundry hanging out of windows and between buildings.Today when something in urban China needs drying outside of one's privately owned space, one must assert agency over a slice of public space: A comforter is draped over a chair in a quiet, sunny corner of an apartment courtyard, or towels and sheets are thrown over a rope running between a signpost, a stop sign, and an electrical pole. While the scales and dried objects vary, the declaration of agency remains.

PMD62 THE ASSOCIATION BETWEEN STOMA RELATED COMPLICATIONS AND STOMA APPLIANCE PRESCREPTION SPEND

To identify the association between stoma complication and the prescription spend of stoma related appliances. This is a retrospective cross-sectional observational study investigating the association between stoma appliance consumption and faecal leakage from the stoma pouch (FL), peristomal skin complications (PSC), social concern (SC) and ballooning/pancaking (BP). Data is collected via a community-based patient support programme in the UK. We use multivariable regression with/without logarithmic transformation of the cost variable to model the absolute and relative association to costs of bags and plates and other reimbursed supporting products, separately and combined. 2713 patients from 31 clinical commissioning groups were included from 2016 to 2018. Mean age was 69.9yr, 47% were female. 25.1% had FL, 23.6% (PSC), 12.2% (SC) and 9.3% (BP). The mean total monthly cost of stoma appliances was £200.01 (£158.23 for bags/plates and £41.84 for supporting products). The parameter estimates (p-values) comparing patients with/without complications controlled for confounders for FL, PSC, SC and BP in the analysis of non-transformed monthly costs of bags/plates are £24.83 (0.002), £0.17 (0.973), £25.16 (<0.001) and £15.53 (0.025), respectively. The estimates in the log transformed analysis are 0.137 (<0.001), 0.048 (0.104), 0.157 (<0.001) and 0.104 (0.011). In the analysis of costs of supporting products, the estimates are £12.61 (<0.001), £2.25 (0.377), £10.58 (0.001), £5.86 (0.096) in the non-transformed analysis are0.226 (<0.001), 0.048 (0.394), 0.100 (0.150) and 0.124 (0.114) in the log transformed. The model analysing the non-transformed costs of all stoma products has the following parameter estimates, £35.55 (<.0001), £2.41 (0.697), £35.74 (<0.001) and £21.39 (0.013), respectively and 0.189 (<0.001), 0.011 (0.716), 0.126 (0.001) and 0.098 (0.020) in the log transformed analysis. FL, SC and BP are all associated with increasing stoma appliance related prescription spend.

A Smart Autonomous Time- and Frequency-Domain Analysis Current Sensor-Based Power Meter Prototype Developed over Fog-Cloud Analytics for Demand-Side Management.

Electrical energy management, or demand-side management (DSM), in a smart grid is very important for electrical energy savings. With the high penetration rate of the Internet of Things (IoT) paradigm in modern society, IoT-oriented electrical energy management systems (EMSs) in DSM are capable of skillfully monitoring the energy consumption of electrical appliances. While many of today’s IoT devices used in EMSs take advantage of cloud analytics, IoT manufacturers and application developers are devoting themselves to novel IoT devices developed at the edge of the Internet. In this study, a smart autonomous time and frequency analysis current sensor-based power meter prototype, a novel IoT end device, in an edge analytics-based artificial intelligence (AI) across IoT (AIoT) architecture launched with cloud analytics is developed. The prototype has assembled hardware and software to be developed over fog-cloud analytics for DSM in a smart grid. Advanced AI well trained offline in cloud analytics is autonomously and automatically deployed onsite on the prototype as edge analytics at the edge of the Internet for online load identification in DSM. In this study, auto-labeling, or online load identification, of electrical appliances monitored by the developed prototype in the launched edge analytics-based AIoT architecture is experimentally demonstrated. As the proof-of-concept demonstration of the prototype shows, the methodology in this study is feasible and workable.

Implementation of Energy Efficient gates using Adiabatic Logic for Low Power Applications

In today's electronic sector, low energy has appeared as a major feature. Power effectiveness is one of the most significant characteristics of contemporary, high-speed and mobile digital devices. Different methods are available to decrease energy dissipation at distinct stages of the planning method and have been applied. As the transistors count per device region continues to rise, while the switching energy does not rise at the same pace, power dissipation increases, and heat removal becomes more hard and costly. The power consumption of electronic appliances can be decreased by using various logic types. For such low-power electronic applications, adiabatic logic mode is very appealing. Using adiabatic logic, distinct power-efficient gates are intended in this document and contrasted for energy dissipation, propagation delay and no of the transistors used. In addition, the circuit developer can use these gates in the combinational and sequential circuits to develop low-power systems. The simulations of these gates are carried out in 90 nm technology using cadence virtuoso instrument.

Energy related practices in Mediterranean low-income housing

ABSTRACTThe development of policies to improve energy efficiency and the retrofitting of the existing housing stock requires adequate knowledge of the operation in practice and user needs. This becomes crucial when intervening in social housing, where household energy practices are likely to be confounded by energy affordability leading to outcomes that are distinct and sub-optimal when compared to those conventionally assumed. A field survey and analyses applied to more than 700 homes from collective social housing buildings in the south of Spain is reported here. The results show a clear stratification of energy consumption and ownership of household appliances and thermal systems. An austere self-imposed use of energy appears, coupled with normative adoption of certain energy-efficient habits particularly with respect to laundry. An emergence of multimedia and computer equipment seems to be changing the overall balance of use of equipment in homes. A clear income level above which provision of thermal comfort using mechanical means became affordable was found. The work provides useful data of home habits and identifies the main underperformance issues of this representative household-group, what may feed the design of upgrade policies and optimization of energy access for this housing stock.

Modified Cross-Entropy Method for Classification of Events in NILM Systems

Non-intrusive load monitoring is an algorithm or process that disaggregates the total power in a facility to identify consumption of individual appliances. In this paper, a new algorithm is proposed to classify events of appliance states based on modification of the cross-entropy (CE) method. The main contribution is a formulation and solution of the problem with the CE method as a constrained optimization problem. This new technique is called the modified CE method. The proposed algorithm is simple, as it operates in real time, using low rate sampling of the active power. In addition, there is no need to train the system or to use complex hardware. The algorithm is tested using the REDD and AMPds datasets and the results are compared to several state-of-the-art techniques.

A Fast Algorithm for Optimal Power Scheduling of Large-Scale Appliances With Temporally Spatially Coupled Constraints

Optimally scheduling power consumption of appliances is the essential feature of smart grid, which enables Demand Response Management (DRM) and helps to shape the power usage profile. This problem is often required to be solved in face of a large number of appliances and many time slots; thus the computational efficiency of solving a large scale optimal power scheduling problem with limited computational resources becomes the major concern of algorithm design. To this end, a novel algorithm is proposed based on Karush-Kuhn-Tucker (KKT) conditions to solve the optimal power scheduling problem with temporally spatially coupled constraints in a distributed manner. The proposed algorithm converts the original problem into equivalently solving an optimal KKT operator in a much lower dimension, thus the computation speed is greatly enhanced. In addition, the proposed method dose not require a step size in the iteration process, thus avoids the oscillation of numerical solution caused by problem parameter changes. Compared with the widely used conventional algorithms, e.g., interior point method and dual decomposition, the higher computational speed and less sensitivity to the problem parameter setting are observed in numerical simulations.

Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households

Non-intrusive load monitoring (NILM) has been extensively researched over the last decade. The objective of NILM is to identify the power consumption of individual appliances and to detect when particular devices are on or off from measuring the power consumption of an entire house. This information allows households to receive customized advice on how to better manage their electrical consumption. In this paper, we present an alternative NILM method that breaks down the aggregated power signal into categories of appliances. The ultimate goal is to use this approach for demand-side management to estimate potential flexibility within the electricity consumption of households. Our method is implemented as an algorithm combining NILM and load profile simulation. This algorithm, based on a Markov model, allocates an activity chain to each inhabitant of the household, deduces from the whole-house power measurement and statistical data the appliance usage, generate the power profile accordingly and finally returns the share of energy consumed by each appliance category over time. To analyze its performance, the algorithm was benchmarked against several state-of-the-art NILM algorithms and tested on three public datasets. The proposed algorithm is unsupervised; hence it does not require any labeled data, which are expensive to acquire. Although better performance is shown for the supervised algorithms, our proposed unsupervised algorithm achieves a similar range of uncertainty while saving on the cost of acquiring labeled data. Additionally, our method requires lower computational power compared to most of the tested NILM algorithms. It was designed for low-sampling-rate power measurement (every 15 min), which corresponds to the frequency range of most common smart meters.

Fiscal Subsidy Policy on Home Appliances: Its Effects on Domestic Consumption and Exports in China

AbstractThis paper evaluates the effects of the Home Appliances Going to the Countryside (HAGC) policy, a fiscal subsidy program implemented in China to boost private consumption of home appliances in rural areas from 2007 to 2012. Using the policy as a natural experiment and employing the difference‐in‐difference estimator, we find that the policy did not increase domestic sales of relevant goods as expected; instead, it actually reduced domestic sales and significantly promoted exports. These surprising results are robust across regressions of alternative datasets, more controls, and different regions. We further provide detailed information of undisclosed audit data for a county in Zhejiang province to shed light on the underlying mechanism of such unexpected results, suggesting loopholes in the HAGC and changes in export tax rebate rates.

MO-NILM: A multi-objective evolutionary algorithm for NILM classification

Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on readings of a centralized meter. In this work a new method for multi-dimensional NILM signals is proposed—the Multi-objective NILM (MO-NILM). While classical NILM algorithms are based on a single objective function, MO-NILM classifies NILM events by solving a multi-objective optimization problem. The main idea is to model each NILM feature as an objective function, and to mutually minimize these objectives based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The presented algorithms can operate in real time using low sampling rates (0.25 Hz and lower) without training the system. In addition, the proposed algorithm is simple, and requires information on the average power signatures of each appliance. The method shows good performance in terms of standard measures when tested on the popular REDD and AMPds datasets.

Nonintrusive load monitoring in residential households with low-resolution data

Detailed information on individual appliance consumption is beneficial for improving energy efficiency and managing demand response. Nonintrusive load monitoring (NILM) aims to estimate the device-level energy consumption from the load data of an entire household. Because the majority of households can only provide load data at a normal smart-meter level, this paper introduces a novel similar time window (STW) algorithm to perform NILM with lower-resolution data. Derived from k-nearest neighbors (kNN), the proposed STW algorithm compares both the time and frequency domain similarities between windows of interest and historical data segments, and then selects the most similar time windows by instance-based learning to determine the device-level energy consumption. The desirable features of this algorithm include (1) reductions in the costs of and requirements for sensing equipment, (2) improvements in privacy preservation, and (3) a significant enhancement of the computational efficiency. To facilitate the selection of the data resolution and to satisfy the NILM application requirements in a cost-effective way, the paper also investigates the relationship among the input/output data resolution, time window length and prediction accuracy. To enable the generalizability of this algorithm, a cross-prediction approach is proposed to obtain the device-level consumption from a “library” of a group of households, without knowing each one’s own historical data. Simulation results using four real-world public datasets demonstrate the competitive performance of the proposed STW algorithm with respect to traditionally used approaches for low-resolution NILM.

A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggregation methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodology. The contribution of this paper is in utilizing the “k-value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.

Demographics of sustainable technology consumption in an emerging market: the significance of education to energy efficient appliance adoption

PurposeThis paper aims to address gaps in the sustainable technology literature by evaluating the demographics of energy efficient appliance consumption in Vietnam. Sustainable technologies can reduce greenhouse gas emissions and address environmental problems such as air quality and climate change. Opportunity is the greatest in emerging markets, where population growth has triggered dramatic rises in electricity consumption. However, their adoption of energy efficient appliances has been slow and understanding why is limited.Design/methodology/approachFollowing a literature review, a questionnaire was designed to capture sustainable consumption attitudes and behaviours. In total, 682 interviews were conducted among Vietnamese electrical appliance consumers to investigate the influence of demographics on sustainable technology consumption.FindingsWhile many respondents were aware of the sustainable benefits of energy efficient appliances, this did not generally translate into responsible purchase behaviour. Of the demographic variables, education had the strongest relationship with sustainability. Those with higher incomes and more children were also more likely to exhibit sustainable consumption attitudes and behaviours. Gender and age were weaker sustainability predictors.Originality/valueThis study is relevant to a wide range of sustainable technology contexts. The literature shows contrary findings regarding relationships between demographics and sustainable consumption, and the value of demographics to sustainable consumer segmentation and targeted behaviour change campaigns has been contested by some researchers. This research highlights education as the most significant demographic predictor of sustainable consumption and reveals the consistency of this finding with many other studies. The implications of this for promoting future sustainability are discussed.