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16 results for “Household Energy”
Unclean but affordable solid fuels effectively sustained household energy equity
<p>This dataset contains the data used in preparation for the paper "Unclean but affordable solid fuels effectively sustained household energy equity" by Ke Jiang et al, describing the inequity of household energy consumption, cost and burden in mainland China in 2017.</p>
Optimised household consumption profiles through a smart building energy mangement system TABEDE
<p>In the context of the TABEDE project (<a href="https://www.tabede.eu/">https://www.tabede.eu/</a>) several synthetic profiles simulating the consumption and generation of residential buildings, whose appliances were controlled by our proposed Energy Management System (i.e., the TABEDE solution), were simulated. Their construction process was characterised by the following:</p> <ul> <li>Consumption profiles were generated via a bottom-up approach capable of emulating the consumption of individual household appliances. These last ones correspond to the most used appliances in the UK, which were randomly distributed among the buildings based on their average utilisation rate and ownership observed in residential buildings in the country.</li> <li>The physics in terms of heat exchange between neighbouring buildings and the environment were considered, together with the size of the buildings and their physical characteristics. A total of 66 houses and apartments, according to 8 type or building archetypes were created.</li> <li>PV generation profiles were generated according to the meteorological condition of the simulated day.</li> </ul> <p>Together with this, the profiles feature how the TABEDE solution optimised the flexible part of the consumption (i.e., appliances that were controllable by the solution and whose consumption could be shifted in time without sacrificing user comfort) to minimize the electricity bill of the buildings.</p> <p>The information contained in the actual database features the following variables:</p> <ul> <li>TABEDE penetration: percentage of buildings owning the TABEDE solution. Buildings with TABEDE will observe their flexible consumption being optimised.</li> <li>PV penetration: percentage of buildings with a PV system installed on them.</li> <li>Simulation day: one day in summer (19/06/2019) featuring the highest solar radiation of the year, and a day in winter (19/12/2019) with the lowest.</li> <li>Batteries: whether the PV systems is installed alongside household batteries.</li> </ul> <p>Details on the formulation can be found in: <a href="https://urldefense.com/v3/__https:/www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/__;!!La4veWw!khYBEaeJY85mX5yQUrp0PwoXcg5U10dEdgZ296hONYGyBS5xg91Z8MoDUQy34a4f9Lo$">https://www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/</a></p>
ENABLE.EU H2020 project dataset and questionnaire from a survey of households on energy use and energy choices
<p>The ZIP archive includes the anonymized micro-data (survey results) and the respective questionnaire from the survey of households in eleven countries, conducted as part of the H2020 project "<a href="http://www.enable-eu.com">Enabling the Energy Union through understanding the drivers of individual and collective energy choices in Europe</a>" (ENABLE.EU). </p> <p>The countries are: Bulgaria, France, Germany, Hungary, Italy, Norway, Poland, Serbia, Spain, Ukraine, and the United Kingdom.</p> <p>The dataset consists of 11 267 completed questionnaires (cases). </p> <p>The ZIP archive includes the following files:<br> • ENABLE.EU survey questionnaire for households in PDF format;<br> • ENABLE dataset from the survey of households in SAV format for IBM SPSS;<br> • ENABLE dataset from the survey of households in DTA format for STATA (the dataset is produced by simple export from SAV format and could contain some differences due to export limitations; If possible, we recommend to use the SAV-SPSS format);<br> • ENABLE dataset from the survey of households in XLSX format for Microsoft Excel, which includes also corresponding tables for the labels of questions and answers.</p> <p>For more information about the survey methodology and survey results please see: "D4.1 Final report on comparative sociological analysis of the household survey results" under the section <a href="http://www.enable-eu.com/downloads-and-deliverables/">Downloads / Deliverables</a> at the ENABLE.EU web-site. </p>
SHEERM: Sustainable Household Energy and Environment Resources Management dataset
<p>This dataset represents a novel and extensive dataset featuring comprehensive cross-sectional data of household electrical load, energy cost, and on-premises solar energy production, directly linked to solar radiation and weather parameters. <br>The SHEERM dataset is essential for understanding and optimizing energy utilization to achieve Sustainable Development Goals (SGD) 7, 9, 11 and 13. It provides data about solar energy production, weather conditions, residential energy needs, and market prices. The combination of these variables facilitates multifaceted analysis, fostering advancements in renewable energy forecasting, climate-sensitive environments, grid management, and energy policy formulation.<br>Together with the SHEERM dataset, there is a paper that details the data collection process, including the sources and methodologies employed. Adhering to established literature, we developed and implemented machine learning models that comprehensively validate the data. Furthermore, as usage notes, we offer additional results by applying various machine-learning approaches to the provided data.<br>The SHEERM dataset aims to help design new energy systems that enhance sustainable energy strategies and demonstrate their potential to accelerate the transition towards renewable energy and carbon neutrality.</p>
Lietuvos namų ūkių apklausa energetikos klausimais (Lithuanian Household Survey on Energy Issues)
<p>Duomenų rinkinyje pateikiami reprezentatyvios Lietuvos namų ūkių apklausos (N=1008) apie Lietuvos namų ūkių energetikos situaciją ir su valstybės parama šioje srityje susijusias žinias. Apklausos klausimyną parengė Lietuvos energetikos instituto mokslininkai, o apklausos lauko darbus atliko UAB "Vilmorus" 2020 m. lapkričio mėn. 16 d. – gruodžio mėn. 7 d.</p> <p> </p>
Lithuanian Household Energy Expenditure and Energy Poverty Data, 2019
<p>This dataset provides data about household energy expenditure and energy poverty in Lithuania. The dataset contains detailed data about 5031 households and is based on the Lithuanian Survey on Income and Living Conditions (2019) micro dataset provided by Statistics Lithuania. It includes additional data derived from original survey data and energy poverty calculation results at household level.</p> <p>Duomenų rinkinyje pateikiami duomenys apie namų ūkių energijos išlaidas ir energijos nepriteklių Lietuvoje 2019 metais. Duomenų rinkinys apima 5131 namų ūkį. Rinkinio pagrindas - Pajamų ir gyvenimo sąlygų statistinio tyrimo duomenys, skelbiami Lietuvos Statistikos departamento. Duomenų rinkinys apima ir papildomus duomenis gautus remiantis originalios apklausos duomenimis bei energijos nepritekliaus skaičiavimų rezultatus namų ūkio lygmenyje.</p>
Categorical variables based on cross country household survey on energy consumption
<p>The data used in this file was collected via two large-scale surveys conducted in Italy, Switzerland and the Netherlands. A total of 6,138 responses were recorded, containing information on socio-demographic and socio-psychological characteristics, dwelling and household characteristics, technologies and energy services used, and their metered electricity consumption. There were a large number of missing responses for metered electricity consumption in the Netherlands, leading to an under-representation of data from this country. The survey responses were used to construct newly defined energy efficiency indicators, and energy service indicators. This allows two distinct factors to be separated: service consumption, and energy efficiency relative to the demanded service. Firstly, dwelling characteristics and survey responses related to energy services (e.g. floorspace, ownership of specific appliances and number of lightbulbs), were regressed to the collected metered electricity data. For each household, this allowed us to calculate the expected lighting and appliance electricity demand based on the level service that the household demanded, which is referred to as lighting and appliance service demand indicators. The idea is that a larger house, or a house with more appliances for example is expected to use more electricity. Relative to this expected electricity demand energy efficiency can be calculated. All variables are categorised in categorical variables deducted based on the questions asked in the two surveys. The survey responses were clustered based on the lighting service demand, appliance service demand and the efficiency gap (k-means clustering with Jaccard dissimilarity measure) which is described in Edelenbosch, Miu et al (2022). Translating observed household energy behaviour to agent-based technology choices in an integrated modelling framework. <em>Iscience</em> (accepted).</p>
Load Shifting Optimization with Genetic Algorithms for Energy Cost Minimization in Households - Case Study Data2
<p>The case study of this dataset uses real household data, representing five days from 0h00 to 23h59. This dataset uses a period of 15 minutes for all loads execution time and energy data. The case study considers twenty unique houses that can have up to five different shiftable appliances, each executing three process cycles.<br> <br> File Description:</p> <ul> <li>Case_Studies_Data-BAU_and_Load_Shifting - Excel containing appliances energy profile, load execution preferences, BAU consumption, and houses' data</li> <li>Houses_Input_Output_JSONs_and_Statistics - Zip containing the input and output files from the proposed system, as well as their corresponding schedule statistics</li> </ul>
Load Shifting Optimization with Genetic Algorithms for Energy Cost Minimization in Households - Case Study Data
<p>The case study of this dataset uses real household data, representing five days from 0h00 to 23h59. This dataset uses a period of 15 minutes for all loads execution time and energy data. The case study considers twenty unique houses that can have up to five different shiftable appliances, each executing three process cycles.<br> <br> File Description:</p> <ul> <li>Case_Studies_Data-BAU_and_Load_Shifting - Excel containing appliances energy profile, load execution preferences, BAU consumption, and other house data</li> <li>Houses_Input_JSONs - Zip containing the input files, from each house, for the proposed system</li> </ul>
Micro data from the 2012 Greek Household Energy Consumption survey
<p>The dataset is a cleaned and modified version of the microdata from the 2012 Greek Household Energy Consumption survey</p>
Energy Choices Survey: Twin Cities Household Ecosystem Project
We designed our methods to estimate carbon, nitrogen, and phosphorus fluxes through individual households, and to address two primary questions: 1. How are these fluxes distributed across households? 2. What biophysical and socioeconomic factors contribute to differences in these fluxes across households? Our hybrid approach combines: 1. A mailed survey 2. Energy provider records 3. On-the-ground landscape measurements 4. A computational tool (the Household Flux Calculator) 4. Parcel data (interpreted using GIS) The resulting dataset includes information on biophysical and socioeconomic variables that potentially influence household-level fluxes of elements. Using this method to study element fluxes at the household level allows us to explicitly link consumption choices and element fluxes.
Energy Use Survey: Twin Cities Household Ecosystem Project
We designed our methods to estimate carbon, nitrogen, and phosphorus fluxes through individual households, and to address two primary questions: 1. How are these fluxes distributed across households? 2. What biophysical and socioeconomic factors contribute to differences in these fluxes across households? Our hybrid approach combines: 1. A mailed survey 2. Energy provider records 3. On-the-ground landscape measurements 4. A computational tool (the Household Flux Calculator) 4. Parcel data (interpreted using GIS) The resulting dataset includes information on biophysical and socioeconomic variables that potentially influence household-level fluxes of elements. Using this method to study element fluxes at the household level allows us to explicitly link consumption choices and element fluxes.
Energy Efficiency Survey: Twin Cities Household Ecosystem Project
We designed our methods to estimate carbon, nitrogen, and phosphorus fluxes through individual households, and to address two primary questions: 1. How are these fluxes distributed across households? 2. What biophysical and socioeconomic factors contribute to differences in these fluxes across households? Our hybrid approach combines: 1. A mailed survey 2. Energy provider records 3. On-the-ground landscape measurements 4. A computational tool (the Household Flux Calculator) 4. Parcel data (interpreted using GIS) The resulting dataset includes information on biophysical and socioeconomic variables that potentially influence household-level fluxes of elements. Using this method to study element fluxes at the household level allows us to explicitly link consumption choices and element fluxes.
Micro data from the Greek Household Budget Survey on energy consumption
<p>The dataset is the end product of compiling a number of datasets from the Greek Household Budget Survey, covering 13 years between 2004 and 2020. The variables included are basic socio-economic, demographic and housing characteristics, along with quantity and cost data of household energy sources.</p>
Survey data on norwegian household energy use, with focus on solar PV, flexible energy use, and retrofitting in 2023 (variables dictionary)
<p>A variable dictionary (both in Norwegian and English) is added. Please note that Norwegian characters like <strong>ø</strong> may not display correctly in the webpage preview.<br>To view them properly, download the CSV file—all characters will appear correctly there.</p>
Sustainable Household Energy Adoption in Rwanda (SHEAR)
ClinicalTrials.gov study NCT05668624. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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