Household Users Research Articles

Qualitative Analysis of Household Energy Awareness in Poland

Because of disruptive changes in energy businesses and services, as well as changes in people’s lifestyles around the world, energy demand and consumption have risen quickly in recent decades. A household energy awareness literature review was conducted. The survey was conducted in Poland with over 1097 respondents. Additionally, it was given a qualitative analysis. At the same time, in order to consider why energy consumption in households is increasing, a traditional quality management tool was used—the Ishikawa diagram. The Pareto–Lorenz diagram was used to analyze the causes of the increase in energy consumption in households. A model of the causes of increasing energy consumption in households has been built. The researchers calculated the chi-square test, which allows them to determine Pearson’s C coefficient. The C-Pearson coefficient can be calculated using the Chi square value. The results of the study clearly show that the respondents have knowledge of energy and the factors that influence the increase in its consumption but are not aware of why energy should be saved and what it means for the environment. The authors propose ways to build the awareness of household users through a modern quality management tool called the interrelationship diagram.

The effects of frozen storage on fatty acids, amino acids, and volatile compounds in mutton stored for 90 days

Frozen storage can delay mutton meat spoilage to the maximum extent, prolonging its shelf life. In this experiment, mutton refrigerated for 24 h was used as the control group and that stored at −18°C was used as the experimental group to explore the effects of frozen storage for 90 days on FA, AA, and VOC of the mutton. The results showed that FA, AA, and VOC changed significantly during storage. PUFA and n-3 FA contents were the highest at 3 days of freezing (54.92 and 15.28 mg/100 g, respectively). AA contents were higher in the experimental group than in the control group, and Glu contents were the highest during the whole storage period, ranging from 2.94% to 3.28%, and the maximum value was on the 3rd day. In total, 38 volatile compounds were detected, and the most abundant volatile compounds were detected at 60 days. The variance contribution rates of PCA were 48.7%, 19.4%, and 14.8%, the PCA model can be used as a separation model. Novelty impact statement This study simulated the storage of mutton by household users and shortened the detection time point at the initial freezing stage, indicating that the differences in flavor substance changes in mutton at the initial freezing stage was little. On the other hand, GC-IMS was effectively used to visualize flavor substance changes during storage, providing a new method to distinguish the storage time of meat products. In conclusion, this study can provide basic research data for long-term storage of mutton and provide technical reference for meat storage identification.

Study on the Design of a Water Dispenser for Visually Impaired Families

In visually impaired families, there are different needs in terms of the access to drinking water, and the existing research on the design of water dispensers is insufficient. In order to solve this problem, a diverse analysis, based on the inclusive design concept, of household users’ operating abilities and water dispenser preferences was carried out. Subsequently and guided by the analysis, a new water dispenser product was developed. The methods used to extrapolate the needs of visually impaired families were observation and interviews, while the questionnaire research was used to understand the user preferences. Secondly, the accessibility of the existing water dispenser was tested for visually impaired, elderly, middle-aged, and young users in the visually impaired family. Three design strategies were proposed that combine the needs and product preferences of the users: the multimode simplification of steps, water quality detection, and adaptive multichannel feedback. The water dispenser was redesigned on the basis of these. On the premise of meeting the different abilities of the users in visually impaired families, the design practice improves the users’ experience of water dispensers, and provides a practical method reference that has value for related product designs.

A novel forecasting based scheduling method for household energy management system based on deep reinforcement learning

• A novel forecasting based reinforcement learning energy scheduling method is proposed. • A GC-LSTM algorithm is adopted in out-door temperature prediction. • A model-free During-DDQN algorithm is adopted to manage household energy. • The proposed method performs well in the household energy management system. The demand response (DR) strategy enables household users to actively optimize and dispatch the household energy management system (HEMS), which may significantly reduce the cost of user energy consumptions. However, the uncertainty of home users behaviors, the diversity of electrical equipment types, as well as the complexity of the working status of various devices have brought severe challenges to the home energy management system. This article proposes a new forecasting based optimization method to deliver the real-time scheduling considering the future environment trend. A recent proposed dueling based deep reinforcement learning approach is adopted to optimally dispatch the HEMS. In addition, due to the delay of heating, ventilation, and air Conditioning (HVAC) indoor and outdoor temperature data non-Gaussian, a new generalized corr-entropy assisted long short-term memory (GC-LSTM) neural network is proposed where the generalized correntropy (GC) loss function is adopted to predict the outdoor temperature. The proposed method is verified in a featured HEMS benchmark problem and the experimental results show that the user costs are effectively reduced while the user satisfaction is maintained utilizing the proposed method.

Demand Based Cost Optimization of Electric Bills for Household Users

Abstract- Internet of Things (IoT) is increasingly becoming the vehicle to automate, optimize and enhance the performance of systems in the energy, environment, and health sectors. In this paper, we use Wi-Fi wrapped sensors to provide online and in realtime the current energy consumptions at a device level, in a manner to allow for automatic control of peak energy consumption at a household, factory level, and eventually at a region level, where a region can be defined as an area supported by a distinct energy source. This allows to decrease the bill by avoiding heavily and controllable loads during high tariff slice and/or peak period per household and to optimize the energy production and distribution in a given region. The proposed model relies on adaptive learning techniques to help adjust the current load, while taking into consideration the actual and real need of the consumer. The experiments used in this study makes use of current and voltage sensors, Arduino platform, and simulation system. The main performance indexes used are the control of a peak consumption level, and the minimum time needed to adjust the distribution of load in the system. The system was able to keep the maximum load at a maximum of 10 kW in less than 10 seconds of response time. The level and response time are controllable parameters.

Household ICT Utilization and Food Security Nexus in Nigeria.

The study examines the nexus between household ICT utilization and food security in Nigeria, which supports goal 2 of the 2030 Sustainable Development Goals (SDGs) that aims to “end hunger, achieve food security, improved nutrition, and promote sustainable agriculture.” The study employs the logit regression to wave 4 of Living Standard Measurement Integrated Survey on Agriculture (LSMS) data for the empirical analysis. Based on the analysis, the study finds that for male household, ICT utilization has a statistically significant and positive nexus with food security. In contrast, for the female households, an insignificant and, however, negative nexus is observed with food security in Nigeria. Furthermore, the findings show that for male household users, a 1 percent increase in male household ICT utilization spurs about 0.68 percent increase in food security in Nigeria. The findings imply that among the male and female household ICT users, the male household ICT utilization is significantly contributes to food security in Nigeria. The study recommends that relevant stakeholders take strategic measures to ensure that the potentials of household ICTs be fully maximized to contribute to food security in the nearest future as confirmed by studies in other regions.

IoT-Based Waste Height and Weight Monitoring System

Over the past decade, the use of smart waste monitoring systems has become a popular way of waste management. This system generally makes it easier for scavengers and household users to monitor the status of the level of waste in the trash. This study aims to create an innovative IoT-based intelligent waste monitoring system to facilitate waste monitoring for households. The proposed system consists of the first 3 important parts, an IoT-based household trash bin monitoring system. Second, the smart trash can monitor system uses 2 sensors, namely ultrasonic sensors and load cell sensors. Third, display a full indicator on the web if it has reached the maximum height or weight limit of garbage. Meanwhile, the evaluation of the system is carried out by testing the accuracy of the weight and height values of the waste on the smart waste system and using the black-box method for testing application features. The proposed smart trash bin has been tested manually using a ruler for measuring the height of the trash compared to measurements using a height sensor resulting in an average gap error of 0.08 cm. While the results of the comparison of measuring waste weight using a scale in kg compared to measuring waste weight using a weight sensor resulted in an average gap error of 0.03 kg. The test results are close to the actual height and weight of the garbage. The main contribution of this research is a system to monitor the volume of waste in the trash that can be linked to the application of waste collection service management for residential environments. Smart trash can users can monitor the condition of the waste by looking at the high level and weight indicators of the waste on the web application when it reaches the maximum then sending an order message to the waste collection manager for waste.

Household characteristics and electricity end-use under dynamic pricing in the collective housing complex of a Japanese smart community

ABSTRACT In this study, we focused on electricity consumption and household characteristics with Dynamic Pricing(DP) experiment in the collective housing complex of a Japanese Smart Community, which had been divided into two groups, Treatment group (110 households) and Control group (65 households). We collected and analyzed a questionnaire survey on the family attributes of residents and all housing electrical appliances to understand the actual conditions of household characteristics. The annual energy consumption by smart meters had been aggregated and analyzed to understand the actual conditions of electricity end-use of the households. The results showed the main factors of household, which affect the electricity consumption, were floor area, family number, income of households, and the number of air conditioner. During dynamic pricing experiment in summer and winter, we found that the implementation of Dynamic Pricing would affect household users' habits of using electrical equipment and had a contribution to energy peak cut. Through the hierarchical cluster analysis, we divided both group into three types of household characteristics respectively. The result has revealed that the family structure would affect the electricity consumption during Dynamic Pricing implement.In this study, we focus on energy consumption and household characteristics with dynamic pricing experiment in the collective housing complex of a Japanese Smart Community. we collected and analyzed a questionnaire survey on the family attributes of residents and the lifestyle of all housing equipment to understand the actual conditions such as the attributes of the resident generation. The annual energy consumption by smart meters will be aggregated and analyzed to understand the actual conditions of electricity consumption of the resident generation. Based on the aggregated results, a database of lifestyle patterns and energy consumption is constructed, and the relationship between each factor and energy consumption, and each lifestyle pattern has been analyzed.

Path dependence, self-fulfilling expectations, and carbon lock-in

Growing under the incumbent dirty regime might generate inertia against switching to a clean alternative and lead to the carbon lock-in. I analyze the economics of carbon lock-in through the lens of path dependence and self-fulfilling expectation. The incumbent dirty regime, as the first mover, accumulates an installed base of household users during early lead periods. The installed base generates an effect of path dependence that incentives upcoming households to keep on joining the incumbent dirty regime rather than switch to the clean alternative. Switching could be an expectation-driven selection outcome if expectations are formed about an expanding clean regime which outweighs the path dependence effect. Switching is a welfare-improving outcome if welfare gains by upcoming households switching to the clean regime offset welfare losses by installed-base households locked in the dirty one.

An efficient and improved model for power theft detection in Pakistan

This paper describes an improved model for the monitoring of power used by a party such as household users and different industries in Pakistan. The power theft detection was done using the intelligent internet of things (IoT) service system for calculating the user's power simultaneously. The power meter catches a theft detection device that is instantly transmitted to the central system which compares both the data by means of microcontroller and if there is any difference found, it informs the power utility about the hooking, meter relief or theft activities happen. Information of the theft detection through the global mobile communications system is transmitted and notified theft is displayed on the terminal monitor or won. As a result, although consumers continue to use excess fuel, the customer's power supply is cut in the electricity boards segment. The general radio package module system sends central circuit and meter data via an internet protocol address to a web server. GSM's IoT based perception is used to monitor the power supply and billing information calculated with a microcontroller continuously with the determination of the electricity table area. With this unit, the duplicate user can be located at the rear of the electricity office with the power meter status.

Design of Grid-connected and Stand-alone Photovoltaic Systems for Residential Energy Usage: A Technical Analysis

Towards realizing the United Nations sustainable development goals, access to clean, cheap and reliable energy, especially electricity, has been considered as one of the vital indices in any community. Hence, this paper presents the design analysis of both a grid-connected and an offgrid photovoltaic (PV) systems for household users in the highly residential city of Ogbomoso in Nigeria using PVGIS software. For the off-grid design, it is estimated that, given a total daily load of 9.57 kWh, a 3.5 kWp PV array size and a battery capacity of 86 kWh are enough to power the load with 5 days of autonomy and 70% depth of battery discharge. For the grid-connected PV system, the annual energy output for a building-integrated PV system is found to be around 4006 kWh; and a total of eight PV modules (each rated 250 Wp, 30.93 V) are stringed to arrive at the desired capacity of 2 kWp. In terms of performance, the performance ratio (PR) of a building integrated grid-tied PV system at the study location was found to be 71.2% while for a free-standing PV system, the PR was 75%.

The mobile money’s poverty-reducing promise: Evidence from Cambodia

Fin-tech platforms such as mobile money services are likely to have a potential to promote socio-economic development in the developing world. Recently, the arguments about the poverty-alleviating promise of mobile money have been controversial and inconclusive. This article analyses the effects of mobile money on household poverty in terms of household income per capita in Cambodia by using an endogenous switching model with data from the Cambodia FinScope Consumer Survey conducted in 2015. A complementary analysis of the effects on domestic remittances is performed to give more insights into the potential effects by adopting an endogenous switching probit model. The results suggest that households taking up mobile money services are likely to enjoy higher household income per capita, with the complementary results illustrating that the uptake of mobile money services is likely to increase the likelihood that households receive domestic remittances. Accounting for financial literacy through its interaction with the uptake of mobile money services, the results reveal that the effects are likely greater if the household users are headed by financially-literate persons.

Childhood stunting and associated factors among irrigation and non-irrigation user northwest, Ethiopia: a comparative cross-sectional study

BackgroundStunting is a critical public health problem of developing countries like Ethiopia. Different interventions like irrigation activity have been carried out by the government of Ethiopia to improve the nutritional status of the community. However, there is scanty of data on childhood stunting and its associated factors between irrigation user and non-irrigation user.ObjectiveTo assess the magnitude of childhood stunting and its associated factors between irrigation and non-irrigation user in North Mecha District, Northwest Ethiopia.MethodsA community-based comparative cross-sectional study design was employed from October to November 2019. A systematic sampling was used to draw mothers with children age 6–59 months (582 irrigated and 582 non- irrigated household users). Data were collected using questionnaire and anthropometric measurement tools. Multivariable logistic regression was used to identify the predictors of stunting. Adjusted odds ratios with 95% CI were used to determine the degree of association between independent and outcome variable. A-p-value < 0.05 was used as cutoff point to declare statistically significant variables with the outcome variable.ResultsThe prevalence of childhood stunting (6–59 months) among irrigation users [32.8% at 95%CI [29.1%-36.7%]] was slightly lower than non-users [40.2% at 95%CI [[36.3%-44.2%]]]. However, the difference did not show significant variation. The odds of childhood stunting were higher among a child from a mother had no antenatal visit, a child whose age was between 12 and 47 months, a child from a mother who did not use water and soap always for washing hands, and a child who had fever.ConclusionThe prevalence of childhood stunting was high and did not show significant variation between irrigation and non-irrigation users. A child from mother had no antenatal visit, whose age was between 12 and 47 months, a mother who did not use water and soap always for washing hands, and who had fever were factors associated with higher child stunting. Thus, the identified modifiable factors should be strengthened to reduce stunting.

On the Gap between Domestic Robotic Applications and Computational Intelligence

Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques and computational intelligence technologies), robotic products have become available to ordinary household users. For instance, domestic robots have assisted humans in various daily life scenarios to provide: (1) physical assistance such as floor vacuuming; (2) social assistance such as chatting; and (3) education and cognitive assistance such as offering partnerships. Crucial to the success of domestic robots is their ability to understand and carry out designated tasks from human users via natural and intuitive human-like interactions, because ordinary users usually have no expertise in robotics. To investigate whether and to what extent existing domestic robots can participate in intuitive and natural interactions, we survey existing domestic robots in terms of their interaction ability, and discuss the state-of-the-art research on multi-modal human–machine interaction from various domains, including natural language processing and multi-modal dialogue systems. We relate domestic robot application scenarios with state-of-the-art computational techniques of human–machine interaction, and discuss promising future directions towards building more reliable, capable and human-like domestic robots.

Continuous biodiesel production based on hand blender technology for sustainable household utilization

Biodiesel is a widely-used, renewable and environmentally-friendly fuel. The 700 W commercial household hand blender was proposed as simple and effective continuous FAME production technique using a homogeneous catalyst based on fresh refined palm oil, waste cooking oil (WCO), and various vegetable oils as feedstocks. The continuous system was designed and the effects of reaction volume, oil and methanol flow rate, reaction temperature and sodium hydroxide catalyst loading on FAME yield and yield efficiency were explored. The steady-state FAME yield of higher than 96.5% can be achieved from both fresh refined palm oil and WCO with the energy efficiency of 18.0 × 10-4 g/J (fresh refined palm oil) and 19.0 × 10-4 g/J (WCO) (6:1 methanol to oil molar ratio, reaction volume of 200 mL, total flow rate 12 mL/min, 60 °C, 0.8 wt% sodium hydroxide for palm oil and 1.5 wt% sodium hydroxide for WCO, highest stirring speed (10,640 rpm) at 220 V, no insulation), indicating that WCO shows good potential as a feedstock of biodiesel production. All properties of the synthesized FAMEs satisfy the EN 14214 and ASTM D 6751 standards. FAME produced from palm oil showed the highest FAME content, followed by sunflower oil and corn oil. The simple and low-cost kitchen hand blender is an alternative process intensification device to efficiently produce good quality FAME for household users, which is a convenient and environmentally-friendly method. WCO can also be recycled to produce a product with high economic value.

Impact of the Lockdown during the COVID-19 Pandemic on Electricity Use by Residential Users

The COVID-19 pandemic and the associated lockdown can be regarded as a forced social experiment, the results of which show how to use energy under specific conditions. During this period, there was a reduction in electricity consumption at the level of the power system, but a different specificity distinguishes the group of household users. The article aims at presenting and analysing the identified issues concerning residential electricity users based on the experience from the COVID-19 pandemic lockdown. Data from energy meters from almost 7000 flats in Warsaw’s housing estates during the lockdown in 2020 and the analogous period before the pandemic were used. The analysis showed that, on average, residential users staying practically the whole day in their flats increased their energy consumption, but without increasing their average daily peak power, smoothing the profile in the morning hours to the level reaching the peak power that had occurred in the analogous period before the lockdown. The peak power of the sections feeding the different numbers of dwellings also remained practically unchanged during the lockdown compared to the pre-pandemic period. The pressure to work and educate remotely should contribute to an increase in the digital competence of society, which may result in an increased interest in new forms of activity and cooperation based on demand-side response and prosumption mechanisms, with digital settlements for energy exchange and services.

WITHDRAWN: Non-intrusive load monitoring technique using deep neural networks for energy disaggregation

Households have increased energy usage in recent years to exceptionally high levels, where it is not feasible anymore. Owing to increased usage of power, there is a desperate need to find a way to use resources more sustainably. One of the key reasons why energy use is not sustainable is because the consumer does not know anything about the energy consumed by intelligent devices (dishwasher, refrigerator, washing machine etc). By letting household users know the energy consumption of intelligent devices. For energy monitoring firms, the energy used by the smart devices in a household needs to be analysed. In the presence of vampire loads such as electric heaters, we use deep neural networks to disaggregate the energy use of the dishwasher in a household. The training times of the deep neural networks are also being attempted. The object of the work is to calculate the efficiency and potentiality of deep neural networks on Non-intrusive load monitoring (NILM). In addition, the essential metrics chosen are RMSE and F1 in order to determine the efficiency of the proposed algorithms. Hence, from our research, we concluded that Gated recurrent Unit (GRU) is, in the presence of vapour loads of households, the best efficiency algorithm in our study, which breaks down the energy consumption of the dishwasher. GRU is however the strongest neural network when the RMSE and F1 scores are known as the metrics. However, if we take training time as the metric, we can take the best training time, that is, 19.34 min, as a simple recurrent neural network.

Evaluating the reliability of distributed photovoltaic energy system and storage against household blackout

Distributed energy resources have been proven to be an effective and promising solution to enhance power system resilience and improve household-level reliability. In this paper, we propose a method to evaluate the reliability value of a photovoltaic (PV) energy system with a battery storage system (BSS) by considering the probability of grid outages causing household blackouts. Considering this reliability value, which is the economic profit and capital cost of PV+BSS, a simple formula is derived to calculate the optimal planning strategy. This strategy can provide household-level customers with a simple and straightforward expression for invested PV+BSS capacity. Case studies on 600 households located in eight zones of the US for the period of 2006 to 2015 demonstrate that adding the reliability value to economic profit allows households to invest in a larger PV+BSS and avoid loss of load caused by blackouts. Owing to the differences in blackout hours, households from the 8 zones express distinct willingness to install PV+BSS. The greater the probability of blackout, the greater revenue that household can get from the PV+BSS. The simulation example shows that the planning strategy obtained by proposed model has good economy in the actual operation and able to reduce the economic risk of power failure of the household users. This model can provide household with an easy and straightforward investment strategy of PV+BSS capacity.

Susceptibility of ten rice brands to weevil, Sitophilus oryzae L. (Coleoptera: Curculionidae), and their influence on the insect and infestation rate

BackgroundSusceptibility studies keep farmers, managers and household users informed and enhance breeding program’s testing against infestation and damage by storage insect pests. Therefore, laboratory tests were carried out to examine the susceptibility of ten rice brands to rice weevil, Sitophilus oryzae L. (Coleoptera: Curculionidae), infestation under temperature and relative humidity of 25 ± 2 °C and 75 ± 5%, respectively. The specific objectives of the study were to identify some commercially available rice brands with resistance to S. oryzae, by determining whether brand difference influences insect body weight at emergence and whether infestation is related to brand palatability and appearance. The ten brands used for the study were royale stallion, Mama royale, parboiled rice, Mama gold, white rice, Super eagle, Indian rice, champion rice, Abakiliki rice and Mama Africa, and standard methods were used to achieve the objectives. The indices measured were F1 progeny emergence, grain weight loss and frass accumulation.ResultsThe results showed that Abakiliki rice was poor in both palatability and appearance, whereas Super eagle was the most palatable and white rice was visually excellent. The results further showed that no brand was resistant to infestation and damage by S. oryzae. Males that were bred on the different rice brands did not differ in weight at emergence, but the weight of females at emergence was influenced by brand difference, and this suggests that female insects are more sensitive to brand difference at weight gain.ConclusionPalatability and appearance were important in the susceptibility of rice brands to S. oryzae. The proliferation of diverse genotypes of rice (branded) with enhanced resistance to S. oryzae should be strongly encouraged to reduce susceptibility and increase rice security.

Using system dynamics to assess the complexity of rural toilet retrofitting: Case study in eastern China

Rural toilet retrofitting (RTR) is a complex, dynamic system that is affected by many factors and the positive/negative feedback relationships between subsystems and variables. Traditional technologies and management methods face challenges in fundamentally describing and solving problems in RTR. To bridge this gap, this study utilizes system dynamics and causal loop diagrams to explain such problems based on data collected from the stakeholders of the RTR in Jiaozhou from 2018 to 2019. Specifically, this study examines the RTR system from the perspectives of household users, wastewater treatment plants, local governments, grassroots promoters, operation and maintenance personnel, toilet supplier and construction teams, and fecal sludge end users. The factors and processes involved in RTR are identified, and the feedback and relationships among its major stakeholders are established. Results show that the motivation of farmers to engage in RTR is a key variable that affects their final decisions regarding retrofitting and maintaining toilet functions. Meanwhile, the important variables related to the feedback and relationships among the major stakeholders of RTR are mostly focused on policies, subsidies, technology, satisfaction, and cooperation. A scientific analysis method and the updated RTR plan for toilet revolution are then formulated to promote the implementation of RTR in developing countries.