Energy Consumption Of Appliances Research Articles

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.

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.

Atomic Scheduling of Appliance Energy Consumption in Residential Smart Grids

Most of the current formulations of the optimal scheduling of appliance energy consumption use the vectors of appliances’ scheduled energy consumption over equally divided time slots of a day as optimization variables, which does not take into account the atomicity of certain appliances’ operations, i.e., the non-interruptibility of appliances’ operations and the non-throttleability of the energy consumption patterns specific to their operations. In this paper, we provide a new formulation of atomic scheduling of energy consumption based on the optimal routing framework; the flow configurations of users over multiple paths between the common source and destination nodes of a ring network are used as optimization variables, which indicate the starting times of scheduled energy consumption, and optimal scheduling problems are now formulated in terms of the user flow configurations. Because the atomic optimal scheduling results in a Boolean-convex problem for a convex objective function, we propose a successive convex relaxation technique for efficient calculation of an approximate solution, where we iteratively drop fractional-valued elements and apply convex relaxation to the resulting problem until we find a feasible suboptimal solution. Numerical results for the cost and peak-to-average ratio minimization problems demonstrate that the successive convex relaxation technique can provide solutions close to and often identical to global optimal solutions.

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.

Towards utilizing internet of things (IoT) devices for understanding individual occupants' energy usage of personal and shared appliances in office buildings

Energy consumption in office buildings highly depends on occupant energy-use behaviors and intervening these behaviors could function as a cost-effective approach to enhance energy savings. Current behavior-intervention techniques extensively rely on occupant-specific energy-use information at the workstation level and often ignore shared appliances. It is because an occupant typically has full responsibility for her workstation appliances energy consumption and shares the responsibility of the shared appliances energy consumption. However, understanding energy-use behavior of both workstation and shared appliances is necessary for applying appropriate behavior-intervention techniques. Despite this importance, there is still no practical and scalable method to capture personalized energy-use information of workstation and shared appliances since the conventional methods use plug-in power meters that are extremely expensive and difficult to maintain over long period of time. To address this gap, we propose a comprehensive occupant-level energy-usage approach which utilizes the data from the internet of things devices in office buildings to provide information related to energy-use behavior of workstation and shared appliances of each occupant in an economical and feasible manner. In particular, we introduce an energy behavior index which quantitatively compares individual occupants’ energy-consuming data to identify high energy consumers and inefficient behaviors. Results from an experiment conducted in an office building equipped with internet of things devices demonstrate the feasibility of the proposed approach to classify occupants to different energy-usage categories. Our proposed approach along with appropriate behavior-intervention techniques could be used to impact occupant energy-use behaviors.

Using smart technologies to identify occupancy and plug-in appliance interaction patterns in an office environment

There has been a widely documented phenomenon identifying an energy performance gap between the building’s design and operational phases. This result has been attributed to the stochastic behaviours exhibited by the occupants, who are assumed to follow deterministic and routine schedules. With the recent advancements in smart monitoring technologies, the increasing affordability of wireless sensors has allowed researchers to collect detailed information on the occupants’ dynamic behaviours. However, past applications of such technologies have been highly intrusive and limit the validity of the data collected due to the Hawthorne effect. Therefore, this paper proposes a non-intrusive data collection methodology using a comprehensive range of wireless smart meters, Bluetooth beacons, and questionnaires to capture the occupants’ movement and appliance interaction patterns. The feasibility of the approach is demonstrated during a two-week data collection effort in a university office. By combining the occupants’ presence with appliance energy consumption data, the authors were able to identify the occupants’ appliance interaction patterns. An extension of this work includes the use of the data collected to identify different occupancy and appliance interaction profiles, which contributes to the development of an appliance interaction model that addresses the energy performance gap caused by occupants’ appliance interaction.

Prediction of Energy Consumption in Electricity or Electronic Appliances using Sentimental Analysis and Thermal Chromic Coating for Saving Electricity Energy

Energy consumption is the challenge and issue of today’s environment in each and every applications of an individual’s life. Analysis of energy consumption has been carried out using Sentimental analysis. After analysis, remedies should take for more power consumption. Consumption made for building nearly 50% and energy loss nearly 50% due to conduction, convection, and radiation. For reducing energy consumption, new material should be insulated which should have thermal insulation properties. Thermo Chromic Material called Vanadium di oxide (VO2) which consists of reflecting properties in the infrared upon a semiconductor-to-metal phase-transition so that a notable optimal change from transparent to reflection.

Tariff for reactive energy consumption in household appliances

The use of inefficient household appliances and their poor power quality results in energy wastage in residential buildings. These appliances also force the power system to operate at low power factor which results in an ineffective energy utilization. This paper reports the energy consumption pattern of mostly used household appliances individually and collectively over a year. Their power quality parameters are measured through experimentation to calculate the reactive energy consumed by household appliances. This paper also proposes the reactive energy tariffs to enhance the awareness among domestic consumers to make efficient use of household appliances. Currently the reactive power management is being dealt for only the industrial consumer by imposing low power factor penalty. This research estimated that Lahore Electric Supply Company (LESCO) can generate a revenue of almost 150 million US dollars in one year from household consumers by applying three part tariff scheme on reactive energy. By improving the power factor it is estimated that an energy conservation of 1.1 × 109 kWh per annum is also possible. Thus the proposed tariff for reactive energy encourages the domestic consumers to get involved actively in energy conservation while enabling the energy utilities to transfer more active energy to consumers without the expansion of the distribution network.

Energy use prediction with information theory and machine learning technique

Appliances energy consumption plays an increasingly important role in the overall building electric energy consumption and its temporal trending. However, predicting appliances energy consumption is complicated by lack of causal understanding of the appliances energy use as well as too many potential predictors that might be relevant to the appliances energy use. In this study, we apply information theory and advance machine learning neural network technique to first rank the importance of potential drivers that dominate appliances energy consumption and secondly model the temporal evolution of appliances energy consumption with a restricted set of environmental predictors. Our results showed that temperature and humidity were the two most important environmental drivers in the house appliances energy consumption modeling. Furthermore, using those environmental drivers, the machine learning model was able to accurately capture the temporal dynamics of appliances energy consumption.

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.

Data-driven Time Series Based Prediction in Smart Home Appliance Energy Consumption

Data-driven Time Series Based Prediction in Smart Home Appliance Energy Consumption

Data-driven simulation for energy consumption estimation in a smart home

Simulation and data-driven models are both tools that can play an important role in reducing the energy consumption of buildings and homes. However, sophisticated control schemes and models are only as good as the data collected by sensors and provided to them. Low-quality or faulty sensor that provide inaccurate data can lead to inefficient buildings. In this paper, we investigate the relationship between sensor quality and the prediction of energy consumption. We first construct a simulation of appliance energy consumption in a smart home and then assess the predictive ability of several data-driven models while varying the quality and function of the simulated sensors. The simulation was constructed using a smart home data set collected by other researchers. We find that the predictive ability is only decreased when noise is added to the appliance energy random variable. We conclude that low-quality sensors that do not monitor the environment as accurately as the devices used in the original study could be used for humidity and temperature without significantly reducing the predictive ability of the data-driven models. The method and findings have implications for how to conduct cost-benefit analyses of IoT device requirements.

Stock modelling of residential appliance energy considering technology and occupant behavior evolution: case study of television in urban China

During the last decade, the energy consumption of residential appliance has grown rapidly in China and accounted for 20% of total urban residential energy consumption in 2015. Many studies show that the occupant behavior has a great influence on the appliance energy consumption. Due to the changes of Chinese residents’ lifestyle, the total energy consumption of residential appliance has changed a lot, so it is an important factor when we select the technology roadmap and make policy suggestions. For existing research of macro energy modelling, there is less consideration on occupant behavior. So we develop a new macro energy model to analyze the residential appliance in urban China, considering the influence of both technology and occupant behavior. This research developed a bottom-up model and did a case study on China urban residential television. The energy consumption of television in China urban households was calculated and scenarios are established to analyze the related policy suggestions. For model input, we got the panel data by conducting many regional range or nationwide survey during 2008∼2015, and arranging the data from yearbook or database. The survey and modelling analysis output the total residential television energy use in urban China by different technology, different province and different occupant behavior. The results show that total energy consumption of television starts to decline from 2011, this is mainly due to the continuous decrease of television watching duration in China urban households and the technical progress. For other appliance, the occupant behavior should also be carefully considered by researchers and policy makers.

Non-intrusive load monitoring and its challenges in a NILM system framework

With the increasing of energy demand and electricity price, researchers show more and more interest in residential load monitoring. In order to feed back the individual appliance's energy consumption instead of the whole-house energy consumption, non-intrusive load monitoring (NILM) is a good choice for residents to respond to the time-of-use price and achieve electricity saving. In this paper, we discuss the system framework of NILM and analyse the challenges in every module. Besides, we study and compare the public datasets and accuracy metrics of non-intrusive load monitoring techniques.

Wakala buildings of Mamluk era in Cairo, Egypt and how far they meet the rating criteria of LEED V4

Buildings are responsible for around 50% of energy consumption around the world and most of this because of spaces design, low heat isolation building material, occupant behavior and non-efficient architectural treatments. This have large impact on heating, cooling and ventilation demand, energy consumption of lighting and appliances, and building attitude. This paper aims to focus on passive treatments in Wakala Buildings in Cairo and how far they meet the LEED Criteria which considered the widest spread rating system in the world. By studying wakala in Cairo, there are a lot of passive treatments and energy efficiency that could be found in examples trough surveying and analyzing. Besides the environmental treatments through the natural materials and façade architectural treatments, measuring phase is to declare the efficiency of the wakala through temperature decline between outdoor and indoor. Also, identifying the thermal comfort for occupants indoors. After measuring the wakala buildings, it is the role of applying the criteria of LEED to find out how far wakala meet the LEED rating system criteria. After all, building technologies used in wakala in the field of passive design and energy efficiency would be clear and what is needed for wakala to have a LEED certification.

Measurement system and dataset for in-depth analysis of appliance energy consumption in industrial environment

Abstract To support a rational and efficient use of electrical energy in residential and industrial environments, Non-Intrusive Load Monitoring (NILM) provides several techniques to identify state and power consumption profiles of connected appliances. Design requirements for such systems include a low hardware and installations costs for residential, reliability and high-availability for industrial purposes, while keeping invasive interventions into the electrical infrastructure to a minimum. This work introduces a reference hardware setup that allows an in depth analysis of electrical energy consumption in industrial environments. To identify appliances and their consumption profile, appropriate identification algorithms are developed by the NILM community. To enable an evaluation of these algorithms on industrial appliances, we introduce the Laboratory-measured IndustriaL Appliance Characteristics (LILAC) dataset: 1302 measurements from one, two, and three concurrently running appliances of 15 appliance types, measured with the introduced testbed. To allow in-depth appliance consumption analysis, measurements were carried out with a sampling rate of 50 kHz and 16-bit amplitude resolution for voltage and current signals. We show in experiments that signal signatures, contained in the measurement data, allows one to distinguish the single measured electrical appliances with a baseline machine learning approach of nearly 100 % accuracy.

Investigation on the energy and exergy efficiencies of a domestic refrigerator retrofitted with water-cooled condensers of shell-and-coil and brazed-plate heat exchangers

Domestic refrigerator is one of the major energy-consuming appliances, and the enhancement of its energy efficiency plays a vital role in implementing the energy conservation policies and green building concepts in residential sector. The major strategies used to improve the performance of domestic refrigerators are the replacement of existing components, use of alternative refrigerants and the reduction in condensing pressure. Therefore, in this work, the conventional air-cooled condenser has been replaced with water-cooled condensers such as shell-and-coil and brazed-plate heat exchangers to maintain a low compression ratio and condensing pressure. The performance of a domestic refrigeration system retrofitted with water-cooled condensers has been studied using experimental methods. The result showed that the system with water-cooled condensers reduces the pull-down time and the per day energy consumption by 70% and 3.5%, respectively. Moreover, the proposed system can improve the COP and exergy efficiency by 6.4% and 4.9%, respectively. Compared to shell-and-coil heat exchanger, the system with brazed-plate heat exchanger can reduce irreversibility and TEWI by 3.9% and 3.7%, respectively. In this study, the system with brazed-plate heat exchanger showed better performance than the shell-and-coil heat exchanger for all operating conditions.

Household appliances penetration and ownership trends in residential buildings

Understanding the development of the trends in the ownership of different appliances in a historic context in various countries of the world can not only provide important insights for understanding the dynamics of adoption of different appliances, but can also help with foresight: how the future may develop for these or other, new appliances on the market. Although available literature in household appliances energy consumption, energy management and energy efficiency has seen some advances, there is a clear lack in the literature on household appliance ownership. In this paper, historic data is gathered and analysed for several groups of appliance types (white appliances, brown appliances and small appliances) for 12 countries representing four continents, when available since 1970 to date. Countries representing different parts of the world were selected to present an overview on household appliances ownership and energy consumption. One of the first conclusions of the study is that there is little or no information in many countries from South America, Africa or South Asia. Refrigerators, freezers and washing machines ownership are an example of most other white goods. Brown goods appeared in the market at very different time, depending on each one (from the 70s to the late 90s) and their ownership growth is much higher than for the previous ones. Most of small line appliances ownership has not reached saturation yet.

Small business electricity disaggregation: Where can we improve? Towards increased transparency of appliance modal parameters

Small commercial buildings (<50,000 ft2 in floor area) consume 44% of all U.S. commercial primary electricity, with plug loads accounting for 31% of that portion and growing. In response, a bottom-up building energy assessment resource (BEAR) was previously developed as a practical approach to disaggregating building energy use, including plug loads. This article examines the performance of BEAR in estimating plug load consumption, specifically appliance energy consumption, through use of smart meters in two building types, food service and office.Results of the analysis reveal that BEAR is capable of providing building stakeholders with meaningful energy information. Accuracy of BEAR's energy consumption estimates could be improved by 25% to 59% through better data on modal (i.e. appliance operating mode such as standby mode) usage and power demand, respectively. These results stress the importance of detailed modal information, because when modal power information was available the median difference between BEAR and the smart meter measured data was 36% compared with 223% in appliances where modal data was not available. In response to these findings, this study develops and presents energy contour plots, a visual tool for plotting modal usage and modal power in a two-dimensional form of energy consumption contours. Moreover, it is the recommendation of this study that increased transparency of modal power be implemented at the manufacturer label or specification to achieve increased accuracy of targeted energy improvements.

Evaluation of an integrated household refrigerator evaporator with two eutectic phase-change materials

Domestic refrigerators are among the most energy-consuming appliances. In many countries, efforts have been undertaken to enhance the thermal efficiency of domestic refrigerators, and such improvements have been focused mainly on compressor operation and electronic control. The use of phase change materials (PCMs) in domestic refrigerators decreases the cycling period of the compressor and reduces internal temperature fluctuations. Two eutectic PCMs are thermally characterised (PLUSICE E-10 and 19.5 wt% NH4Cl). Their phase-change temperatures and latent heat values are determined, and their thermal conductivity and density are measured at 20 °C. The main goal of the present work is to study the behaviour of an integrated modified-evaporator of a household refrigerator with two different eutectic PCMs by performing standardised tests. In general, the experimental results help enhance our understanding and improve the operation of household refrigerators in terms of the power consumption and the inner temperature variation.