Consumption Of Appliances Research Articles

Tabu Search Algorithm for Optimal Electric Water Heating Energy Management

The aim of this paper is to develop an optimal energy management strategy for electric water heaters (EWHs), based on operational data such as photovoltaic (PV) production, ambient temperature (AT), water demand (WD), and fixed appliance consumption (FAC), electricity tariffs and user preferences. The proposed strategy uses a multi-objective Tabu Search (TS) algorithm to determine the optimal domestic hot water output over the next 24 hours based on this prior data. The aim is to minimize the electricity bill while maintaining a desired level of comfort, by keeping the water temperature within a range compatible with the user’s thermal comfort. In addition, the temperature setpoint was varied according to operating conditions. A comparison with particle swarm optimization (PSO)-based management, which uses a similar strategy but employs the PSO algorithm for optimization, reveals that the proposed strategy achieves a significant 33.2% reduction in electricity costs and a 3.8% reduction in carbon emissions.

The Impact of Media Publicity and Online Product Reviews on Consumers’ Purchase Intentions of Energy-Efficient Appliances

In the changing global economy, energy security has become a central concern for the sustainable development of nations. The limited energy supply in developing countries has led governments and enterprises to explore efficient energy resources. The consumption of energy-efficient appliances is a crucial way to address energy scarcity. This study aims to predict consumer purchase intention for energy-efficient household appliances. The study extends the Stimulus – Organism – Response model by incorporating green perceived value and customer trust as mediators between external stimuli and consumers’ behavioral responses. Data was collected from 287 household heads in Vietnam and analyzed by applying partial least squares structural equation modeling. Results show that media publicity, online product reviews, green perceived value, and customer trust all significantly correlate with consumers’ purchase intention. Results further indicate that green perceived value mediates the relationship between media publicity, online product reviews and consumers’ purchase intention. Moreover, customer trust in energy-efficiency labels is a significant mediator between media publicity, online product reviews and consumers’ purchase intention. The research offers theoretical and practical insights into green consumption and the promotion of energy efficiency in emerging markets.

Enhancing smart home appliance recognition with wavelet and scalogram analysis using data augmentation

The development of smart homes, equipped with devices connected to the Internet of Things (IoT), has opened up new possibilities to monitor and control energy consumption. In this context, non-intrusive load monitoring (NILM) techniques have emerged as a promising solution for the disaggregation of total energy consumption into the consumption of individual appliances. The classification of electrical appliances in a smart home remains a challenging task for machine learning algorithms. In the present study, we propose comparing and evaluating the performance of two different algorithms, namely Multi-Label K-Nearest Neighbors (MLkNN) and Convolutional Neural Networks (CNN), for NILM in two different scenarios: without and with data augmentation (DAUG). Our results show how the classification results can be better interpreted by generating a scalogram image from the power consumption signal data and processing it with CNNs. The results indicate that the CNN model with the proposed data augmentation performed significantly higher, obtaining a mean F1-score of 0.484 (an improvement of +0.234), better than the other methods. Additionally, after performing the Friedman statistical test, it indicates that it is significantly different from the other methods compared. Our proposed system can potentially reduce energy waste and promote more sustainable energy use in homes and buildings by providing personalized feedback and energy savings tips.

From buildings to cities: How household demographics shape demand response and energy consumption

In the face of rapidly evolving energy landscapes, driven by increasing integration of renewable energy and the transition towards a more sustainable grid, the role of demand response (DR) programs in maintaining grid stability and efficiency has become increasingly critical. This study presents an in-depth methodology for assessing the nexus between household demographics and engagement in demand response (DR) programs, investigating their impact on energy consumption and financial outcomes at both building and urban scales. Six household archetypes are defined: single working individual, single retired individual, working couple, retired couple, nuclear family, and lone parent household. Additionally, three energy-use archetypes— austere, average, and wasteful—are considered. Employing a stochastic load profile generator, the study examined variables like annual occupancy hours, thermal energy needs, and appliance consumption. In a Canadian residential context, a shift from a single working adult to a nuclear family occupant archetype led to an increase in annual occupancy by 24%, causing a 21% fluctuation in Energy Use Intensity (EUI). Additionally, diverse consumption behaviors led to a 41% EUI variance. The study evaluated load curtailment capabilities across various household archetypes; notably, a ‘wasteful’ household could reduce their load by 32% through a 2 °C-thermostat adjustment during DR events, and this rose to 54% with pre-heating. At the urban scale, demographic shifts substantially influence load profiles and cause variable peak load times. Areas with more retirees have higher mid-day energy usage. The research quantified DR benefits in load reduction and fiscal gains. By raising DR participation from 10% to 50%, load curtailment grew by 69% in areas with younger families. Pre-heating further enhanced load curtailment by 12% and reduced energy rebound by 4%. Economically, ‘wasteful’ households could accumulate 11% more credits compared to average homes. This research introduced a flexible framework for incorporating household demographics and energy use behavior into DR studies, which can be adapted and re-applied to different case studies based on the availability of relevant data.

FIKElectricity: A Electricity Consumption Dataset from Three Restaurant Kitchens in Portugal

Industrial Kitchens are very energy-intensive businesses, consuming between five and seven times more energy per square meter than other commercial spaces like office spaces and retail stores. Still, very little research has been carried out on improving the energy efficiency of this industry. This paper presents the FIKElectricity dataset, a collection of electricity data from three Portuguese restaurant kitchens during their daily operation. The duration of the datasets spans three to four weeks in each industrial kitchen and comprises aggregated and individual appliance consumption, collected at 1 Hz and frac{1}{5} Hz, respectively. The public release of FIKElectricity is expected to draw more attention from the research community to this overlooked industrial sector. The data collection and post-processing methods are thoroughly described in this paper, as well as the dataset organization. Examples showing the dataset’s quality and instructions for its reuse are also provided.

Design of an Economical Intelligent Frequency Meter

Abstract: Vigilantly monitoring the energy consumption of electrical appliances is a crucial aspect of effectively controlling energy usage and expenditures, hence optimizing financial and temporal resources. The utilization of electrical devices can result in various modifications to the characteristics of an electrical power source. Various aspects of an electrical power source undergo modifications when employed for the purpose of powering devices. There have been alterations in the items that necessitate documentation beside the cumulative measurement of meters. This paper discusses the design and implementation of an Arduino-based frequency meter specifically tailored for the measurement of frequency in a three-phase synchronous generator. Additionally, this method was employed to identify instances of malfunction in a three-phase alternating current generator, encompassing both low and high frequencies. The frequency meter that was suggested displayed the measured parameters and the protection condition of each data point directly on the liquid crystal display (LCD) screen. Additionally, a USB cable was employed to facilitate the printing of the Arduino software program in serial mode on a personal computer. A smart monitoring system was developed for various working circumstances using a Bluetooth module. The ZMPT101b AC voltage sensor has been utilized in conjunction with the frequency meter. The frequency was determined by analyzing the signal obtained from the AC voltage sensor, without the need for additional equipment such as a zero crossing detector.

General Overview and Proof of Concept of a Smart Home Energy Management System Architecture

This paper proposes and implements a smart architecture for Home Energy Management Systems (HEMS) that enables interoperability among devices from different manufacturers. This is achieved through the use of standardized elements and the design of an innovative middleware. The system comprises a control unit that communicates with smart outlets using the Wireless Smart Ubiquitous Network (WI-SUN) Home Area Network (HAN) specification, while smart metering is achieved using the WI-SUN Field Area Network (FAN) specification. To manage important data, a web platform and mobile app were created. Additionally, machine learning techniques are utilized to identify energy consumption of individual appliances when only the aggregate energy consumption of the house is available. The architecture presented here supports real-time control of energy use and generation through HEMS, and new devices can be added transparently. Finally, a comparison of the proposed system with similar systems in literature highlights its many advantages in terms of functionality.

Standby energy consumption and saving potentials in the residential sector in tropical areas: the Caribbean island Curaçao as a case study

Rising levels of carbon dioxide (CO2) are of significant concern in modern society, as they lead to global warming and consequential environmental and societal changes. The standby energy consumption of appliances in households is considerable and can be up to 15% of the appliance energy consumption in the residential sector. Overall, standby energy consumption is accountable for roughly 1% of total global CO2 emissions. When we contrast this impact on global CO2 emissions with the impact of the transportation sector, standby energy’s contribution is minimal. The transportation sector is responsible for about 24% of the global CO2 emissions arising from the combustion of fuel. Nevertheless, a significant reduction in standby energy consumption in the residential sector is crucial to reduce the CO2 footprint accordingly. This paper is among the first to assess the magnitude of standby energy consumption and to explore options for reducing standby energy consumption in the Caribbean. The Caribbean island Curaçao was taken as a first case study. Based on a field study of 20 households, the standby energy consumption of about 300 appliances were measured. It was estimated that about 8% of residential electricity consumption was linked to standby energy consumption. The average standby power of the 20 households in Curaçao is 50.3 W. Past research shows that it may be possible to reduce the estimated standby energy consumption by approximately 43%, which is about 6.4 MWh/year in Curaçao. Besides adopting technical solutions, the intended reduction of standby energy consumption can be realized through the introduction of import regulations to favor the selling of appliances with lower standby energy consumption and the creation of public awareness through TV and other media campaigns.

Implementation of a Real-Time IoT Based Energy Management System

Energy Management System has received a lot of attention from both academia and industries due to the rate of energy consumed by electrical appliances. These industries are faced with inflation in the cost of electrical bills. Therefore, this study presents the design and implementation of a real-time IoT based Energy Management System (EMS) to reduce the inflation in the cost of these electrical bills. The system comprises four (4) basic units namely; the sensing unit, control unit, display unit and switching unit. The sensing unit comprises of two sensors namely; Current Transformer (CT) sensor and voltage sensor which senses and measures the parameters (voltage and current) and sends the parameters to the control unit. This control unit comprises two (2) microcontrollers namely; Arduino Nano and ESP32. The Arduino Nano processes the received data from the sensing unit and sends the processed data to the display unit and ESP32 microcontroller for real-time monitoring. In addition, the ESP32 microcontroller sends the processed data to the cloud. Furthermore, the web-based application receives the measured parameters from the cloud and thus, allowing the user to monitor as well as control the rate of energy consumption of electrical appliances. The experimental results obtained showed that the developed system efficiently monitors and control the rate of energy consumption of the three applied loads (Edison 2.7 bulb, standing fan and phone charger). Hence, reduces the cost of electrical bills consumed by these appliances.

Forecasting household energy consumption based on lifestyle data using hybrid machine learning

Household lifestyle play a significant role in appliance consumption. The overall effects are that, it can be a determining factor in the healthy functioning of the household appliance or its abnormal functioning. The rapid growth in residential consumption has raised serious concerns toward limited energy resource and high electricity pricing. The propose 134% electricity tariffs adjustment by Electricity Company of Ghana (ECG) at the heat of economic hardships caused by Covid-19 has raised serious public agitation in Ghana (west Africa) . The unpredictable lifestyle of residential consumers in an attempt to attain a comfortable lifestyle and the rippling effects of population growth burdens energy demand at the residential sector. This study attempts to identify the lifestyle factors that have great influence on household energy consumption and predict future consumption of the household with mitigating factors to cushion the effects on high consumption. The study is based on lifestyle data using hybrid machine learning. The hybrid model achieved high accuracy (96%) as compared to previous models. The hybrid model performance was evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE) and correlation coefficient (R) metrics.

Integrating non-intrusive load monitoring based on graph-to-point learning into a self-adaptive home energy management system

Non-intrusive load monitoring (NILM) can extract the detailed consumption of target appliances from mains reading of a building, which is of great significance for consumers to optimize their electricity consumption and participate in demand response programs. Due to the fact that appliances with multiple states and complex working patterns often bring the most challenging obstacles in energy disaggregation, conventional deep learning methods tend to lead to unsatisfactory disaggregation results. Hence, a novel graph-to-point learning is proposed, in which residual graph convolutional network (ResGCN) and dual attention bidirectional long short-term memory (DABiLSTM) are combined to inherently capture the characteristics of time dependency. Then, accurate NILM results are integrated into a user-centric and self-adaptive home energy management system (HEMS), where a discrete Bayesian network for each appliance is adopted to automatically describe the end-users’ consumption behaviors. Lastly, the proposed algorithm is compared with state-of-the-art methods based on REFIT and REDD datasets for performance validation. Experiments results show that compared with traditional HEMS, NILM-based HEMS can markedly reduce operation cost by 53% and improve the overall consumer comfort by 31%.

A Novel Schematic Design of Enhanced State Gated Transformers for Energy Disaggregation of Nonintrusive Load Monitoring

Non-intrusive load monitoring (NILM) is a technique for estimating the energy consumption of individual appliances without the need for direct metering. In this letter, we examine the attention mechanism of transformers and present two transformer-based NILM approaches, named State Gated Transformer (SGT) and enhanced State Gated Transformer (eSGT) to learn complex non-linear relationships between aggregate and individual power consumption of appliances. The proposed SGT employs parallel self-attention networks, whereas the proposed eSGT enhances the former with a cross-coupled pair structure. We present the schematic design of transformers which allows for concise characterization of the attention mechanism, inspiring the exploration of novel designs at the circuit level. Our experimental results demonstrate the effectiveness and versatility of the proposed approach for real-world NILM datasets.

Optimization of the electricity consumption behaviors of users under uncertain electricity prices and consumption patterns

Real-time electricity prices and customer demands are inextricably linked in the smart grid environment. For the purpose of reducing grid load peaks, enhancing grid security, and optimizing customers’ electricity bills, it is critical to model dynamic real-time electricity prices and flexible customer requests, along with providing an optimized plan to guide users’ decisions regarding the use of household appliances and quantify their needs. The solution to this problem, however, is not straightforward due to the following factors. Firstly, the real-time electricity price is constantly changing, so it must be estimated from grid data associated with it. Furthermore, there are often some variations in the actual power consumption of appliances due to the uncertainty of human behavior, the characteristics of appliances, and the influence of the environment. In addition, when optimizing, serious problems such as tripping and circuit overload must be taken into account. Therefore, considering random electricity prices and random electricity consumption, in the paper, we present the models to simulate real-time changes in electricity prices and energy consumption behaviors, as well as a method to help users optimize their use of household appliances and quantify their requirements with lower electricity cost. By analyzing the experimental results, we demonstrate that our work can be used to provide a scheduling scheme for appliances that minimizes electricity costs and reduces grid pressure.

Telegram Application for Monitoring, Controlling and Protecting the Consumption of Household Electrical Appliances

Nominal electricity rates always increase, so it is necessary to take steps to save electricity consumption. One of the steps to save electricity consumption is taken by implementing a remote electrical equipment control system based on the Smart Home Internet of Things (IoT) system. Besides being able to save money, IoT systems are also able to help simplify work and human activities. This research aims to implement Internet of Things (IoT) equipment to monitor, control, protect, and On/Off timers for household electrical appliances monitored by the Telegram application and 16×2 LCD. Monitored system parameters i.e. voltage, current, frequency, power, energy, and electricity rates for each device. Device hardware i.e. Arduino Uno, 1 Channel Relay, PZEM-004t Voltage Sensor, NodeMCU ESP8266, and Current Sensor (Current Transformer). The equipment has been successfully tested on 10 different household appliances i.e. charged cellphones, fans, charged laptops, solder, iron, fluorescent lamp 36 W, television (TV), set-top box (STB)-TV, printer, and 5 W bulb lamp. The results showed that the prototype is capable of monitoring, controlling, protecting, and On/Off timer settings as well as being able to be monitored from home electric appliances both near and far using the 16×2 LCD and the Telegram application, respectively. The sensor tool using the Telegram application is able to measure/monitor the parameters of voltage, current and frequency at 10 loads with an average error value of 0.32%, 12.83% and 0.1% respectively compared to measurements using a Multimeter. The IoT prototype is also able to provide more complete, faster (4-5 sec) monitoring, control, protection, timer On/Off parameters and superior performance compared to devices designed by a number of previous researchers.

Apply Graph Signal Processing on NILM: An Unsupervised Approach Featuring Power Sequences.

As a low-cost demand-side management application, non-intrusive load monitoring (NILM) offers feedback on appliance-level electricity usage without extra sensors. NILM is defined as disaggregating loads only from aggregate power measurements through analytical tools. Although low-rate NILM tasks have been conducted by unsupervised approaches based on graph signal processing (GSP) concepts, enhancing feature selection can still contribute to performance improvement. Therefore, a novel unsupervised GSP-based NILM approach with power sequence feature (STS-UGSP) is proposed in this paper. First, state transition sequences (STS) are extracted from power readings and featured in clustering and matching, instead of power changes and steady-state power sequences featured in other GSP-based NILM works. When generating graph in clustering, dynamic time warping distances between STSs are calculated for similarity quantification. After clustering, a forward-backward power STS matching algorithm is proposed for searching each STS pair of an operational cycle, utilizing both power and time information. Finally, load disaggregation results are obtained based on STS clustering and matching results. STS-UGSP is validated on three publicly accessible datasets from various regions, generally outperforming four benchmarks in two evaluation metrics. Besides, STS-UGSP estimates closer energy consumption of appliances to the ground truth than benchmarks.

A Wasserstein-based distributionally robust neural network for non-intrusive load monitoring

Non-intrusive load monitoring (NILM) is a technique that uses electrical data analysis to disaggregate the total energy consumption of a building or home into the energy consumption of individual appliances. To address the data uncertainty problem in non-intrusive load monitoring, this paper constructs an ambiguity set to improve the robustness of the model based on the distributionally robust optimization (DRO) framework using the Wasserstein metric. Also, for the hard-to-solve semi-infinite programming problem, a novel and computationally efficient upper-layer approximation is used to transform it into an easily solvable regularization problem. Two different data feature extraction methods are used on two open-source datasets, and the experimental results show that the proposed model has good robustness and performs better in identifying devices with large fluctuations. The improvement is about 6% compared to that of the convolutional neural network model without the addition of distributionally robust optimization. The proposed method supports transfer learning and can be added to the neural network in the form of a single-layer net, avoiding unnecessary training times, while ensuring accuracy.

Impact of circular economy on the decarbonization of the Italian residential sector

Circular economy is a new economic model that aims at overcoming today's linear “take, make, dispose” pattern, decoupling global economic development from finite resource consumption. Household appliances are a promising field for the adoption of Circular Economy, being one of the most resource-intensive industries and due to the global e-waste problem. The aim of this work is to quantify the potential contribution of making the household appliances sector more circular to the decarbonization of the Italian energy system, using the TIMES-RSE model. First, a literature review of the available circular measures was performed and used as a basis for modeling assumptions. The following circular measures were considered: extending the lifetime of appliances, increased adoption of remanufactured appliances, growing importance of leasing, lower average washing temperature for washing machines, and lower usage of tumble dryers. Second, each circular practice was modeled either as a new technology available for the optimization of the system or as a change in demand for a certain commodity. The reference scenario is the Italian Long Term Strategy. It is shown that the modeled circular practices can reduce the energy consumption of household appliances in 2050 by 0,327 Mtoe (−16%) and cut in half their costs through the 2020–2050 period, saving 96 billion euros (−47%). Moreover, savings could be even higher if efforts are taken to remove barriers to their diffusion, including consumers' habits and manufacturers' inertia. As the energy weight of appliances is low within the overall residential sector, energy savings of these circular practices are negligible at the sectoral level, however, economic savings of 107 billion euros are achieved over 2020–2050 (−22%).

Thresholding methods in non-intrusive load monitoring

Non-intrusive load monitoring (NILM) is the problem of predicting the status or consumption of individual domestic appliances only from the knowledge of the aggregated power load. NILM is often formulated as a classification (ON/OFF) problem for each device. However, the training datasets gathered by smart meters do not contain these labels, but only the electric consumption at every time interval. This paper addresses a fundamental methodological problem in how a NILM problem is posed, namely how the different possible thresholding methods lead to different classification problems. Standard datasets and NILM deep learning models are used to illustrate how the choice of thresholding method affects the output results. Some criteria that should be considered for the choice of such methods are also proposed. Finally, we propose a slight modification to current deep learning models for multi-tasking, i.e. tackling the classification and regression problems simultaneously. Transfer learning between both problems might improve performance on each of them.

Application of Neuro-Fuzzy Techniques for Energy Scheduling in Smart Grids Integrating Photovoltaic Panels

In recent years, most of the research in the field of smart grids integrating renewable energy sources assumed energy efficiency as a scheduling objective. However, the aspects of energy consumption or energy demand have not been described clearly, even though they have been proven to be an effective way of reducing energy consumption. In this context, this study aimed to cover a key research challenge in the field, such as the development of an intelligent strategy for solving energy consumption scheduling problems. The added value of our proposal consists of classifying individual consumption profiles assigned to each operation cycle phase, instead of considering an average of non-varying consumption of household appliances. Within this hybrid approach, the proposed explainable system, based on self-organizing maps of neural networks, fuzzy clustering algorithm, and scheduling technics, correlates the complex interrelation between power generated from renewable energy sources in a smart grid, prosumers’ load behaviors, and the consumption profile of controllable or uncontrollable appliances. The tests were made using green energy consumption and production from real monitored data sets. The load-shifting algorithm that was used to reduce energy consumption from the national energy grid proved its effectiveness. In fact, consumers paid 25% less for the energy they used from the national energy grid during the times when the amount of electricity produced from renewable sources was reduced as a result of weather conditions.

Home Energy Management System Based on Genetic Algorithm for Load Scheduling: A Case Study Based on Real Life Consumption Data

This paper proposes a home energy management system able to achieve optimized load scheduling for the operation of appliances within a given household. The system, based on the genetic algorithm, provides recommendations for the user to improve the way the energy needs of the home are handled. These recommendations not only take into account the dynamic pricing of electricity, but also the optimization for solar energy usage as well as user comfort. Historical data regarding the times at which the appliances have been used is leveraged through a statistical method to integrate the user’s preference into the algorithm. Based on real life appliance consumption data collected from a household in Morocco, three scenarios are established to assess the performance of the proposed system with each scenario having different parameters. Running the scenarios on the developed MATLAB script shows a cost saving of up to 63.48% as compared to a base scenario for a specific day. These results demonstrate that significant cost saving can be achieved while maintaining user comfort. The addition of supplementary shiftable loads (i.e., an electric vehicle) to the household as well as the limitations of such home energy management systems are discussed. The main contribution of this paper is the real data and including the user comfort as a metric in in the home energy management scheme.