Appliance Characteristics Research Articles

Robust energy management through aggregation of flexible resources in multi-home micro energy hub

This study introduces a novel approach to home energy management systems (HEMS) through the development of a micro energy hub. The hub proficiently aggregates flexible resources across multiple homes, underpinned by a detailed modeling of appliance characteristics for enhanced energy management accuracy. The integration of Distributed Energy Systems (DES) further enriches the micro energy hub's capabilities, while a multi-objective two-stage robust optimization (M-TSRO) model is developed to address the inherent uncertainty of DES, aiming to concurrently realize energy savings, carbon emission reduction, and load smoothing. The effectiveness of the proposed micro energy hub is evaluated using real-world REFIT data set, demonstrating improved load scheduling, smoother load profiles, and enhanced energy flexibility. The results indicate significant improvements in grid control levels, reaching 68.75% in the basic scenario and 56.25% in the prosumer scenario. The prosumer scenario exhibits a significant reduction of 13.34% in the integrated objective compared to the basic scenario. This reduction is primarily driven by a notable decrease of 31.44% in the operational cost and an 8.06% decline in carbon emissions of the micro energy hub. These findings highlight the potential of micro energy hubs in optimizing energy consumption and increasing power flexibility through collaborative load scheduling strategies.

Interpreting the energy choices and environmental satisfaction determinants in low-income housing typologies: Cases from slums and slum rehabilitation housing of Mumbai, India

This study explores the impact of built-environment characteristics and occupant behaviour on appliance ownership, energy consumption, and environmental satisfaction in low-income settlements. A shift in the built-environment setting significantly impacts energy choices, appliance purchase decisions, appliance characteristics, and related occupant behaviour and household practices. This study performs a comparative analysis of the horizontal slums and vertical tenements of slum rehabilitation housing (SRH) in Mumbai by quantitatively investigating the difference in built-environment settings from horizontality to verticality on energy decisions. A Computer-based Personal Interview was conducted among 643 slum and SRH households. This study established the significance of ‘lack of satisfaction and well-being’ as a key aspect of the impoverishment of the slum-displaced population. The displaced population with a tenured house and ‘hard infrastructure’ in the resettlement colonies adapts to the social structure, modifies social and household practices, and delves into improved energy choices, improving living standards. Yet, this minimally decent life fails to provide the occupants with environmental and life satisfaction and well-being. The study establishes that occupant behaviour is individual predilection oriented, and the socio-spatial context of ‘built-environment,’ with following rational modification, can improve the population's energy consumption and environmental satisfaction.

Stochastic modelling of flexible load characteristics of split-type air conditioners using grey-box modelling and random forest method

Split-type air conditioners (ACs) are the major contributors to the total electricity use and peak power demand in residential buildings due to their high penetration. Accurate prediction of their energy consumption plays a significant role in demand side management (DSM), because it can help exploit the demand response (DR) potential of these ACs fully. However, the existing studies on the load characteristics modelling of split-type ACs are still not comprehensive, especially in the consideration of occupant behavior and appliance characteristics. To solve this problem, this study has developed a novel flexible load characteristics model by combining the stochastic method, grey-box modelling (i.e., equivalent thermal parameter (ETP) model) and random forest method. The stochastic method can help capture the dynamic occupants’ energy-related behaviors considering various family structures. Random forest can address the difficulties in simulating the power characteristics of variable-speed ACs under various operating conditions. With the prediction error of 2.8%, the proposed model can effectively characterize the energy performance of split-type ACs at both individual and aggregate levels. To better show the load flexibility of ACs, an investigation on DR potential of split-type ACs was also carried out by resetting temperature setpoints. The result showed that the maximum DR potentials at the scope of 1000 aggregated households are 394.54 kWh and 323.82 kWh on weekdays and weekends, respectively. The developed flexible load characteristics model of ACs can be used by utility companies for DR potential assessment and model-based DR control for a single household or building clusters.

An adaptive lightweight seq2subseq model for non‐intrusive load monitoring

Non-intrusive load monitoring (NILM) is an important technology for deeply mining consumers' internal electricity consumption information, which can improve the level of awareness of the load and significantly improve the demand-side management capability of the smart grid. For a long time, traditional NILM faces the challenge of huge data, long training time and low accuracy. To address this issue, this paper proposes an adaptative lightweight seq2subseq model. The model adaptively determines the optimum window length of the active power segment to extract the appliance characteristics and thus reduces the redundant data to construct the appliance-level-seq2subseq model. The proposed model significantly improves the ability to extract and utilize information. At the same time, transfer learning is used to ban the training of appliance models with similar characteristics, further reducing model complexity and training time. The experimental results based on the REDD dataset verify the method's effectiveness. Compared with the seq2seq and seq2point models, the proposed model achieved better accuracy and F1 score, up to 98.10% and 90.29%, respectively. At the same time, the proposed method is further verified on UK-DALE, which proves that the proposed method has good portability and robustness.

Development of an Electrical Energy Consumption Model for Malaysian Households, Based on Techno-Socioeconomic Determinant Factors

Energy-saving strategies are required to address the increasing global CO2 and electrical energy consumption problems. Therefore, the determinant factors of electrical energy consumption consist of socio-demographic changes, occupant behavior, house and appliance characteristics, or so-called techno-socioeconomic factors, which all need to be assessed. Statistics models, such as the artificial neural network (ANN), can investigate the relationship among those factors. However, the previous ANN model only used limited factors and was conducted in the developed countries of subtropical regions with different determinant factors than those in the developing countries of tropical regions. Furthermore, the previous studies did not investigate the various impacts of techno-socioeconomic factors concerning the performance of the ANN model in estimating monthly electrical energy consumption. The current study develops a model with a more-in depth architecture by examining the effect of additional factors such as socio-demographics, house characteristics, occupant behavior, and appliance characteristics that have not been investigated concerning the model performance. Thus, a questionnaire survey was conducted from November 2017 to January 2018 with 214 university students. The best combination factors in explaining the monthly electrical energy consumption were developed from occupant behavior, with 81% of the variance and a mean absolute percentage error (MAPE) of 20.6%, which can be classified as a reasonably accurate model. The current study’s findings could be used as additional information for occupants or for companies who want to install photovoltaic or wind energy systems.

The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes

The IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

Determinant Factors of Electricity Consumption for a Malaysian Household Based on a Field Survey

Electricity-saving strategies are an essential solution to overcoming increasing global CO2 emission and electricity consumption problems; therefore, the determinant factors of electricity consumption in households need to be assessed. Most previous studies were conducted in developed countries of subtropical regions that had different household characteristic factors from those in developing countries of tropical regions. A field survey was conducted on electricity consumption for Malaysian households to investigate the factors affecting electricity consumption that focused on technology perspective (building and appliance characteristics) and socio-economic perspective (socio-demographics and occupant behaviour). To analyse the determinant factors of electricity consumption, direct and indirect questionnaire surveys were conducted from November 2017 to January 2018 among 214 university students. Direct questionnaire surveys were performed in order to obtain general information that is easily answered by respondents. On the other hand, some questions such as electricity consumption and detailed information of appliances must be confirmed by the respondents’ parents or other household members through an indirect questionnaire survey. The results from multiple linear regression analyses of the survey responses showed that appliance characteristic factors were the main variables influencing electricity consumption and house characteristics were the least significant. Specifically, air conditioners, fluorescent lamps, and flat-screen TVs emerged as appliances with the most significant effect on electricity consumption. Occupant behaviour factors had a more significant influence than socio-demographic factors. The findings in this study can be used by policymakers to develop electricity-saving strategies in Malaysia.

Analysis of dynamic appliance flexibility considering user behavior via non-intrusive load monitoring and deep user modeling

The research on non-intrusive load monitoring (NILM) and the growing deployment of home energy management system (HEMS) have made it possible for households to have a detailed understanding of their power usage and to make appliances participate in demand response (DR) programs. Appliance flexibility analysis helps the HEMS dispatching appliances to participate in DR programs without violating user's comfort level. In this paper, a dynamic appliance flexibility analysis approach using the smart meter data is presented. In the training phase, the smart meter data is preprocessed by NILM to obtain user's appliances usage behaviors, which is used to train the user model. During operation, the NILM is used to infer recent appliances usage behaviors, and then the user model predicts user's appliances usage behaviors in the DR period considering long-term behaviors dependences, correlations between appliances and temporal information. The flexibility of each appliance is calculated based on the appliance characteristics as well as the predicted user's appliances usage behaviors caused by the control of the appliance. The HEMS can choose the appliance with high flexibility to participate in the DR programs. The case study demonstrates the performance of the user model and illustrates how the appliance flexibility analysis is performed using a real-world case.

A successful fuel transition? Regulatory instruments, markets, and social acceptance in the adoption of modern LPG cooking devices in Indonesia

In 2004, almost half of Indonesian society was dependent on firewood for fuel. Following the implementation of a policy to substitute kerosene use with Liquefied Petroleum Gas (LPG) in 2007, there was a significant improvement in LPG use from 10.5 percent to 47 percent of households, whilst kerosene use fell significantly from 36 to 9 percent from 2007 to 2011. There was also a 10 percent reduction in the number of households using firewood. This paper investigates LPG adoption by households who previously relied on kerosene and firewood from two perspectives: the institutional perspective, and social acceptance. Semi-structured interviews with representatives from central government to local government in addition to members of the public in six regions in Java and Sumatera were conducted. Findings indicate that the central government’s commitment to implement the substitution policy—alongside effective regulatory instruments, financial support and infrastructure—was critical to the success of the policy. Meanwhile, from a household perspective the reasons behind adoption of LPG were favourable prices, as the result of subsidies and incentives; energy market lock-in; social trust and norms; government communication and campaigns; and favourable appliance characteristics and/or kitchen design. However, there were also LPG resisters who maintained the use of firewood rather than adopting LPG. Safety concerns, cooking preferences and habitual practices were identified as the main determinants of LPG resistance. Understanding the reasons underlying cleaner fuel acceptance or resistance is essential for policy makers to determine successful regulatory pathways to improve fuel quality for better quality of life.

A Modified IP-Based NILM Approach Using Appliance Characteristics Extracted by 2-SAX

Deregulation on the delivery side of the power market has continuously been moving forward worldwide, which make bidirectional flow and interactions between customers and grids needs to be more refined and in-depth. Large-scale coverage of the advanced metering infrastructure (AMI) brings in skyrocketing of an immense amount of fine-grained, real-time consumption data and causes communication traffic congestion between meters and a cloud computing center. To tackle these two challenges, this paper proposes a modified IP-based non-intrusive load monitoring approach using appliance characteristics extracted by quadratic symbolic aggregate approximation (2-SAX). A 2-SAX algorithm is implemented to carry out dimensionality reduction on equipment load data and extracted the state's transition behavior characteristics and operation probability characteristics of each device. The extracted features can use to modify the disaggregation results of integer programming for overcoming the shortcomings of the previous IP approach. The developed method is tested with AMPds dataset. The results of experiments illustrate the 2-SAX consequences in 38.82%, 52.46%, and 13.41% reduction in MAE, MAPE, and RMSE on the heat pump and achieves similar performance on the other appliances, compared with normal SAX. Meanwhile, the proposed method MIP-AC2S delivers significant accuracy advantage and competitive performance over IP, ALIP, and MIP disaggregation method.

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

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

Model-Free Iterative Temporal Appliance Discovery for Unsupervised Electricity Disaggregation

Electricity disaggregation identifies individual appliances from one or more aggregate data streams and has immense potential to reduce residential and commercial electrical waste. Since supervised learning methods rely on meticulously labeled training samples that are expensive to obtain, unsupervised methods show the most promise for wide-spread application. However, unsupervised learning methods previously applied to electricity disaggregation suffer from critical limitations. This paper introduces the concept of iterative appliance discovery, a novel unsupervised disaggregation method that progressively identifies the "easiest to find" or "most likely" appliances first. Once these simpler appliances have been identified, the computational complexity of the search space can be significantly reduced, enabling iterative discovery to identify more complex appliances. We test iterative appliance discovery against an existing competitive unsupervised method using two publicly available datasets. Results using different sampling rates show iterative discovery has faster runtimes and produces better accuracy. Furthermore, iterative discovery does not require prior knowledge of appliance characteristics and demonstrates unprecedented scalability to identify long, overlapped sequences that other unsupervised learning algorithms cannot.

Toward Energy-Awareness Smart Building: Discover the Fingerprint of Your Electrical Appliances

Energy efficiency raises significant concerns as it is one of the most promising ways to mitigate climate change. Disaggregation and identification of individual electrical appliances activities are one of the essentials for energy preservation especially for smart buildings. This paper proposes a lightweight electrical appliance activity detection approach for smart building, which leverages a single smart metering device to establish a learning and detection processing for multiple appliances. In this system, data interpolation and transition detection algorithm are proposed to effectively reduce the cost of model training and optimize the detection accuracy. The concept of appliance fingerprint is proposed and a variety of fingerprints, including appliance-based and context-based, are defined to depict fine-grained appliance characteristics. Based on these fingerprints, the paper proposes a multisource fingerprint-weighting KNN (FWKNN) classification algorithm and presents a boosting framework for continuous online learning and detection. A prototype system is implemented and demonstrated in IBM Bluemix PaaS cloud platform. Experimental result and analysis prove that FWKNN outperforms other benchmark methods in detection accuracy.

The Use of the Kano Model to Enhance Customer Satisfaction

Abstract Background/purpose: The interest of measuring customer satisfaction is reflected in its ability to gain customer loyalty, enhance favourable word of mouth, lead to repeat purchases and improve a company’s market share and profitability. The issue of integrating the Kano model of customer satisfaction with other models and tools to support development or improvement of a product, or to determine market strategies, is relatively unexplored in the Slovenian sector. This research aims to construct the Kano model in order to enhance customer satisfaction in the case of home appliances. Design/Methodology/Approach: Data was collected using an online survey amongst randomly selected individuals from the service interventions for an end users database. Principal component factor analysis was first used to identify the underlying factors of home appliance characteristics. In the next phase we calculated the derived and stated importance of customer satisfaction, which was then used to construct the Kano model of customer satisfaction. We further analysed which factors are the strongest drivers, or predictors, of repeat purchase using multiple regression analysis. Results: In the study we identified the underlying home appliance factors. The results show that these factors are: sales environment, price, user features, design features and technical features. The results were then used to construct the Kano model where the analysis goes beyond the qualitative analysis by implementing two approaches, stated and derived importance approach. According to the Kano model, marketers should concentrate on delight characteristics such as: wider knowledge of the salesperson, professional skills of the salesperson, design of home appliance, brand of home appliance. What is more, factors called ‘user features’ are the strongest predictors of repeat purchase. Conclusion: This paper links the Kano model with measuring customer satisfaction and presents a contribution for marketing research theory. Therefore, the results could be used to support optimization of business decision-making, as well as for further scientific research.

An integrated approach to modelling end-use energy and water consumption of Australian households

To predict both end-use energy and water consumption of households, an integrated approach was developed by adding water module into the AusZEH design tool, which was a physically-based model to simulate energy consumption of residential buildings considering building envelop, appliance characteristics, local climate and occupant behaviour. Water and its associated energy consumption includes indoor and outdoor water systems considering actual operation of the water systems. Monte Carlo approach was applied to simulate both energy and water use patterns of appliances considering uncertainties. The comparisons between simulation and actual data show the model can track both energy and water consumption and usage patterns.

Factors Influencing Energy Consumption of Energy Star and Non–Energy Star Homes

AbstractBecause of depleting global energy resources, an economic necessity in the United States is to reduce energy consumption. The construction industry is proceeding to reduce the energy consumption of buildings by developing efficient designs and constructing buildings that have energy-efficient materials and features. In 1992, the United States EPA and DOE introduced the Energy Star (ES) program to promote energy-efficient products that have the same or better services than their counterparts without the Energy Star label. According to the EPA, ES homes that use these products consume less energy than non–Energy Star (NES) homes. However, it is also important to determine other factors that affect the energy consumption of ES and NES homes. Data were collected from 30 ES and 30 NES homes of similar construction for annual electricity and natural gas consumption, appliance characteristics, and homeowners’ behaviors. Analytical results indicated that six factors that are related to the characteristics...

Characteristics and mobile Internet use intensity of consumers with different types of advanced handsets: An exploratory empirical study of iPhone, Android and other web-enabled mobile users in Germany

This work explores personal characteristics and mobile Internet (MI) use behaviors of consumers equipped with four distinct types of advanced handsets for accessing the Internet via cellular radio infrastructures of mobile network operators (MNO). Furthermore, it investigates the extent to which personal and mobile appliance characteristics explain variance in actual MI use intensity. Data on two demographic variables, three MNO relationship characteristics and actual MI use intensity (average monthly volume of mobile IP traffic generated by a subscriber in May and June 2011) of 9321 adult consumers with a flat MI pricing scheme are extracted from customer files of the German subsidiary of a large international MNO. 959, 2213, 2410 and 3739 of the sample members use an Apple iPhone 3, an Apple iPhone 4, a model running with Google’s Android operating system (OS) and other MI-enabled mobile OS/phone types, respectively. Compared to the adult population in Germany, persons at least 50 years of age are clearly underrepresented among MI adopters with the four studied device types. Differences between the four phone type groups with regard to gender, age, time from enrollment and MI use experience emerge as statistically significant, but they achieve only minor substantial relevance. MI use intensity is highly positively skewed: In each of the four appliance groups, a small number of users disproportionately add to the total MI traffic generated by the subjects. Consumers’ advanced OS/handset type strongly contributes towards explaining MI use intensity variance. iPhone subscribers generate more traffic than Android customers who in turn show a higher MI activity level than individuals running other web-enabled mobile models. Age is the only studied personal characteristic consistently showing a (negative) association with MI usage, which both is statistically and materially significant. Conclusions are drawn for MNO on MI marketing issues. Implications of study limitations for research on MI adoption and use behaviors on the MI are also outlined.

Cooking behaviours: a user observation study to understand energy use and motivate savings

Electric cookers are one of the highest energy consuming domestic appliances, and there are several aspects that can influence the amount of energy used when preparing a meal. Appliance characteristics are shown to influence consumption, but human factors play an important role in the overall electricity usage. A user observation study was conducted among university students to understand how they use electric cookers. They were asked to perform a specific cooking task, and the electricity usage was measured. Participants' behaviours were analysed and compared with a set of energy saving techniques. Appliance characteristics that influenced how students use energy were also investigated. The results show that users performed the task in several different ways, presenting diverse energy consumption, the average being 3 times above the necessary to complete the task. This information is now informing the design of interventions to motivate people to change their behaviours whilst cooking.

The Information Appliance of Choice

Where portability was once a luxury, it is now a requirement in today's pharmacy technology world. The perfect information appliance for one pharmacist is not necessarily ideal for another pharmacist. The determination of “perfection” as it relates to information appliances is based on appliance characteristics and user preferences. In this installment, we address appliance characteristics and user preferences, focusing on mobile appliances.

The effect of the amount of protrusion built into the Bionator on condylar growth and displacement: A clinical study

To evaluate the treatment effects of a variable degree of forward posturing of the mandible incorporated into the Bionator, a study was undertaken on 14 patients with Class II, Division 1 malocclusion, treated for 1 year with a Bionator, maintaining the mandible in an edge-to-edge incisal position (Be group). This group was compared with a matched group of patients treated with the Bionator in whom the mandible was maintained in a “functional maxipropulsion” (Bmax group). Both groups were subdivided further according to sex for selected parameters. Twenty-nine measurements on cephalometric x-ray films taken before and after 1 year of treatment were statistically analyzed. According to this study, “edge-to-edge” protrusion is superior to “functional maxipropulsion” in increasing ramal height. The difference in increase in corpus length after treatment (greater in boys than in girls) was sex related rather than dependent on appliance characteristics. When studying the effect of functional appliances, it is suggested that a patient group be subdivided by sex. After treatment a repositioning of the condyle was found in a more downward and possibly slightly forward direction relative to sella point (S) in both groups. A relocation of the condyle should be considered as another treatment effect of functional appliances. The more important lowering of the condyle found in the Bmax group may partly explain the significant increase in SNB, the 1.6-fold larger decrease in overbite, and the highly significant increase in anterior lower facial height in this group. The fact that the effects on ramal height and on condylar displacement were significantly more pronounced in the Be group and the Bmax group, respectively, was attributed to the different amount of protrusion built into the functional appliance. Comparative studies on the effects of functional appliances should standardize the degree of propulsion.