Electricity Consumption Of Appliances Research Articles

Simulating appliance-level household electricity data: Accounting for residential behavior and usage patterns in China

Data scarcity presents a significant challenge for improving deep learning algorithms in Non-intrusive Load Disaggregation. These algorithms heavily rely on extensive appliance electricity consumption data, which is expensive to gather through on-site measurements. Therefore, generating simulated data has emerged as a viable alternative. However, existing approaches often randomly combine appliance power, failing to capture realistic household electricity usage patterns. To address this, this study investigates the dynamics of household electricity consumption throughout a 24-h day, considering factors such as household size, age, occupation, and education. Markov chains and Variational Auto-encoder are employed as a data generation model to simulate electrical power consumption data. Appliance power consumption data are aggregated to represent the total household load by incorporating resident electricity consumption behavior. Comparing the generated data with actual data from a self-measurement dataset, the Jensen-Shannon divergence, RMSE, and MAE are utilized to assess their similarity, and the Entropy and the Coefficient of Variation are utilized to evaluate randomness and volatility. The results indicate a high level of distribution similarity between the generated and actual data. Moreover, the observed variations show the model's capability to produce diverse and rich datasets. Overall, this research demonstrates the value of simulated data created as a low-cost method for Non-intrusive Load Disaggregation.

Full state observer-based pole placement controller for pulse width modulation switched mode voltage-controlled buck Converter

Switched mode DC-DC converters play an important role in today's renewable energy systems, electric Vehicles, consumer electrical appliances, and many more. While many researchers have designed controllers for DC-DC converters, achieving low implementation cost remains a significant hurdle. This paper proposes a cost-effective observer-based pole placement controller for a PWM voltage-controlled Buck converter with clear step by step design approach. The state space averaged model of the DC-DC power converter is used to design pole placement with integrator compensator and full state observer. Pole placement is employed to improve the transient and steady state behavior of Voltage reference tracking response and full state observer is applied to extract the inductor current with the aim of removing the actual current sensor. Design specifications are set to a settling time of less than 5 msec and an overshoot of less than 5 %. While designing Observer-Controller combination, the separation principle, designing the controller and observer independently, is utilized by placing the observer poles far to the left of the controller poles for the Observer to converge faster. The designed controller and observer is verified with simulation in MATLAB/SIMULINK software and design of developed controller and observer are realized using low-cost analog electronics circuit components The results shows that the proposed system's strength in tracking the reference output voltage even in the presence of input voltage disturbance and the mean value of inductor current is well extracted having only the output voltage and MOSFET gate signal.

Drivers and spatial patterns of carbon emissions from residential buildings: An empirical analysis of Fuzhou city (China)

Global economic development will continue to harm the natural environment. China is the largest contributor to global carbon emissions, with cities and the construction sector playing a major role. Understanding carbon emissions from buildings in cities can inform mitigation measures. This study conducted a comprehensive accounting of carbon emissions from lighting and appliance electricity consumption in residential buildings. The investigation covered Fuzhou city in Fujian Province of China in 2011–2021. ArcGIS generated and analyzed the spatial patterns of carbon emissions by districts. The STIRPAT model identified the leading carbon-emission drivers. The findings indicate: (1) fine-grained spatial distribution of carbon emissions demonstrates a marked concentration in the city center and contiguous districts, low emissions in surrounding districts, and a gradually increasing trajectory; and (2) GDP per capita, urbanization rate, and resident population are the principal drivers of carbon emissions, with every 1 % increase in GDP per capita raising carbon emissions by 0.66 %. Relevant practical energy-saving and emission-reduction measures are distilled from the results. The findings provide a scientific basis for decision-makers to formulate emission-reduction targets and strategies for Fuzhou's residential buildings, a theoretical basis for promoting regional low-carbon development, and a reference for other regions with a similar developing economy.

Occupant behavior, thermal environment, and appliance electricity use of a single-family apartment in China

The household is the basic unit of a residential community or building. High-resolution, long-term open data are necessary to help study residential electricity consumption, smart home technologies, and electricity flexibility technologies at household level. This paper introduces an IoT-based data collection platform (IDCP) consisting of gateways, sensors, and cloud servers. This platform can collect data on the occupant presence, indoor environment, window-opening states, and appliance electricity consumption simultaneously. This study deployed the IDCP in a single-family apartment in Beijing, China, and compiled a dataset, namely, CN-OBEE, including data on the per-minute occupant behavior, thermal environment, and appliance electricity use of the apartment for an entire year (from May 31, 2021, to May 31, 2022), and hourly meteorological data collected by the nearest national weather station during the same period. This dataset is the first detailed and publicly available occupant behavior and electricity use dataset for Chinese homes. As a regional feature, the dataset compiled by this study includes window-opening behavior and the use of split air conditioners (ACs).

Benchmark of Electricity Consumption Forecasting Methodologies Applied to Industrial Kitchens

Even though Industrial Kitchens (IKs) are among the highest energy intensity spaces, very little work has been done to forecast their consumption. This work explores the possibility of increasing the accuracy of the consumption forecast in an IK by forecasting disaggregated appliance consumption and comparing these results with the forecast of the total consumption of these appliances (Virtual Aggregate—VA). To do so, three different methods are used: the statistical method (Prophet), classic Machine Learning (ML) method such as random forest (RF), and deep learning (DL) method, namely long short-term memory (LSTM). This work uses individual appliance electricity consumption data collected from a Portuguese restaurant over a period of four consecutive weeks. The obtained results suggest that Prophet and RF are the more viable options. The former achieved the best performance in aggregated data, whereas the latter showed better forecasting results for most of the individual loads. Regarding the performance of the VA against the sum of individual appliance forecasts, all models perform better in the former. However, the very small difference across the results shows that this is a viable alternative to forecast aggregated consumption when only individual appliance consumption data are available.

Improving the Reliability of DC-DC Power Supply by Reserving Feedback Signals

The paper deals with the problem of improving the reliability of DC-DC power supplies with pulse-width modulation. The topicality of the work is related to the importance of power supply issues in modern electronics, since the quality of operation of consumer electrical appliances, including critical ones, directly depends on the serviceability of sources. The object of the study is feedback circuits aimed at stabilization of the parameters of power supply of consumers. Failures of the mentioned feedback circuits most often occurs due to the electronic components degradation under harsh operating conditions as well as under severe mechanical overloads. Such failures are dangerous for uncontrolled increase of power supply output voltage and output current. To avoid this, a new method of reserving voltage feedback signals is presented in the paper which is implemented on the basis of flyback supply topology. Feedback signals are formed from the optocoupler located on the load side and from the auxiliary winding of the power transformer, together forming two independent output voltage control circuits. Only one circuit performs stabilization at any given moment of time. If one of these circuits fails, the second one can simply replace it in its operation. The proposed method does not require any digital signal processing algorithms or microprocessor control modules and can be implemented on the basis of cheap, widely available analog chips that perform pulse-width control of the output voltage. As a result, the problem of sudden feedback loop failure is solved and the reliability of electrical equipment is increased. The validity of the proposed method is confirmed by the results of computer simulation with the use of MatLab-Simulink environment. The obtained results can be used in design of fault-tolerant secondary power supplies that operate in harsh operating conditions.

TraceGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks

Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter. Most current techniques for NILM are trained using significant amounts of labeled appliances power data. The collection of such data is challenging, making data a major bottleneck in creating well generalizing NILM solutions. To help mitigate the data limitations, we present the first truly synthetic appliance power signature generator. Our solution, TraceGAN, is based on conditional, progressively growing, 1-D Wasserstein generative adversarial network (GAN). Using TraceGAN, we are able to synthesise truly random and realistic appliance power data signatures. We evaluate the samples generated by TraceGAN in a qualitative way as well as numerically by using traditional GAN evaluation methods such as the Inception score. Finally, we provide a simplistic example for the use of TraceGAN as a data augmentation tool for supervised NILM training.

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.

Monitoring a Sample of Main Televisions and Connected Entertainment Systems in Northern Italy

Energy labels are a powerful instrument to influence the electricity consumption of appliances and lighting devices in households. However, the real consumption data depend on a number of different factors, including marketing policies, purchase preferences, technology development, and last but not least behavioural habits. While white appliance consumption trends tend to change over a longer period, the use of entertainment devices changes quickly. A number of different devices (digital versatile disc (DVD) player, decoder, game console, home theater, video recorder) are normally connected to the main television set, and these devices change rapidly, and, at the same time, new behaviors are emerging. There is an increasing gap between, on one hand, the higher consumption of televisions and connected devices and the number of regulations developed for their regulation, and, on the other hand, the lack of knowledge on the real onsite consumption. In order to fill this gap, in 2017, a measurement campaign was promoted and developed in some households in northern Italy. The consumption of 28 main televisions and 14 entertainment systems was measured on a daily basis for at least two weeks. Standby consumptions were measured as well. On the basis of outcomes evaluated, it results that these devices are responsible for 9.3% of total electricity consumption as an average of 5.6% for televisions and 3.7% of the attached devices. Standby consumption is still considerably high (3.6% of the total electricity consumption), especially for satellite decoders. Some interesting correlations were studied highlighting the effect of the introduction of the energy labels or the increasing size of the TV over time. The main results obtained were compared to those of previous monitoring campaigns launched in Italy.

Rationalization of Electricity Consumption in Househ Olds

Abstract With rationalization of electricity consumption, it is possible to obtain savings of electric energy in households, as well as financing and capital input for the electricity production. Rational use of appliances in the best operating modes can reduce the final consumption of electricity, representing a positive impact on improving the environment quality. The main objective of this paper was to measure the electricity consumption of appliances in various operating modes. The measured values from two energy suppliers were recorded and processed in tables and figures, from which we created a table of the financial costs necessary for operation of appliances in different modes. For the calculation of annual electricity consumption and electricity prices, an application allowing selection of individual products from suppliers with current electricity prices was designed. According to the tables of electricity prices, various modes of appliance operation allow the selection of the most preferred mode for appliance operation based on the lowest price, rational consumption and energy costs. The aim of the paper is to demonstrate the consumption and costs of operating appliances in certain operating modes, standby modes and their efficient use or functionally similar appliances for reduction of electricity consumption in households.

Frequency stability improvement of micro hydro power system using hybrid SMES and CES based on Cuckoo search algorithm

Micro hydro has been chosen because it has advantages both economically, technically and as well as in terms of environmental friendliness. Micro hydro is suitable to be used in areas that difficult to be reached by the grid. Problems that often occur in the micro hydro system are not the constant rotation of the generator that caused by a change in load demand of the consumer. Thus causing frequency fluctuations in the system that can lead to damage both in the plant and in terms of consumer electrical appliances. The appropriate control technology should be taken to support the optimum performance of micro hydro. Therefore, this study will discuss a strategy of load frequency control by using Energy Storage. Superconducting magnetic energy storage (SMES) and capacitor energy storage (CES) are devices that can store energy in the form of a fast magnetic field in the superconducting coil. For the optimum performance, it is necessary to get the optimum tuning of SMES and CES parameters. The artificial intelligence methods, Cuckoo Search Algorithm (CSA) are used to obtain the optimum parameters in the micro hydro system. The simulation results show that the application of the CSA that use to tune the parameters of hybrid SMES-CES-PID can reduce overshoot oscillation of frequency response in micro hydro power plant.

Meal plan search engine inspired by ant colony’s foraging

Currently, the communication link between consumer electrical appliances and information devices is developing; for example, a refrigerator, which keeps food fresh, now also manages information about the food ingredients it contains. This environment, known as "Ubiquitous computing" or "Smart home appliance," has the potential for further research and development. In this environment, we consider a system that recommends food recipes or meal plans that can be cooked with the available ingredients in the refrigerator. However, a conventional keyword search is not effective for such a system, and therefore, in this paper, we propose an algorithm for the search engine that is inspired by an ant colony's foraging. In addition, we validate the effectiveness of the proposed search engine through simulations.

Efficiency improvement opportunities for televisions in India: implications for market transformation programs

Televisions (TVs) account for a significant portion of residential appliance electricity consumption in India, and TV shipments in India are expected to continue to increase. We assess the market trends in the energy efficiency of TVs that are likely to occur without any additional policy intervention and estimate that TV efficiency will likely improve with saving potential of 6 terawatt-hours (TWh) per year in 2020, compared to today’s technology. We discuss various energy-efficiency improvement options and evaluate the cost-effectiveness of three of them, at least one of which improves efficiency by at least 20 % cost-effectively beyond these ongoing market trends. We provide insights for policies and programs that can be used to accelerate the adoption of efficient technologies to capture the cost-effective energy savings potential from TVs which we estimate to be 3.4 TWh per year in 2020.

Room-scale profiles of space use and electricity consumption in non-domestic buildings

Understanding activities within premises can contribute to a fuller understanding of energy use within buildings and the building stock. Analysis of detailed surveys of over 300 non-domestic premises has produced empirical room-scale space-use profiles for 16 premises types. Electricity consumption and internal gains resulting from the operation of electrical appliances have been characterized for 295 combinations of internal space use and premises activity type. For each combination, the outputs include the energy-use intensity (kWh/m2/year) profiles for: overall appliance consumption; 14 end uses of appliances (e.g. lighting, catering, computing); and 18 groups of appliance activity descriptions (e.g. sales, office work, process). These profiles of characteristics were created for application in an urban-scale energy-use model, based upon premises floor space recorded in property taxation data, without the need for detailed energy surveys of premises. Appliance electricity consumption and internal gains are revealed at a finer spatial resolution than previous methods, indicating the diversity of energy-use characteristics in greater detail than for entire homogenized premises or premises types. This method may be used for evaluating the current electricity consumption and consequent carbon emissions from the non-domestic building stock (or parts thereof) and for estimating the effects of potential interventions.

An Approach to Obtain System Disturbance Data to Consumer Reimbursement Analysis by Real Time Measurements

This paper is aimed at presenting and establishing a procedure to obtain system disturbance data by real site measurement to the evaluation of refunding requests for electrical damages occurred in consumer electrical appliances. The idea is to offer an alternative approach than the computational one to feed a software specially developed to provide final reports about the causal nexus between the distribution network abnormal operating conditions and the claimed reimbursement. In this way, in addition to the original way of reproducing the system phenomenon allegedly responsibly for the fact, the relationship between the occurrence and the equipment damage can now be made using real site information in terms of abnormal supply voltage waveform registers all over the disturbance period. For such, it is described a structure to measure, process, and transfer real-time information on incidents in power system, and its corresponding insertion in the aforementioned software. Experimental studies are also presented and discussed to illustrate and validate the proposal here made and a hypothetical refunding study is described to highlight the whole methodology

G100064 エネルギー管理システムのための無線インテリジェントタップの開発([G10006]機械力学・計測制御部門一般セッション(6):新技術)

A smart tap which can monitor power consumption for electrical appliances has attracted attention in Building and Energy Management Systems and Home Energy Management Systems (BEMS, HEMS). However, most smart taps cannot control electricity consumption of appliances from remote. In this paper, we have developed a new smart tap, called Intelligent Power Tap, which can collect power consumption data and control electricity consumption of electrical appliances via wireless network. Then, we have designed a Wi-Fi sensor module board for wireless network communications. Moreover, the effectiveness of the developed intelligent tap is demonstrated by an experiment on remote fan control.

Securing energy efficiency as a high priority: scenarios for common appliance electricity consumption in Thailand

Between 1995 and 2008, Thailand’s energy efficiency programs produced an estimated total of 8,369 GWh/year energy savings and 1,471 MW avoided peak power. Despite these impressive saving figures, relatively little future scenario analysis is available to policy makers. Before the 2008 global financial crisis, electricity planners forecasted 5–6% long-term increases in demand. We explored options for efficiency improvements in Thailand’s residential sector, which consumes more than 20% of Thailand’s total electricity consumption of 150 TWh/year. We constructed baseline and efficient scenarios for the period 2006–2026, for air conditioners, refrigerators, fans, rice cookers, and compact fluorescent light bulbs. We drew on an appliance database maintained by Electricity Generating Authority of Thailand’s voluntary labeling program. For the five appliances modeled, the efficiency scenario results in total savings of 12% of baseline consumption after 10 years and 29% of baseline after 20 years. Approximately 80% of savings come from more stringent standards for air conditioners, including phasing out unregulated air conditioner sales within 6 years. Shifting appliance efficiency standards to current best-in-market levels within 6 years produces additional savings. We discuss institutional aspects of energy planning in Thailand that thus far have limited the consideration of energy efficiency as a high-priority resource.

Fire behaviour of plastic parts in electrical appliances—standards versus required fire safety objectives

AbstractFor most categories of consumer electrical appliances, UL and IEC standards are typically the only widely used safety standards. A number of these standards are examined to determine if they incorporate technical provisions so that complying products would thereby meet expected fire safety objectives. Specifically, the provisions are examined governing plastic parts in electrical appliances. It is found that the standards do not consistently contain provisions that would, by themselves, suffice for a designer to produce a fire‐safe design. Instead, major shortcomings exist, and if an appliance were designed solely to meet the standards and without any additional fire safety considerations, it would not have a reasonable degree of safety. A typical outcome could be a product which is readily ignitable by a small external ignition source and which shows fierce burning upon being so ignited. Furthermore, an internal electrical fault in such a product would probably not be contained within the unit and, rather, would spread to its easily combustible exterior surfaces and produce a similar, fierce fire. It is urged that UL and IEC standards be upgraded so that compliance with the standards would carry a reasonable assurance that fire safety objectives have been met. The recommendations made are intended to cover all UL and IEC appliance standards, not limited to the example cases considered. Copyright © 2006 John Wiley & Sons, Ltd.

Virtual design and testing of protective packaging buffers

Manufactured products are commonly encased in moulded protective packaging buffers to protect them from damage due to impact shock during handling and transportation. The materials used to fabricate these buffers are well-known and cost-effective but not friendly to the environment. New bio-degradable materials such as paper pulp and starch have emerged as formidable alternatives, but little is known about how to design buffers from them. This paper describes a novel intelligent methodology for the virtual modeling, testing and design of protective packaging buffers. The methodology employs the use of genetic algorithms, finite element model and design routines developed to determine the optimal buffer design. Based on an ANSYS™ finite element model of the buffer, simulated drop tests were performed. The magnitudes of the largest reaction forces for the simulated drop tests as encountered by the model are computed and translated into the highest G value that the buffer can sustain without damage to the product. From the results, a more superior set of buffer designs is then derived with each passing generation. Validation tests were conducted on six different buffer configurations designed to protect six common consumer electrical appliances. The simulated G values were found to differ by a maximum of 11.8% from empirical results. The industrial norm of 10% deviation between empirical and simulated values can easily be realized when further refinements are made to the basic finite element model of the buffer. The findings validate the new methodology in buffer design in particular for new packaging materials where there are only a limited number of explicit or heuristic design rules.

End-uses of electricity in households of Karnataka state, India

This paper describes the results of a survey of electricity consumption in a sample consisting of 1,165 households in four districts of Karnataka state in India. The survey revealed the patterns of consumption of electricity in AEH[1] and non-AEH[2] households, the stock of electrical appliances used by the households, the differences in the consumption of electricity in urban and rural areas, the use of other sources of energy for domestic purposes, and the degree of penetration of energy-efficient appliances/devices. The analysis of electricity consumption of appliances in AEH households showed that the consumption of electricity is mainly due to lighting, air circulation, water-heating and cooking. In non-AEH households, the end-uses accounting for most of the consumption of electricity are lighting, entertainment and air circulation. The survey also yielded the appliance elasticities and the degree of penetration of energy-efficient devices. As a result policy-makers can take corrective actions by promoting efficiency improvements in certain end-use devices so that increases in the penetration of these devices would not significantly affect the overall electricity requirement for the domestic sector.