Household Profiles Research Articles

Short Term Load Forecasting for Smart Grids Using Apache Spark and a Modified Transformer Model

Smart grid is an advanced electrical grid that enables more efficient distribution of electricity. It counters many of the problems presented by renewable energy sources such as variability in production through techniques like load forecasting and dynamic pricing. Smart grid generates massive amounts of data through smart meters, this data is used to forecast future load to adjust distribution. To process all this data, big data analysis is necessary. Most existing schemes use Apache Hadoop for big data processing and various techniques for load forecasting that include methods based on statistical theory, machine learning and deep learning. This paper proposes using Apache Spark for big data analysis and a modified version of the transformer model for forecasting load profiles of households. The modified transformer model has been tested against several state-of-the-art machine learning models. The proposed scheme was tested against several baseline and state-of-the-art machine learning models and evaluated in terms of the RMSE, MAE, MedAE and R2 scores. The obtained results show that the proposed model has better performance in terms of RMSE and R2 which are the preferred metrics when evaluating a regression model on data with a large number of outliers.

Survey and Analysis of Local Energy Markets Based on Distributed Ledger Technologies

Local energy markets (LEMs) provide opportunity to handle the challenges arising from the lower grid level while using the traditional top-down approach to manage distributed generated renewable energy resources. Blockchain based local energy markets (LEMs) have been introduced in recent years as a way to enable local consumers/prosumers to trade their energy locally in a distributed and highly secured manner in an LEM. However, there are still some challenges regarding the main factors that can drive local consumers/prosumers to participate in a blockchain based LEM, optimal community size and prosumer to consumer ratio for an efficient LEM. Also, there is still no information on how the quantifying factors for participation on a blockchain based LEM can affect the performance of an LEM. This paper presents a survey and simulation based analysis of quantifying factors for participation in a blockchain based LEM. The survey was distributed among local consumers/prosumers and a total of 261 responses were received from the responders. The results from the responders were analyzed using a Python code based statistical analysis model. The simulation based analysis was conducted using a community based LEM model and evaluated using data received from a combination of German household profiles and standard load profiles. The survey results showed that the major drive for local consumers/prosumers to participate on blockchain based LEM is their willingness to support renewable energy integration, transparency and trust offered by a blockchain network. On the other hand, the simulation based analysis showed that small and medium communities with prosumers to consumer ratios between 0.3 to 0.5 create more economic and technical benefits for local consumers/prosumers compared to large communities. The community based simulation results were modelled together with the survey results to determine how the individual quantifying factors for participating in a blockchain based LEM can affect the performance of an LEM.

Overview and Comparative Study of Energy Management Strategies for Residential PV Systems with Battery Storage

PV and battery systems have been widely deployed in residential applications due to increasing environmental concerns and fossil energy prices. Energy management strategies play an important role in reducing energy bills and maximize profits. This paper first reviews the state of energy management problems, including commonly used objectives, constraints, and solutions for PV and battery applications. Then, a comparative study of different energy management strategies is conducted based on a real applied product and household profile. Moreover, results are discussed, and suggestions are given for different scenarios. Finally, conclusions and insights into future directions are also provided.

Changes in the determinants and spatial distribution of under-five stunting in Bangladesh: Evidence from Bangladesh Demographic Health Surveys (BDHS) 1996-97, 2014 and 2017/18.

Bangladesh has experienced tremendous change in child nutrition over the past few decades, but there are large differences between different regions in progress made. The question is whether continuation of current policies will bring the progress needed to reach national and international targets on child nutrition security. Using national data BDHS 1996/97, 2014, and 2017, this study attempts to map such reductions across Bangladesh and to explore the distribution of covariate effects (joint effects) that are associated with childhood stunting over these two periods, overall and by region. The main contribution of this paper is to link observed stunting scores to a household profile. This implies that different variables are evaluated jointly with stunting to assess the likelihood of being associated with stunting. Overall, the covariates: 'Parental levels of education', 'children older than one year old', 'children live in rural area', 'children born at home' formed the country winning profile in 1996/97, whereas parental levels of education disappear in the winning profile for children stunted in 2014. This implies that over the years, Bangladesh has been successful in addressing parental education for long-term reductions in child undernutrition. In addition, the diversity of profiles of households with stunted children increases over time, pointing at successful targeting of policies to increase food security among children over the period. However, in areas where improvements have been insignificant, also the profiles remain stable, indicating a failure of policies to reach the target populations. The analysis for 2017 confirms this picture: the diversity of profiles remains high, with little change in the dominant profiles. Further decline in stunting is possible through region specific multipronged interventions, targeting children older than one year among vulnerable groups, in addition with strengthening family planning programs as larger families also have a higher risk to have stunted children. In general, the profiles in 2014 and 2017/18 are much more diverse than in 1996, which can be explained by the relative success of specific targeted policies in some divisions, while being much less successful in other regions. In sum, our results suggest that the challenge lies in the implementation of policies, rather than in the generic approach and assumed theory of change.

A cross‐sectional analysis of the demand for coffee in the United States

AbstractThe purposes of this study were to develop a profile of households who purchase coffee, to determine the socioeconomic determinants of the demand for coffee by US households, and to update the own‐price, cross‐price, and income elasticities of demand for US households. The profile suggests to targeting wealthier households, households without children living in the household, older households, white households, and households located in the Pacific region and in New England. The own‐price elasticity of −1.93 indicates that the demand for at‐home purchases of coffee is elastic. Tea is a substitute for coffee. Given the income elasticity of 0.20, changes in household income are not likely to have sizeable impacts on at‐home coffee consumption. Nevertheless, coffee is a necessity in economic parlance. The source of data for this analysis was the NielsenIQ pertaining to 61,380 households for calendar year 2015. [EconLit Citations: D1, D9, D12].

Profiling consumers with an environmentally sustainable and healthy diet: The case of Spanish households.

Our diet has substantial implications not only for our health but also for the environment. However, the two dimensions are not comparable, even though consumers often associate them with their purchasing choices. Promoting more sustainable diets requires a better knowledge of household profiles considering the healthy and organically sustainable character of the food purchased. Previous studies have approached the analysis of consumer profiles separately, differentiating both dimensions without clear conclusion regarding the variables that make up these profiles. In this study, we looked for household profiles by cross-referencing the organic nature of the products consumed (environmental sustainability) with their degree of processing (healthfulness) in Spain. The results show that the most sustainable products are consumed in tiny municipalities (less than 2,000 inhabitants). In contrast, less sustainable products are consumed in high-income, single-family households or households with small children. The person responsible for the purchase is working or between 39 and 45 years old. In conclusion, our study shows that socio-demographic variables are statistically significant in identifying household profiles with sustainable diets.

Intelligent wild geese algorithm with deep learning driven short term load forecasting for sustainable energy management in microgrids

Energy management in power grids becomes essential to reduce the cost for the consumer and improve the power supply reliability. The microgrid is a vital part of the smart grid and it requires intelligent power management approach for effective functioning. Presently, delivering demand load and sustaining energy are two major challenges that exist in the power system. To resolve these problems, short-term load forecasting (STLF) models have been presented as an effective management and energy supply mode in power systems. The recently developed deep learning (DL) and machine learning (ML) models can be employed for accurate STLF in microgrids. In this view, this study presents an intelligent wild geese algorithm with deep learning driven short term load forecasting (IWGADL-STLF) model for sustainable energy management in microgrids. The proposed IWGADL-STLF model intends to accurately and rapidly predict the STLF in the microgrids. To accomplish this, the IWGADL-STLF model uses attention based Bi-directional long short term memory (ABiLSTM) model which involves the input parameters as formation of household and commercial load profiles with commercial load profile of the microgrid as output. The proposed IWGADL-STLF model identifies the behavioural patterns of parameters and models the behaviour in short time period for effective prediction process. Since hyperparameters play a vital role in the DL models, in this study, WGA is applied as a hyperparameter optimizer of the ABiLSTM model. The IWGADL-STLF approach has shown effective results with low MAE, MAPE, and R2 values. A comprehensive experimental analysis reported the enhanced performance of the presented model over the other existing approaches under several aspects.

Development of a probabilistic behavioural model creating diverse A/C operation patterns of households

Studies that attempt to model real patterns of residential occupant behaviour have become increasingly popular over the last decade. Most research of this kind tend to produce behaviour profiles based on an average occupant, yet the prediction of residential energy consumption varies significantly according to behavioural profiles of individual households. Oftentimes, averaged behavioural profiles are inadequate for predicting energy demands accurately, therefore requiring models to incorporate the diversity of occupants’ behaviours. The main objective was to develop an air-conditioning (A/C) usage model that produces diverse A/C cooling and heating operation patterns in residential settings. Longitudinal field observations were conducted in 41 Brisbane region households of Australia over a one-year period. Regression coefficients in the model were set to be drawn from a normal distribution to reproduce observed variability in A/C operation patterns amongst the households. Simulations were performed on a validation dataset to test the predictive skill of the model. The result indicated that the frequency and duration of A/C usage observed in the validation dataset well matched with the prediction made by the model. The proposed model could be incorporated in building energy simulation tools to predict realistic energy use for A/C in a given residential building design.

Real-time recommendations for energy-efficient appliance usage in households.

According to several studies, the most influencing factor in a household's energy consumption is user behavior. Changing user behavior to improve energy usage leads to efficient energy consumption, saving money for the consumer and being more friendly for the environment. In this work we propose a framework that aims at assisting households in improving their energy usage by providing real-time recommendations for efficient appliance use. The framework allows for the creation of household-specific and appliance-specific energy consumption profiles by analyzing appliance usage patterns. Based on the household profile and the actual electricity use, real-time recommendations notify users on the appliances that can be switched off in order to reduce consumption. For instance, if a consumer forgets their A/C on at a time that it is usually off (e.g., when there is no one at home), the system will detect this as an outlier and notify the consumer. In the ideal scenario, a household has a smart meter monitoring system installed, that records energy consumption at the appliance level. This is also reflected in the datasets available for evaluating such systems. However, in the general case, the household may only have one main meter reading. In this case, non-intrusive load monitoring (NILM) techniques, which monitor a house's energy consumption using only one meter, and data mining algorithms that disaggregate the consumption into appliance level, can be employed. In this paper, we propose an end-to-end solution to this problem, starting with the energy disaggregation process, and the creation of user profiles that are then fed to the pattern mining and recommendation process, that through an intuitive UI allows users to further refine their energy consumption preferences and set goals. We employ the UK-DALE (UK Domestic Appliance-Level Electricity) dataset for our experimental evaluations and the proof-of-concept implementation. The results show that the proposed framework accurately captures the energy consumption profiles of each household and thus the generated recommendations are matching the actual household energy habits and can help reduce their energy consumption by 2–17%.

An Assessment of Food Security Status and Contribution of Livestock in Households Consumption Profile: A Comparative Study in Different Regions of Punjab

Livestock is one of the fastest-growing sectors, especially in emerging economies, to meet the projected demand for protein for the growing population. Livestock’s role is beyond its contribution to meat and milk. It affects food security in multiple ways. The current study is designed to assess the food security status of households having livestock and the caloric contribution of livestock in the consumption profile of households. For this purpose, a sample of 456 households was collected from three districts (152 from each Bahawalpur, Faisalabad, and Rawalpindi) of Punjab, representing the southern, central, and northern regions of the province. The calorie intake of households is assessed by studying the consumption profile and evaluation of generated energy in terms of calories to assess food security. The contribution of livestock is estimated from cumulative energy generated in the form of calories from the consumption of livestock products. Data analysis reflects that 59% of Bahawalpur households are food secure compared to Faisalabad, where 63% of households are food insecure. Similarly, Rawalpindi has 60% of households with food security status. The households with livestock and whose women participate in livestock activities extract 20% of their nutrition from animal sources in Bahawalpur, 20% in Faisalabad, and 30% in Rawalpindi. The overall food security situation can be improved by increasing the share of livestock products in the household’s consumption profile.

Spatiotemporal change in duration of households with every member out-of-home: a case in Kumamoto, Japan

ABSTRACT Nobody-at-home situations can cause several problems, such as home-delivery failures and burglaries. A recent study demonstrated temporal profiles of households with every member out-of-home (HEMO) situation by using household travel surveys. However, the spatial distribution of HEMO and its transition were not examined, and the effect of household attributes on HEMO was not analyzed statistically. In this study, the spatiotemporal variation in HEMO duration was investigated to address this gap, and the duration was analyzed using two econometric models. The 1984, 1997, and 2012 household travel surveys from Kumamoto, Japan, were used for the spatiotemporal visualization. In addition, the Tobit model and time allocation model were developed to statistically determine the reason for the variation in duration. The average HEMO duration increased by more than 1 h between 1984 and 2012. The downtown area revealed a longer HEMO duration, and the area with a longer duration expanded to rural areas between 1984 and 2012. The estimated econometric models revealed the statistical impacts of household attributes on HEMO duration. The HEMO duration of single-person households with a worker or student was long, and that of households with a working husband and homemaker wife was short. The spatiotemporal distribution of HEMO durations presented in this paper has the potential to be used in future urban studies, including those on the logistics of home-delivery, home-visiting survey design, crime prevention, and energy research.

Household profiles of neglected tropical disease symptoms among children: A latent class analysis of built-environment features of Tanzanian households using the Demographic and Health Survey.

BackgroundWhile malaria and neglected tropical disease (NTD) morbidity and mortality rates among children <5 years old have decreased through public health efforts in the United Republic of Tanzania, associations between household environments and disease outcomes are relatively unknown.MethodsWe conducted latent class analysis (LCA) on 2015-2016 Tanzania Demographic Health Survey data from mothers with children <5 years old (N = 10 233) to identify NTD household risk profiles. The outcome of child NTD was assessed by mothers’ reports of recent diarrhoea, cough, treatment for enteric parasites, and fever symptoms. Household-built environment indicators included urban/rural designation, electricity access, water source, cooking fuel, flooring, wall, and roofing materials. External environmental covariates were considered to further differentiate profiles.ResultsFive profiles were identified in the sample: rural finished walls households (40.2%) with the lowest NTD risk; rural rudimentary households (20.9%) with intermediate-low NTD risk; finished material households (22.5%) with intermediate NTD risk; urban households (14.4%) with intermediate-high NTD risk and high likelihood of enteric parasites; rural finished roof/walls households (2.1%) with the highest overall NTD risk.ConclusionsThis study is among the first to use LCA to examine household environment characteristics to assess child NTD risk in Tanzania. This paper serves as a framework for community-level rapid NTD risk assessment for targeted health promotion interventions.

The COVID-19 pandemic: access to relief under social assistance programs by rural households in India

PurposeThis paper aims at analyzing the determinants of access to relief under social assistance programs among rural households during COVID-19 outbreaks in India.Design/methodology/approachThe study is based on the data of COVID-19-Related Shocks Survey, which covered 5,200 rural households across 6 states of India namely Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh Rajasthan, and Uttar Pradesh. The access to relief has been assessed as relief-in kind (RIK) as a free special package of wheat, rice, and pulses, supplied through the public distribution system; and direct benefit transfer (DBT) in cash under the Pradhan Mantri Kisan Samman Nidhi (PM-KISAN) Yojana and the Pradhan Mantri Jan-Dhan Yojana (PMJDY). The association between demographic profiles of rural households and access to relief has been analyzed using the chi-square test. Further, marginal effects have been estimated to assess the determinants of rural households' access to relief.FindingsThe results show a significant association between types of relief vis-à-vis demographic profiles of the rural households. A significant difference in access to relief among rural households is also evident across the states. Further, the analysis of the marginal effects indicates that female-headed households belonging to lower social class, depending on non-agricultural occupation with lower income, belonging to below poverty line families and seeking wage employment, are more likely to access relief as food grains; whereas male respondents with lower age, belonging to lower-income quartile with memberships in Self Help Groups are more likely to access the cash benefit transfers.Practical implicationsThe COVID-19 pandemic has affected the food security and livelihood of many across the globe, which necessitated provisioning a package of support to everyone, particularly rural poor households. The World Bank undertook the COVID-19-Related Shocks Survey to provide a quick policy response for managing the risk of COVID-19 outbreak effectively. The results of this study provide timely insights for developing an effective relief strategy for rural households during a crisis.Originality/valueThere is limited investigation on access to relief by rural households during the COVID-19 outbreaks and factors affecting the access to relief in terms of cash and kind. This study has utilized a reliable data source to analyze the access of relief packages by the rural communities during the coronavirus outbreak.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2021-0632.

From energy behaviours to lifestyles: Contribution of behavioural archetypes to the description of energy consumption patterns in the residential sector

Many studies emphasise that household energy behaviour is a key determinant of domestic energy consumption, but few highlight the fact that it is fundamentally associated with lifestyle and housing context. Existing studies that address this issue perform clustering but use only restricted datasets and rarely describe the associated households and housings. In this study, we develop an original methodology to construct behavioural archetypes from qualitative and quantitative variables, opening up perspectives for a wider and more transparent use of survey data. The clustering is performed with 35 variables describing hygiene, food, heating, lighting and leisure practices and housing occupation. Seven homogeneous archetypes of domestic behaviours were constructed from a database describing 1363 households in the Ile de France region. The analysis of the related household profiles shows that the behavioural archetypes are related to specific housing contexts and energy consumption levels. The work also invites to stand back from the variables usually mobilized, such as income or tenure status, and used for the conception of targeted policies. In particular, the work highlights the value of the household life cycle in constructing a typology suitable for policy makers. Finally, this work opens up avenues for the construction of archetypal energy consumption models.

Terrace Gardening: A Sustainable yet Economic Approach

The study emphasizes the importance of terrace gardening in providing fresh, seasonal, and most importantly, chemical-free vegetables for everyday family requirements. It also tackles the problem of odorous, untouchable and unwanted kitchen waste by converting it into valuable organic manure that may be used as a low-cost, environmentally acceptable input for organic terrace gardening. This study was conducted with the ultimate aim to document the socio-economic profile of households practicing terrace gardening and list out the perceived benefits and constraints in adoption of terrace gardening. Ninety sample respondents who were practicing terrace gardening in Hyderabad were chosen for the study. Respondents who were practicing terrace gardening for the past 5 years were selected randomly from the list obtained from Horticulture Training Institute. The results inferred that majority of the respondents perceived that terrace gardening was providing them fresh fruits and vegetables with minimal investment as well as increased emotional well-being. Lack of availability suitable land near residential areas is identified as the major constraint and followed by lack of time was reported along with other constraints.

Anthropological Study of the Perceptions of Neighboring Households Related to the Causes and Consequences of Pollution in the Ebrié lagoon of Koumassi (Abidjan)

Our study aimed to analyze the perceptions of riparian households related to the causes and consequences of the pollution of the Koumassi Ebrié lagoon. It is based on the mixed approach combining quantitative and qualitative method, using documentary research, direct field observations, the survey by questionnaire, semi-directive individual interviews and Focus Group. The results obtained first show the socio-demographic profile of households. For this purpose, the average household size is 6 people. In addition, households operate such as fishing, lagoon transport, selling fish products, fish smoking. In addition, the average income of households is between 10,000 and 50,000 FCFA. The causes mentioned by households, at the origin of the pollution of the Ebrié Lagoon of Koumassi are: (1) household waste spills and domestic wastewater from activities, at the same time, riparian and abidjanese populations, directly In the lagoon, (2) wastewater spills from the activities of the industries installed near the lagoon and (3) the use of toxic products (pesticides) by certain fishermen for their activity (fishing). Finally, the results of the study show that the pollution of the Koumassi Ebrié lagoon impact the fishery resources, the environment and habitats of respondents. Also the study reveals the impact of pollution on health, food, rituals and household leisure.

Association between household cleaning product profiles evaluated by the Ménag'Score® index and asthma symptoms among women from the SEPAGES cohort.

Considering household disinfectants and cleaning products (HDCP) as mixture of ingredients, rather than each ingredient individually, might help in characterizing their role in asthma. We investigated the association between HDCP and asthma, using the recently developed Ménag'Score®, a health risk assessment score based on exhaustive ingredient lists of HDCP. The study is based on 103 female volunteers of the SEPAGES cohort (2014-2019), with repeated data (up to 3 collection times, 200 observations). HDCP use was assessed from a barcode-based smartphone application linked with an ingredient database. The Ménag'score® risks for health and environment were computed for each weekly used HDCP from their exhaustive ingredient data (from A: no known risk to E: highest risk). The association between the use of HDCP with a poor Ménag'score® (D or E; overall, health, environment scores) and asthma symptoms, was estimated by generalized estimating equations models adjusted for age, BMI and smoking status. Participants were on average 33years old, 11% smoked and 20% had at least one asthma symptom. The Ménag'score® was computed for 540 HDCP scanned by participants. Weekly use of HDCP with a poor Ménag'score®-health (around 60% of the participants) was associated with a higher risk of asthma symptoms (OR 3.13, 95% CI [1.32-7.43]). No association was observed for the Ménag'score®-environment. The use of HDCP with a poor Ménag'score®-health was associated with asthma symptoms. The results support the use of the Ménag'score®-health to further evaluate the health risks of HDCP in observational studies and as a potential public health tool.

Estimates of households consumer behavior in Russia

In this paper, the consumption profiles of households in the Russian Federation are constructed and, on their basis, the parameters of future discounting and relative risk aversion are evaluated. The paper demonstrates that the age profiles are hump-shaped, the peak consumption level is reached at 38—40 years. The important determinant of the hump shape is the composition of the household: when consumption is recalculated for the average individual in a household, the hump shape becomes less pronounced. On average, households have a high level of relative risk aversion and a low level of future discounting. This configuration of parameters indicates the important role of the precautionary motive in the consumer behavior of households. Estimates are presented for households with different levels of education and number of people.

Optimization of Micro Gas Turbine Based Hybrid Systems for Remote Off-grid Communities

Inherent fuel flexibility of micro gas turbine (MGT) makes the engine a promising energy solution to remote Canadian communities that are not connected to the North American electricity grid, where bio-oils derived from locally available bio-mass may be utilized to meet local power and heat demands to reduce fossil fuel consumption. The switch to bio-oils enabled by MGTs reduces not only carbon footprints but also operating expenses due to high transportation costs of fossil fuels. However, MGT efficiencies are greatly reduced at partial loads. This work investigates the feasibility of addressing MGT efficiency drops at partial loads by incorporating MGT with a Battery Energy Storage System (BESS) to form a hybrid system so that the MGT can be operated at near full power at all times for better efficiencies. In this study, a daily power demand profile of a typical Canadian household is adopted for optimizing battery size and MGT operating strategies. By optimizing MGT daily start time and the engine's threshold partial load factor, the specific fuel consumption and battery size can be minimized for a specific number of households on a micro-grid supported by the MGT-based hybrid power system.

A comparative analysis of NFHS-4 and 5 household profile indicators in districts of Haryana, India

National family health survey is a large scale, multi-round survey conducted in a representative sample of households throughout India. Till date there are five rounds of the NFHS surveys were conducted starting from NFHS-1 in 1992-93 followed by NFHS-2 in 1998-99 after that NFHS-3 in 2005-06, then NFHS-4 in 2015-16 and lastly and recently conducted NFHS-5 from 2019 to 2021. NFHS - 5 has been captured during 2019-20 and has been conducted in around 6.1 lakh households. The international institute for population sciences Mumbai, served as the nodal organization providing coordination and technical guidance for the NFHS. In order to monitor the ongoing health and family welfare programs in between two census, NFHS is being undertaken. Household’s profile indicators of NFHS-4 and 5 in this article comprised of improved sanitation facility, improved drinking water source, using iodized salt, coverage under a health insurance, a household with electricity and using clean fuel for cooking. The comparison will help in understanding where the policy and program makers need to be more focused as per administrative division of the state and area of household profile, in collaboration with National health programs and policies.