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The distribution system's economic operation is significantly impacted by the management of distributed generation (DG) resources, energy storage (ES), and controllable loads. The paper employs a smart distribution system that incorporates dispatchable and non‐dispatchable DG resources, as well as battery storage, in addition to the demand response (DR) scheme. New modelling was performed in hourly steps to achieve the optimal unit commitment. In smart homes, appliances are prioritized and classified into four types: adjustable, interruptible, shiftable, and uncontrollable loads. Load reduction in smart homes is also considered based on load prioritization and customer participation in the DR scheme to achieve the proposed scheme's objectives. The proposed method considers costs associated with microturbines (MTs), including manufacturing, start‐up, shutdown, and pollution. Additionally, planning is conducted to purchase or sell electricity to the upstream network. The simulation is run on an IEEE 33‐bus system to demonstrate the proposed method's effectiveness. The system is assumed to be capable of operation in both island and grid‐connected modes. The results demonstrate the proposed approach's efficacy in load reduction, operation cost, and execution time. [ABSTRACT FROM AUTHOR]

The development of Smart Home Controllers has seen rapid growth in recent years, especially for smart devices, that can utilize the Internet of Things (IoT). However, a large portion of the household devices and appliances already in use, are not IoT enabled, and therefore, requires their default control mechanisms for the devices to operate. This paper proposes a smart appliance controller that uses Augmented Reality, MQTT, and other up-to-date platforms to control aftermarket home appliances in the most efficient manner. The proposed work integrates mobile AR with IoT, to control household appliances, with the help of infrared (IR) signals. The characteristics of the system are evaluated through a series of tests and performance measures. The results of the test highlight the quick response time of MQTT for the implementation of a Home Automation System, when compared to the request–reply protocol: CoAP (4 times as fast). [ABSTRACT FROM AUTHOR]

This paper investigates the challenge of finding and analyzing security vulnerabilities among widely available low-budget Internet of Things smart home appliances. It considers the identification of security vulnerabilities within the appliances' embedded software and connectivity functions over wired and wireless channels in local networks and external communications with manufacturers' cloud services. To analyze the security of these appliances, a universal laboratory test bench is proposed and a set of methodologies for testing the security of smart home devices is described. The proposed testing platform offers a practical solution for security analysis of Internet of Things smart home devices and it can serve as a reference approach for future research. The results from the research indicated varying levels of susceptibility across different types of devices. A list of recommendations for manufacturers and others to improve the security level of these appliances is provided. The findings emphasize the need for regular security assessments of smart home devices, to maintain the protection of personal and sensitive information. [ABSTRACT FROM AUTHOR]

With the widespread adoption of applications and IoT devices, modern society has come to rely on them in various aspects of daily life. These applications and devices cover a range of needs, including home appliances and even medical devices for body monitoring. Because a significant amount of data generated by IoT devices and applications must be transmitted over networks, particularly the Internet, there is an increased risk of cyber-attacks on this transmitted data. This data can be text, images, sound, or other forms, and it is critical to ensure its protection, especially if it contains valuable information. Extensive research has been conducted on various encryption algorithms to ensure the security of transmitted data, whether through block ciphers or stream ciphers. In addition, efforts have been made to increase encryption efficiency by securing the transmission channels. The question of this research is “what is the state of the art in the lightweight encryption in terms of strength and speed up the process?”. This paper provides a comprehensive survey of research related to two main categories of lightweight encryption, and work focused on secure authentication between entities. The paper includes an analysis of the techniques used in each surveyed paper and highlights their main results. [ABSTRACT FROM AUTHOR]

Energy harvesting models are the focus of most countries, given that governments are becoming aware of the limitations of natural resources and the need to optimize them. On the other hand, different systems used in everyday life and in industrial environments involve the use of heat cycles, but in most cases, their thermoelectric energy is not recovered from these processes. Accordingly, this paper proposes to implement a model based on a low-cost Peltier array that can be attached to commonly used devices with heat cycles involving small temperature differences (ΔT = 25 ℃). A maximum power point tracking (MPPT) method was used to extract the maximum power from this array. This device is thought to take advantage of home and industrial elements' heat to power low-power system applications. The results show that this technology allows acceptable use and represents an effective recovery mechanism. This work represents a new approximation of the energy harvesting solutions from thermoelectric energy with future benefits, especially on the Internet of Things (IoT) applications, which has been one of the technology areas of most significant expansion and growth in recent decades. The IoT has opened significant challenges in the scientific community, especially regarding the energy supply methods of the IoT elements or nodes, considering that these elements can be located in places where it is impossible to wire to supply power and that use of batteries is unsustainable in the long term, also generating a negative environmental impact. The proposed system harvests energy from the temperature difference generated at a window, considering that the device is controlled environment within a room. [ABSTRACT FROM AUTHOR]

Copyright of Romanian Journal of Civil Engineering / Revista Română de Inginerie Civilă is the property of Matrix Rom and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To support the 2021 Manufacturing & Service Operations Management (M&SOM) Data-Driven Research Challenge, RiRiShun Logistics (a Haier group subsidiary focusing on logistics service for home appliances) provides MSOM members with logistics operational-level data for data-driven research. This paper provides a detailed description of the data associated with over 14 million orders from 149 clients (the consigners) associated with 4.2 million end consumers (the recipients and end users of the appliances) in China, involving 18,000 stock keeping units operated at 103 warehouses. Researchers are welcomed to develop econometric models, data-driven optimization techniques, analytical models, and algorithm designs by using this data set to address questions suggested by company managers. History: This paper has been accepted as part of the 2021 M&SOM Data-Driven Research Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72091215/72091210, 71871207, 71991464/71991460, and 71921001]. Supplemental Material: Data and the online appendix are is available at https://doi.org/10.1287/msom.2021.0994. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Home appliances connected to Internet-of-Things (IoT) platforms have been extensively installed in smart homes. In this context, home energy management systems (HEMSs) have emerged as a viable solution for reducing energy costs. Although several studies have analyzed the implementation of HEMSs, a majority of these studies were based on the installation of numerous sensors. Owing to the complexity and costs associated with the installation of multiple sensors, implementation of HEMSs in smart homes is challenging. This paper presents an energy management scheme for an HEMS that minimizes the energy cost in a smart home with data obtained from IoT-based appliances typically used in a smart home. Case studies were conducted to demonstrate the effectiveness of the proposed method. Prototype software (version 11) based on the proposed method for a HEMS and a test house with IoT-based appliances were also implemented in the case studies. [ABSTRACT FROM AUTHOR]

A novel ensemble machine learning approach for predicting energy consumption in smart appliances is presented in this paper. The main objective is minimizing the number of necessary sensors and improving the prediction accuracy at the same time. The proposed method combines the Fuzzy C-Means method with a multi-stream deep neural network to achieve this goal. The method focuses on prediction accuracy and aims to extract the minimum number of essential features from the dataset corresponding to the required sensors. These selected features are then scatter-reduced and homogenized into subsets. Each subset is used to train a cluster-specific deep neural network designed exclusively for that subset. The final prediction is obtained by computing the fuzzy-weighted sum of these cluster-specific network outputs. Numerical results show that the proposed prediction method outperforms conventional methods in terms of root mean square error and mean absolute percentage error criteria, despite using fewer sensors. This improvement can be attributed to the reduced dataset scatter, which improves the learning speed and model performance. Furthermore, the fuzzy combination of the outputs improves the final prediction accuracy. Overall, the proposed approach provides a more cost-effective and accurate solution for predicting the energy consumed by smart appliances. [ABSTRACT FROM AUTHOR]

Research on automated domestic appliances, categorized as Smart Home Technology (SHT), has increased exponentially over the last decade and has taken various guises, from qualitative descriptive investigation to empirically based analysis. Given the unresolved uncertainties surrounding the SHT acceptance literature and concern regarding the relatively low smart home device uptake, there is a need to reappraise the existing literature to delve deeper and search for solutions. Based on the research method PRISMA, a systematic literature review on SHT acceptance was undertaken to evaluate its different models and develop a hypothetical model. Twenty-three papers were selected in the review, and the results indicate that the Technological Acceptance Model was the most applied model when investigating SHT acceptance. Moreover, the most significant variables used to measure SHT acceptance were compatibility and perceived usefulness. The systematic literature review also revealed some significant patterns including the uptake of non-Western research and the use of sales and market share as a metric of SHT acceptance. Future directions on how researchers, smart home developers and governmental agencies can utilize the findings conclude the systematic review. [ABSTRACT FROM AUTHOR]

Robot vacuum cleaners have gained widespread popularity as household appliances. One significant challenge in enhancing their functionality is to identify and classify small indoor objects suitable for safe suctioning and recycling during cleaning operations. However, the current state of research faces several difficulties, including the lack of a comprehensive dataset, size variation, limited visual features, occlusion and clutter, varying lighting conditions, the need for real-time processing, and edge computing. In this paper, I address these challenges by investigating a lightweight AI model specifically tailored for robot vacuum cleaners. First, I assembled a diverse dataset containing 23,042 ground-view perspective images captured by robot vacuum cleaners. Then, I examined state-of-the-art AI models from the existing literature and carefully selected three high-performance models (Xception, DenseNet121, and MobileNet) as potential model candidates. Subsequently, I simplified these three selected models to reduce their computational complexity and overall size. To further compress the model size, I employed post-training weight quantization on these simplified models. In this way, our proposed lightweight AI model strikes a balance between object classification accuracy and computational complexity, enabling real-time processing on resource-constrained robot vacuum cleaner platforms. I thoroughly evaluated the performance of the proposed AI model on a diverse dataset, demonstrating its feasibility and practical applicability. The experimental results show that, with a small memory size budget of 0.7 MB, the best AI model is L-w Xception 1, with a width factor of 0.25, whose resultant object classification accuracy is 84.37%. When compared with the most accurate state-of-the-art model in the literature, this proposed model accomplished a remarkable memory size reduction of 350 times, while incurring only a slight decrease in classification accuracy, i.e., approximately 4.54%. [ABSTRACT FROM AUTHOR]

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China. [ABSTRACT FROM AUTHOR]

Purpose: Considering the problems faced by the home appliance industry in Iran, such as the increase in waste, lack of information transparency and lack of traceability of manufactured products, etc. the companies in the home appliance industry are moving toward Industry 4.0 and have been prompted to use it. On the other hand, the adoption of Industry 4.0 is associated with challenges that may lead to the failure of the adoption project and the bankruptcy of home appliance manufacturers. This study identified the challenges in the implementation of Industry 4.0 on current status and provided suitable solutions to overcome the identified challenges. Design/methodology/approach: In this study, a review of the literature and background of Industry 4.0 identified the challenges that influence the adoption of Industry 4.0. To measure the current status of the identified challenges, the opinions of experts in the Iranian home appliance industry were used. To find solutions to the challenges in the adoption of Industry 4.0 in the Iranian home appliance industry, a fuzzy cognitive mapping and scenario design were used. Findings: The results of this study show that to face the challenge of data sharing, skilled personnel should be effectively promoted among workers in the home appliance industry. The results of this study also show that the barriers to cooperation should be removed to reduce the impact of the IT Security Concerns challenge. Originality/value: This paper is the first article that identifies the challenges and effective solutions for implementing Industry 4.0 in the home appliance industry. [ABSTRACT FROM AUTHOR]

Reverse logistics networks reintroduce end-of-life products to remanufacturing, which is significant for sustainable development and environmental protection. In this paper, we investigate the current reverse logistics network of household appliances in China and redesign a new network that introduces necessary facilities: disassembly centres and three types of remanufacturing plants to improve recycling rates. A mixed-integer linear programming model with multi-level capacity choices is proposed to determine the locations and capacities for disassembly centres and remanufacturing plants. An efficient decomposition-and-expansion heuristic is developed to solve the model. The redesigned network is evaluated using a real case. The results indicate that the recycling rate is largely improved by the new network in which almost all useful materials can be recycled, whereas, in the traditional network, only metals are efficiently recycled. A sensitivity analysis of model parameters is conducted using the Taguchi method to identify their effects on recycling decisions. The proposed algorithm is further evaluated using a set of randomly generated instances. The results show that the algorithm can yield high-quality solutions within a short time. The best configuration of the algorithm is suggested via sensitivity analysis of parameters using the generated instances. [ABSTRACT FROM AUTHOR]

In the process of recycling, dismantling, and reusing household appliances, implementing extended producer responsibility (EPR) has become increasingly important. Designing a reasonable pricing mechanism for waste household appliance recycling is critical for the implementation of EPR. To address the problem of labor-intensive and experience-dependent traditional manual methods for assessing the value of waste household appliances, in this paper, we propose an evaluation method based on the subtractive clustering method and an adaptive neuro fuzzy inference system (SCM–ANFIS), which outperforms traditional neural networks such as LSTM, BP neural network, random forest and Takagi–Sugeno fuzzy neural network (T–S FNN). Moreover, in this paper, we combine the five aforementioned algorithms to design a combination evaluation model based on maximum ratio combination (CEM–MRC), which can achieve a performance improvement of 0.1% in terms of mean absolute percentage error (MAPE) compared to the suboptimal BP neural network. Furthermore, an enhanced evaluation model based on classification selection (EEM–CS) is designed to automatically select the evaluation results between the optimal SCM–ANFIS and the suboptimal CEM–MRC, resulting in a 0.73% reduction in MAPE compared to the optimal SCM–ANFIS and a 1.42% reduction compared to the suboptimal CEM–MRC. In this paper, we also validate the performance of the proposed algorithms using a dataset of waste television recycling, which demonstrates the high accuracy of the proposed value assessment mechanisms achieved without human intervention and a significant improvement in evaluation accuracy as compared to conventional neural-network-based algorithms. [ABSTRACT FROM AUTHOR]

The advancements in the field of materials and force gadgets, coupled with the increased affordability of high-performance processors, have led to a widespread adoption of Brushless Direct Current (BLDC) motors across various industries. These applications range from household appliances to automotive, aerospace, and medical sectors. The widespread use of may be attributed to its manifold advantages over various types of engines, such as its superior efficiency, robust power output, extended operational lifespan, relatively quiet operation, and broader range of achievable speeds. Due to the increasing adoption of Brushless Direct Current (BLDC) motors in many real-life applications as a replacement for traditional motors, this paper aims to compile and evaluate a comprehensive list of control algorithms employed for BLDC motor control. This study discusses several ways for managing speed and current, including hysteresis band control, variable DC-link voltage control, and Pulse Width Modulation (PWM) control schemes. The optimization of Proportional-Integral-Derivative (PID) gains for these controlling techniques is achieved through the utilization of the particle swarm optimization (PSO) algorithm. By employing Fast Fourier Transform (FFT) analysis to examine the controller behavior through frequency analysis of the output signals and calculating the Total Harmonic Distortion (THD), a more advantageous control method may be determined. [ABSTRACT FROM AUTHOR]

Nowadays, various interfaces are used to control smart home appliances. The human and smart home appliances interaction may be based on input devices such as a mouse, keyboard, microphone, or webcam. The interaction between humans and machines can be established via speech using a microphone as one of the input modes. The Speech-based human and machine interaction is a more natural way of communication in comparison to other types of interfaces. Existing speech-based interfaces in the smart home domain suffer from some problems such as limiting the users to use a fixed set of pre-defined commands, not supporting indirect commands, requiring a large training set, or depending on some specific speakers. To solve these challenges, we proposed several approaches in this paper. We exploited ontology as a knowledge base to support indirect commands and remove user restrictions on expressing a specific set of commands. Moreover, Long Short-Term Memory (LSTM) has been exploited for detecting spoken commands more accurately. Additionally, due to the lack of Persian voice commands for interacting with smart home appliances, a dataset of speaker-independent Persian voice commands for communicating with TV, media player, and lighting system has been designed, recorded, and evaluated in this research. The experimental results show that the LSTM-based voice command detection system performed almost 1.5% and 13% more accurately than the Hidden Markov Model-based one, in scenarios 'with' and 'without ontology', respectively. Furthermore, using ontology in the LSTM-based method has improved the system performance by about 40%. [ABSTRACT FROM AUTHOR]

This paper presents a detailed study of the energy savings that can be achieved through residential appliances by implementing conservation voltage reduction (CVR). The computed energy savings are derived from the ZIP coefficients of a load model (static) under varying voltage conditions that represent the power consumed by each appliance tested. Multiple studies have shown energy savings at the substation level but not at the level of a specific residence and appliance; hence, the latter is the focus of this analysis. The appliances are determined based on the type of heating and cooling loads, and the daily duration of use for each appliance, which contributes to the total monthly consumption that is billed for a household are included in this study. Energy savings in Malaysia has been a focus of many current studies, and this study is the first attempt to achieve energy savings through the implementation of CVR. No published work has compared individual appliances that truly benefits from CVR, and this is addressed in this study. The main contribution of this study is that it provides very detailed and measured data that are used to analyze and generate energy savings for very specific residential appliances intended to meet every 1% voltage drop. The load model is developed for the common household appliances in Malaysia, and then a voltage reduction study is applied to the appliances as well as the Malaysian distribution networks. Here, we also provide insight for performing voltage reduction at an electric vehicle (EV) charging station in Malaysia. The results of this study should serve as a foundation for all practicing utilities engineers to address the issue of CVR in Malaysia. The CVR factors for residential, commercial, and industrial networks in Malaysia are also published in this paper. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCIs) have a potential to revolutionize humancomputer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs--e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, "within-paradigm transfer learning," aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, "cross-paradigm transfer learning," involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments. [ABSTRACT FROM AUTHOR]

This paper introduces the first completely unsupervised methodology for non-intrusive load monitoring that does not rely on any additional data, making it suitable for real-life applications. The methodology includes an algorithm to efficiently decompose the aggregated energy load from households in events and algorithms based on expert knowledge to assign each of these events to four types of appliances: fridge, dishwasher, microwave, and washer/dryer. The methodology was developed to work with smart meters that have a granularity of 1 min and was evaluated using the Reference Energy Disaggregation Dataset. The results show that the algorithm can disaggregate the refrigerator with high accuracy and the usefulness of the proposed methodology to extract relevant features from other appliances, such as the power use and duration from the heating cycles of a dishwasher. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) technology, crucial for intelligent electricity management, has gained considerable attention in residential electricity usage studies. NILM enables monitoring of total electrical current and voltage in homes, offering insights vital for enhancing safety and preventing domestic electrical accidents. Despite its importance, accurately discerning the operational status of appliances using non-intrusive methods remains a challenging area within this field. This paper presents a novel methodology that integrates an advanced clustering algorithm with a Bayesian network for the identification of appliance operational states. The approach involves capturing the electrical current signals during appliance operation via NILM, followed by their decomposition into odd harmonics. An enhanced clustering algorithm is then employed to ascertain the central coordinates of the signal clusters. Building upon this, a three-layer Bayesian network inference model, incorporating leak nodes, is developed. Within this model, harmonic signals are used as conditions for node activation. The operational states of the appliances are subsequently determined through probabilistic reasoning. The proposed method's effectiveness is validated through a series of simulation experiments conducted in a laboratory environment. The results of these experiments (low mode 89.1%, medium mode 94.4%, high mode 92.0%, and 98.4% for combination) provide strong evidence of the method's accuracy in inferring the operational status of household electrical appliances based on NILM technology. [ABSTRACT FROM AUTHOR]

Deep drawing is an operation to transform flat or hollow blanks to create hollow parts of the required shape and size. Deep drawing is an essential operation in sheet-forming technology to manufacture hollow parts. These parts are commonly used in the automobile, aviation, and household appliances industries. To prevent wrinkles on the rim part, a blank holder will be used to compress and flatten the workpiece before the metal is pulled into the die by the punch. Deep drawing without using workpiece holding force is applied to form hollow parts with low depth. In this case, the mould has a simple structure, helping to reduce manufacturing time and costs and making it easier to maintain and repair. The radius of the die is an essential parameter in deep drawing; it greatly affects the quality of products (making wrinkles and tears). However, this study has shown that, in deep drawing without using a blank holder, the cone angle of the die α has a major influence on the quality of the body and rim of the products, such as the products may or may not have wrinkles, either being thinned or thickened. This cone angle also affects the drawing force. This study has determined that, with α>120°, wrinkling begins to appear in the part wall, and wrinkling tends to increase as the angle α increases. The cone angle of the die for quality products is in the range of 100° < α ≤ 120°. The taper angle smaller than 120° simultaneously increases the quality of the products and the cost of manufacturing die, so the most optimal value is determined by simulation and experimental verification as α = 120°. The results of this study can be applied to fabricate hollow cylindrical parts with similar shapes when using the deep drawing method without using the blank holder. [ABSTRACT FROM AUTHOR]

Whereas the literature on rebates has focused on single-product firms, this paper analyses the role of rebates offered by a multiproduct firm Rebates have long been recognized as a price discrimination mechanism. When firms offer an extensive product line, an important managerial question concerns the choice of which products to promote. This paper finds that a multiproduct firm leverages rebates to screen consumers on the basis of their redemption costs, which enables it to exercise price discrimination via the product line more effectively. In particular, because consumers who use rebates tend to have lower valuations, the classic cannibalization concern is less severe in the product line offered to rebate users. Therefore, the lowest-quality product that is promoted with a rebate is of higher quality than the lowest-quality product that is offered without rebate. Hence, the firm does not promote its lowest-quality products. Instead, by promoting some products with rebates, the firm drives some low-valuation consumers to trade up to higher-quality products. Notably, it is found that, contrary to the commonly held view, rebates can serve a price discrimination role even when redeemed by all consumers. Therefore, this paper proposes a novel price discrimination explanation for large rebates, sometimes in the order of several hundreds of dollars, that are frequently used in industries such as consumer electronics and household appliances. History: Yuxin Chen served as the senior editor for this article. Supplemental Material: The e-companion is available at https://doi.org/10.1287/mksc.2023.1430. [ABSTRACT FROM AUTHOR]

The first aim of this paper was to determine the variability in the signal characteristics and psychoacoustic data of canister-type vacuum cleaners. Fifteen vacuum cleaners with different sound power levels, provided by the manufacturers, were selected as test units to calculate their acoustic and psychoacoustic parameters. The selection of the devices was based on an even distribution of the reported sound power levels. The investigated variability in the acoustic and psychoacoustic parameters on different vacuum cleaners was discussed to derive the common characteristics of canister-type vacuum cleaner noise. The derived common characteristics were compared with the those in the available literature on the noise generation mechanisms of vacuum cleaners. Based on these characteristics, prototypical vacuum cleaner noise was defined. The second aim of this paper was to understand the annoyance perception of vacuum cleaner noise. Annoyance assessments were obtained from two sets of listening experiments. The first listening experiment was conducted to find the correlates of annoyance evaluations. Loudness, sharpness and tonal components at lower and higher frequencies were found to be dominant correlates of vacuum cleaner noise annoyance estimations. In the second listening experiment, a possible interaction between loudness and sharpness was investigated in different listening test methods. The selected loudness and sharpness values for this experiment were consistent with the observed ranges in the first part. No significant interaction between loudness and sharpness was observed, although each separately correlated significantly positively with annoyance. [ABSTRACT FROM AUTHOR]

This paper utilizes an IoT platform called IoTtalk to shape "intelligence" into a house to make it a smart home. The developed project is called HomeTalk that serves as a platform to accommodate various smart applications in a house. We describe the following HomeTalk applications. The PlantTalk application takes care of house plants. The FishTalk application provides fish comfortable life in the fish tank at home. The BreathTalk application detects the number of people in a room, which also purifies the air. The TheaterTalk application uses home and special appliances to create the effects for a 4D experience theater at home. The FrameTalk application allows a painting frame to interact with people in a house. The GardenTalk application provides smart gardening. By examining these projects, this paper explicitly identifies and generalizes the features of HomeTalk that provides flexibility to reuse these IoT devices by sharing them among various smart home applications simultaneously. Specifically, we show how these applications share the sensors and actuators in the house. In the future, we will integrate them through an award winning project called Orchid House. [ABSTRACT FROM AUTHOR]

Purpose: models of reverse logistics (RL) that consider the sustainable development goals (SDG) and circular economy activities (CEA) are increasingly appearing in the literature. Intention of the article is to show at what stage of implementation of the SDG and CEA the home appliance industry (HAI) is currently at and what challenges it faces in terms of implementing the RL model. Design/methodology/approach: The paper shows that the information, data, industry positions and reports presented on the website of the Association of Appliance Manufacturers (APPLiA) show that the HAI is too slow in moving towards the SDG and CEA. It also prevents the implementation of theoretical models of RL. Findings: The article shows that the current actions taken by HAI in Europe and Poland are insufficient to meet the SDG, CEA, and the implementation of full models of RL. Research limitations/implications: The presented research results refer to the data presented by the HAI of Europe and Poland, as well as the information contained on the websites of the APPLiA Europe and Poland. The presented research results can be related to the information of other relevant industries such as automotive, aerospace and others, so that the hypothesis that the industry insufficiently pursues the SDG and CE and does not take strategic steps towards the implementation of models of RL with special consideration of all CE activities. Practical implications: The paper points out that for the home appliance industry to realize the SDG and the CE through the implementation of RL, it must use Design for eXcellence (DfX) tools now with a focus on Design for Circular Economy (DfCE) and Design for Logistics (DfL). Social implications: no social implication. Originality/value: The article presents the author's model of RL and formulates challenges for the industry that are associated with its implementation. It should draw the attention of various industries to the need to take strategic actions for implementation SDG and CEA. [ABSTRACT FROM AUTHOR]

To test electric devices such as household appliances, electric vehicle and other loads, there is need for emulators instead of real devices, which decreases the cost of testing. This paper presents a dynamically programmable load emulator that imitates the current–time characteristic of different types of the loads. The exact mathematical model of various types of electric loads is firstly extracted. These mathematical models are implemented by LabVIEW software. A non-ideal buck converter is used for the hardware part of the emulator, which is controlled by LabVIEW. The proposed emulator can mimic the types of loads given by LabVIEW software with great accuracy and is an example of an actual load simulator with good approximation. Manufacturers or laboratories of electric equipment can use the proposed emulator to perform their tests instead of using the actual device. This reduces the cost of tests and speeds them up so that all types of electric loads can be modeled with an emulator. In addition, because a simple power electronic converter structure is used, the proposed emulator has low complexity and low manufacturing cost. Simulation and laboratory results prove the efficiency of this emulator in making models of different types of loads. [ABSTRACT FROM AUTHOR]

This paper presents the development of advanced adaptive controller for dynamic voltage restorer (DVR) to regulate the power quality connected to a distribution system. Various voltage quality issues have been generated as distorted grid conditions are responsible for abnormal operation of the consumer electronics and domestic household appliances. Therefore dynamic voltage restorer (DVR) is employed for elimination of voltage quality issues in the distribution lines. To improve the performance and efficiency of DVR, an improved robust SRF (IR-SRF) controller has been developed in this paper for the mitigation of voltage quality issues under weak grid conditions. In the developed IR-SRF controller, moving average filter (MAF) has been utilized for better extraction of ripples from the sensed voltage. For better grid voltage signal synchronization with the proposed controller under highly dynamic condition, an enhanced Phase Locked Loop (PLL) scheme has been utilized instead of conventional PLL. For the regulation of DC link voltage under dynamic environmental conditions, MAF based fuzzy tuned PI controller has been utilized with the ability to adapt the situation. The developed controller has been simulated and results for various grid voltage conditions have been analyzed. The obtained simulation results are compared with conventional control algorithm to validate the efficacy. The efficiency of the proposed controller has been validated using the experimental results. [ABSTRACT FROM AUTHOR]

Induction heating (IH) technology is widely recognized and utilized in residential applications due to its high efficiency and safe operating characteristics. Resonant inverter circuits are widely used in IH systems because of their high efficiency and ability to perform soft switching. Among the various resonant inverters used in IH systems, the single-switch quasi-resonant (SSQR) inverter topology is typically preferred for low-cost and low-output-power applications. Despite its cost advantage, the SSQR topology has a relatively narrow soft-switching range, which can be unstable depending on the electrical parameters of the load and the resonant converter circuit. Accurately determining the capacitance value of the resonant capacitor and the inductance value of the induction coil, which are the key circuit elements of the SSQR induction cooker, is crucial for designing a reliable, efficient, and durable cooking system. In other words, there exists a critical relationship between the resonant converter circuit parameters, load characteristics, and safe operating conditions. Additionally, when considering closed-loop control methods used for power control and safety, selecting appropriate resonant circuit elements becomes vital in ensuring both reliable and efficient operation. This paper focuses on a novel and simplified design method for the SSQR inverter utilized in household appliances. The proposed method and its advantages in terms of the safe operating area of the switch are theoretically investigated and verified through simulations and prototype circuits. [ABSTRACT FROM AUTHOR]

Evaluations of nonintrusive load monitoring (NILM) algorithms and technologies have mostly occurred in constrained, artificial environments. However, few field evaluations of NILM products have taken place in actual buildings under normal operating conditions. This paper describes a field evaluation of a state-of-the-art NILM product, tested in eight homes. The match rate metric—a technique recommended by a technical advisory group—was used to measure the NILM's success in identifying specific loads and the accuracy of the energy consumption estimates. A performance assessment protocol was also developed to address common issues with NILM mislabeling and ground-truth comparisons that have not been sufficiently addressed in past evaluations. The NILM product's estimates were compared to the submetered consumption of eight major appliances. Overall, the product had good performance in disaggregating the energy consumption of the electric water heaters, which included both electric resistance and heat-pump water heaters, but only a fair accuracy with refrigerators, dryers, and air conditioners. The performance was poor for cooking equipment, furnace fans, clothes washers, and dishwashers. Moreover, the product was often unable to detect major loads in homes. Typically, two or more appliances were not detected in a home. At least two dryers, furnace fans, and air conditioners went undetected across the eight homes. On the other hand, the dishwasher was detected in all homes where available or monitored. The key findings were qualitatively compared to those of past field evaluations. Potential areas for improvement in NILM product performance were determined along with areas where complementary technologies may be able to aid in load-disaggregation applications. [ABSTRACT FROM AUTHOR]

Carbon reduction programs are being introduced for carbon neutrality and energy transition to clean energy sources in various sectors, such as energy, buildings, transportation, and agriculture. In the residential electricity energy of the energy sector, the time-of-use (TOU) rate plan, which employs dynamic rates depending on energy usage times based on the advanced metering infrastructure (AMI), is being implemented for efficient electricity energy consumption. For broad expansion of the TOU rate plan, customers need information about its benefits, such as potential savings on electricity bills. In this paper, we first analyze the statistical characteristics of electricity energy usage using the metering data collected from 10 apartment complexes through AMI and develop a model to calculate the electricity bill savings. We next introduce examples of major home appliances, of which usage times can be shifted, and offer projected bill savings from the developed model. We analyze the examples from both the perspectives of households and apartment complexes. The information from this analysis is helpful in practically investigating customers' willingness to shift the usage time for a successful implementation of the demand response program. [ABSTRACT FROM AUTHOR]

Home Automation Systems (HAS) are commonly controlled by smartphones and microcontrollers for convenience, efficiency, and security. It integrates smart devices, the Internet of Things (IoT), cloud computing, and rule-based event processing to significantly impact the process of autonomous system development. IoT technology has made smart home applications popular for their access to a variety of data and services. Due to a shortage of current HAS functionality that supports the control of multiple aspects of a home, it is essential to provide an alternative design of HAS. In this paper, a prototype for a smart IoT-based HAS is proposed to control and monitor the parameters of home appliances. The proposed system integrates both IoT and cloud computing technologies to increase the system's performance. It uses Node MCU as a central controlling unit. The system integrates various smart devices, and security alarms, and operates in both manual and remote modes. Solid-state Relay is used to regulate load and switches, and the system includes an irrigation system that autonomously irrigates home gardens. The system can be controlled over IoT cloud service, and watering is made easier by setting parameter thresholds, minimizing water waste, and utilizing water as needed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Washing machines (WMs) are common household appliances that help to save time and effort used in brushing and washing clothes. It is a common practice to use manually operated WMs, and based on their uses, WMs are classified into top and front open cloth washing machines, which operate based on an automatic control mechanism. In this paper, we present the design and simulation of an Arduino fuzzy logic-based WM control system, with an emphasis on improvement in its operating algorithm. Simulations were performed to determine the optimal time the WM takes to wash clothes, the maximum number of clothes the WM can wash per time, the acceptable dirtiness level of cloths, and the type of clothes that the machine can wash. The number of clothes to be washed, the degree of dirtiness, and the type of clothes govern the fuzzy logic control process adopted by the WM. The output voltage of the WM varies as the degree of dirtiness of the water varies from 0 to 1.95 V for very dirty water output from washed clothes and varies from 4 to 4.89 V for low-contaminated water. Another constraint considered was the load current, which increased as the number of clothes increased. The WM's operation time is determined by the amount of voltage and load current used during its operation. As a result, the control of the WM is dependent on the dirtiness level of the clothes and the amount of load. [ABSTRACT FROM AUTHOR]

Given the longevity of investments in energy-consuming products (such as household appliances, vehicles, and properties), underinvestment in energy efficiency can have long-lasting negative economic and environmental consequences. Previous research has indicated that underinvestment may be due to imperfect information in relation to the long-term benefits of investing in energy efficiency. This paper presents the results of a cluster randomised controlled trial examining an intervention which aims to overcome this information deficit by providing long-term energy cost information on appliances in an electrical retail chain in Ireland. Two treatments are considered: a label showing 10-year energy cost information based on typical usage for four appliance categories (fridge freezers, dishwashers, washing machines, and tumble dryers); and a second treatment which supplements this label with a QR code where consumers can gain personalised cost estimates based on their expected appliance usage. Results indicate that neither of the treatments resulted in an increase in the average energy efficiency of appliances sold. Also, engagement of customers with the QR code was extremely low. Given that the newly designed EU energy labels incorporate QR codes for personalisation, this low usage suggests that this element of the new labels may be ineffective in increasing the uptake of energy efficiency. Finally, a customer survey suggests that while the treatment increased the stated importance of energy efficiency in decision-making, this did not translate into an increase in efficiency of products purchased, i.e., stated preferences for energy efficiency did not translate into revealed purchasing preferences. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) is the practice of estimating power consumption of a single household appliance using data from a total power meter of the user's house. The transformer model has emerged as a popular method for handling NILM problems. However, with the increase in data from electricity meters, there is a need for research focusing on the accuracy and computational complexity of the transformer model. To address this, this paper proposes a sequence-to-sequence load decomposition structure named GTCN, which combines the gate-transformer and convolutional neural networks (CNNs). GTCN introduces a gating mechanism to reduce the number of parameters for training the model while maintaining performance. The introduction of CNNs can effectively capture local features that the gate-transformer may not be able to capture, thereby improving the accuracy of power estimation of individual household appliances. The results of the experiments, based on the UK-DALE dataset, illustrate that GTCN not only demonstrates excellent decomposition performance but also reduces the model parameters compared to conventional transformers. Moreover, the proposed GTCN structure, despite maintaining the same number of model parameters as the traditional transformer architecture after incorporating CNNs, outperforms the conventional transformer model, as well as current seq2seq and R-LSTM technologies, and achieves enhanced prediction accuracy and improved generalization capability. [ABSTRACT FROM AUTHOR]

The Internet of Things is made up of objects with unique identities that are linked to one another online. The idea is to simply connect and keep an eye on numerous sensors and equipment via the Internet, which is widely used in this new era. This paper primarily explains the general overview and knowledge of IOT-based sensing systems and monitoring systems, which leverage databases and software to construct smart, automated household appliances. board, and an Android OS smartphone is used to remotely control the internet. This system's core part and brain, the Node MCU, can serve as an interface between a wide range of hardware parts and the real-time database. The system provides many cuttingedge switching features that turn on and off lights, fans, and other connected household equipment. It is widely used for switching lights, fan on/off by sensing and analyzing data. The cloud-based system is another notification element of this system architecture. The main feature of this system is that it can also be controlled from remote areas which can prove to contribute towards energy saving. [ABSTRACT FROM AUTHOR]

The process of designing progressive dies can be complex, tedious, time-consuming, and error-prone, which can significantly impact the development period and design quality. This paper presents a systematic methodology that includes the planning design and automated design of die structure. A super process model (SPM) is presented as the output of the planning design of die structure and the input of the automated design of die structure. Subsequently, parametric template methodology based on deformation feature process geometry (DFPG) is proposed to automate the design of punches and dies. Lastly, a layout method based on layout space for standard parts is proposed to perform the automated layout of standard parts for die-sets, punches, and dies; additionally, a robust calculation method of layout space is introduced. This system is then implemented in two home appliance enterprises. Case studies and industrial applications have demonstrated the effectiveness of the developed system, which can improve the design efficiency by a minimum of 85% and reduce design error by an average of 50%. [ABSTRACT FROM AUTHOR]

In this paper, an experimental methodology to characterize the noise paths in a washing machine with a horizontal axis was developed. The noise paths considered in this research were the noise that escapes through holes, the non-resonant path through the panels, and the noise radiated by the panels of the cabinet. The characterization method was based on several sound intensity measurements on the outside panels of the washing machine. In addition to these measurements, characterization of the radiation factor was performed by applying a method that relates intensity and vibration measurements while the structure of the washing machine is excited using a shaker. Applying the methodology to a washing machine, the main transmission path of the noise along the frequency domain where this home appliance has its highest values was identified. This methodology can provide the manufacturer with a guide to improve the acoustic performance of washing machines by applying noise control solutions in the noise path depending on the frequency domain. [ABSTRACT FROM AUTHOR]

In this research paper, the problem of scheduling the home appliances is addressed. Generally, the householder's main attention is focused on minimizing his electricity payment by moving the use of the appliance from hours of high electricity rates to hours of low electricity rates. Consequently, the problem of peak demand is created. Thus, the main target behind this work is to propose an efficient scheduling strategy so that the electricity payment is reduced, the peak demand is minimized, and the user's comfort is maintained. The proposed scheduling problem is mathematically elaborated as Mixed-Integer Linear Programming where the decision variables indicate whether the electrical appliances are switched ON/OFF. The proposed strategy is implemented in four different scenarios with 10 appliances and proved to be efficient, simple, and reliable. To scrutinize the efficiency of the suggested method, similar studies in literature are used for comparison. Findings prove that the proposed method accurately minimizes the electricity payments, the peak demand while completing the 100% task required at the end of the day with lower computational time response. [ABSTRACT FROM AUTHOR]

Single-phase asynchronous motors have an irreplaceable role in small production fields such as household appliances and office equipment. However, due to the existence of small single-phase asynchronous motors with low power factor, low efficiency, vibration and noise, and other problems, the performance of a single-phase asynchronous motor, including efficiency, power factor, and vibration noise has been unable to meet the increasing needs of people. In this paper, a single-phase line-starting permanent magnet synchronous motor (SPLSPMSM) for air compressor is designed with the core size of Y series three-phase asynchronous motor for reference. The operating capacitance, the number of turns of the main stator winding, the turns ratio of the main and auxiliary windings, and the permanent magnet size are selected as optimization variables, and the efficiency, power factor, and starting torque are the optimization objectives. A regression model was developed by the response surface method (RSM) to optimize the performance of the motor, and the reliability of the response surface experiment was verified. The results show that the performance of the optimized motor is improved in terms of rated operation and starting performance. [ABSTRACT FROM AUTHOR]

Featured Application: The method presented in this paper serves to predict the consumption of household appliances by modeling their behavior and by simulating accordingly. The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict household electricity consumption. As a novelty, this paper proposed a method of predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov–Smirnov Test and Pearson's X2 test). To test the veracity of the evaluations, first, a set of random values was simulated for each hour, and their respective averages were calculated. These were compared with the averages of the real values for each hour. With the exception of HVAC during working days, great results were obtained. For the refrigerator, the maximum error was 3.91%, while for the lighting, it was 4.27%. At the point of consumption, the accuracy was even higher, with an error of 1.17% for the dryer while for the washing machine and dishwasher, their minimum errors were less than 1%. The error results confirm that the applied methodology is perfectly acceptable for modeling household appliance consumption and consequently predicting it. However, these consumptions can be only extrapolated to dwellings with similar surface areas and habitats. [ABSTRACT FROM AUTHOR]

This paper presents a new application of the sine cosine algorithm (SCA) to obtain optimal home energy management systems (HEMS) that use the load shifting strategy of demand side management (DSM) to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to decrease electricity bills and peak to average ratio (PAR) while maintaining user comfort through coordination among home devices. In order to meet the load demand of electricity consumers, this study schedules the load on a day‐ahead and real‐time basis. The main objective of the paper is to help balance the load through ON‐peak hours and OFF‐peak hours. Three price signals, namely: real‐time pricing (RTP), time of use (TOU), and critical peak pricing (CPP), are used to evaluate the proposed algorithm. The results show that the electricity bill and PAR are minimized by up to 40% and 50%, respectively. The scheduling using coordination between devices in real‐time does not significantly impact the cost of electricity and the peak to average ratio. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) is widely used to evaluate product's life cycle environmental impact and identify the environmental weaknesses. However, it is difficult for existing LCA software to perform flexible LCA analysis based on the product life cycle characteristics and industry background. Meanwhile, under the existing LCA research model, product designers and manufacturers are usually not LCA evaluators, resulting in a certain time gap between the evaluation results and product improvement. Designers with less experience in green design often find it difficult to identify high environmental impact links in products at different life cycle stages and product levels, and updated products are challenging to meet various environmental restrictions. This paper establishes a multi-module product life cycle analysis model that combines product industry background that includes basic information, assessment information, structural information, and restriction information to achieve the multi-scenario of product LCA in different dimensions in a typical domain. The calculated mechanism of the dynamic power emission factor is built according to the service time and space dimensions. The proposed method forms an integrated environmental performance evaluation of household appliance (EPEHA) system. A software assessment and an optimization method are proposed to improve the EPEHA system. The results of this study show that these proposed methods can improve the timeliness and diversity of results analysis of product LCA in the field of household appliances in China. The universal data exchange format and simple operation interface of the EPEHA system enable people related to the product to quickly understand the environmental impact of the product in different scenarios, even if they lack green design knowledge and professional software training. [ABSTRACT FROM AUTHOR]

Multi-label classification is an advantageous technique for managing uncertainty in classification problems where each data instance is associated with several labels simultaneously. Such situations are frequent in real-world scenarios, where decisions rely on imprecise or noisy data and adaptable classification methods are preferred. However, the problem of class imbalance represents a common characteristic of several multi-label datasets, in which the distribution of samples and their corresponding labels is non-uniform across the data space. In this paper, we propose a multi-label classification approach utilizing fuzzy logic in order to deal with the class imbalance problem. To eliminate the need for an expert to determine the logical rules of inference, deep neural networks are adopted, which have proven to be exceptionally effective for such problems. By combining both fuzzy inference systems and deep neural networks, the strengths and weaknesses of each approach can be mitigated. As a further development, a symbolic representation of time series is put in place to reduce data dimensionality and speed up the training procedure. This allows for more flexibility in model application, in particular with respect to time constraints arising from the causality of observed time series. Tests carried out on a multi-label classification dataset related to the current and voltage profiles of several household appliances show that the proposed model outperforms four baseline models for time series classification. [ABSTRACT FROM AUTHOR]

In this paper, an inductive power transfer (IPT) system without compensation elements is presented for small house appliances. The proposed system's transmitter side is an independent induction heating cooktop. IPT can be achieved when the kettle with the receiving coils is placed on the transmitter coil. The coils are designed with a high coupling coefficient. The magnetic system model consisting of aligned transmitter and receiver coils is created in the Maxwell program. In the created model, the analysis depends on the air gap and frequency, which are the variables that affect the wireless power transfer. The electronic circuit simulation uses the coil model to examine the system's dynamic behavior. The design of the transmitter/receiver coils of the IPT system is made with a cylindrical coil with a diameter of 145 mm, taking into account that it is compatible with the dimensions of the existing kettle and induction heating cooktops coil. A half-bridge series resonant converter circuit is used to adjust the power transferred to the load. To verify the simulation results and test the designed system, an experimental circuit using a 2200 W kettle is carried out. In the experiments, the air gap between the coils is kept constant at 7 mm, and measurements are taken for different powers. Experimental results confirm the magnetic model and simulation results. As a result, wireless power transfer is realized in a wide range without loss of performance in the kettle. System efficiency is greater than the 90% specified in the Ki cordless kitchen standard, and the harmonic currents drawn from the mains are lower than the values determined by the IEC 61000-3-2 standard. [ABSTRACT FROM AUTHOR]

The verification complexity is becoming more complex, for the hardware-software tightly-coupled embedded processors in intelligent smart home appliances and automotives. Software-embedded processors require more verification overhead in time-dimension simulation for the long-term state transitions. Designers may not have enough time to perform all tests due to the repeated simulation. This paper presents an existing simulation method of implemented with Verilog digital design and propose a new method to reduce simulation run-time. To shorten simulation time, the compilation and common simulation run-time that are repeatedly performed were saved as snapshot file. The development tun-around time was reduced by performing the simulation using Tcl-based verification code for dynamically reloading from the stored simulation snapshot. The DSP block embedding in 8051 MCU is used as a demonstration. Despite the additional time of storing simulation snapshot and Tcl based verification code, simulation time was reduced by 53% compared to the traditional method. [ABSTRACT FROM AUTHOR]

Mandatory energy efficiency label is an effective way to change consumers' consumption habits and guide them to buy energy-saving appliances. However, few studies concerned about the impact of energy efficiency label on consumers' purchasing behavior. Based on the theory of planned behavior (TPB), social cognitive theory and signaling theory, this paper constructs a theoretical model of the effect of the energy label on consumers' purchasing behavior of energy-saving household appliances. The survey data of 396 household appliance consumers in Mianyang City, China, are collected by the interception method, and the theoretical model is tested by structural equation modeling (SEM). Empirical results of this study indicate that consumers' cognition and perceived value of energy efficiency label significantly affect label trust. Perceived value has a significant impact on consumers' purchasing behavior of energy-saving appliances, while label cognition and label trust indirectly influence consumers' purchasing behavior through the intermediary variable of purchase intention. External environmental factors such as publicity and education as well as subjective norms affect consumers' actual purchasing behavior through the intermediary effect of purchase intention. This study provides important insights into the policy intervention measures to promote consumers' purchasing behavior of energy-saving appliances. [ABSTRACT FROM AUTHOR]