Browse
Submit Data
Databases
API
Help

Dataset Information

31 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Functional Data Analysis of high-frequency load curves reveals drivers of residential electricity consumption.

ABSTRACT: Smart energy meters generate real time, high frequency data which can foster demand management and response of consumers and firms, with potential private and social benefits. However, proper statistical techniques are needed to make sense of this large amount of data and translate them into usable recommendations. Here, we apply Functional Data Analysis (FDA), a novel branch of Statistics that analyses functions-to identify drivers of residential electricity load curves. We evaluate a real time feedback intervention which involved about 1000 Italian households for a period of three years. Results of the FDA modelling reveal, for the first time, daytime-indexed patterns of residential electricity consumption which depend on the ownership of specific clusters of electrical appliances and an overall reduction of consumption after the introduction of real time feedback, unrelated to appliance ownership characteristics.

SUBMITTER: Fontana M

PROVIDER: S-EPMC6592564 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Functional Data Analysis of high-frequency load curves reveals drivers of residential electricity consumption.

Fontana Matteo M Tavoni Massimo M Vantini Simone S

PloS one 20190625 6

Smart energy meters generate real time, high frequency data which can foster demand management and response of consumers and firms, with potential private and social benefits. However, proper statistical techniques are needed to make sense of this large amount of data and translate them into usable recommendations. Here, we apply Functional Data Analysis (FDA), a novel branch of Statistics that analyses functions-to identify drivers of residential electricity load curves. We evaluate a real time ...[more]

PMID: 31237923

Similar Datasets

Data-driven load profiles and the dynamics of residential electricity consumption.

Project description:The dynamics of power consumption constitutes an essential building block for planning and operating sustainable energy systems. Whereas variations in the dynamics of renewable energy generation are reasonably well studied, a deeper understanding of the variations in consumption dynamics is still missing. Here, we analyse highly resolved residential electricity consumption data of Austrian, German and UK households and propose a generally applicable data-driven load model. Specifically, we disentangle the average demand profiles from the demand fluctuations based purely on time series data. We introduce a stochastic model to quantitatively capture the highly intermittent demand fluctuations. Thereby, we offer a better understanding of demand dynamics, in particular its fluctuations, and provide general tools for disentangling mean demand and fluctuations for any given system, going beyond the standard load profile (SLP). Our insights on the demand dynamics may support planning and operating future-compliant (micro) grids in maintaining supply-demand balance.

| S-EPMC9357012 | biostudies-literature

Data for electricity consumption, thermo-physical characteristics of residential buildings in Tehran.

Project description:This data article presents data related to 100 residential units in Tehran, Iran. For each building the ``average monthly electricity consumption'' data for 190 months has been provided. In addition, some physical characteristics of each building are given in the dataset. Presented data includes collected electricity end-users in residential units as well as thermos-physical characteristics of the buildings through a structured questionnaire. The physical characteristics covered in this study include; the number of floors, morphology of the yard and the ratio of the windows on each side window material, area, floor number of the studied residential unit, location in the city, cooling system, heating system, and so on. Information provided in this data article can be useful for research on energy prediction studies and also energy management strategies, and policy making to achieve sustainability targets. Having few datasets published on this topic, especially in hot and arid geographic regions, authors believe that the result of this data study can be generalized to the larger region of the Middle East and North Africa (MENA). The data consists of two parts; 1. Physical specifications of the building and 2. History of electricity consumption.

| S-EPMC8761682 | biostudies-literature

Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data.

Project description:In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smart homes complexes that use renewable energy sources as a part of their consumed electricity, overcoming the limitations of not having available historical meteorological data and the unwillingness of the contractor to acquire such data periodically in the future accurate short-term forecasts from a specialized institute due to the implied costs. In this purpose, we have developed a mixed artificial neural network (ANN) approach using both non-linear autoregressive with exogenous input (NARX) ANNs and function fitting neural networks (FITNETs). We have used a large dataset containing detailed electricity consumption data recorded by sensors, monitoring a series of individual appliances, while in the NARX case we have also used timestamps datasets as exogenous variables. After having developed and validated the forecasting method, we have compiled it in view of incorporating it into a cloud solution, being delivered to the contractor that can provide it as a service for a monthly fee to both the operators and residential consumers.

| S-EPMC5981650 | biostudies-literature

Data on the daily electricity load profile and solar photovoltaic (PV) system components for residential buildings in Lagos, Nigeria.

Project description:This article contains the average daily electric load profile (for 24 h of the day) for the five categories of residential buildings (duplex, single family bungalow, traditional court yard, flat/apartment dwelling and 'face-me-I-face-you') in three Local Government Areas (LGAs) of the state of Lagos, Nigeria. In each of the LGAs, 10 buildings per residential building type were surveyed for the collection of data with the aid of a questionnaire. In each surveyed household, a household member completed the energy audit section of the questionnaire with the assistance of the questionnaire administrator while the section of the questionnaire designed as a time-of-use diary was left with the household for completion. For each building surveyed, the data retrieved from the completed time-of-use diary was used in Microsoft Excel for computing the hourly electricity load profile for the seven days of the week. In order to obtain the hourly energy load (in watts) for each building, the power rating of the appliances used during each of the 24 h of the day was summed and the result in watts was converted to kWh by dividing by 1000. Each dwelling's daily load profile was obtained as an average of the load profile for the seven days of the week. The article as well provides data on the solar photovoltaic systems' components designed to supply electricity to the building and the levelized cost of electricity (LCOE) of the systems for the base case scenario and different sensitivity cases obtained from simulations using HOMER Pro. The load profile data provided in this article can be reused by other researchers in the design of solar photovoltaic systems for residential buildings.

| S-EPMC7186491 | biostudies-literature

Climate change and residential electricity consumption in the Yangtze River Delta, China.

Project description:Estimating the impact of climate change on energy use across the globe is essential for analysis of both mitigation and adaptation policies. Yet existing empirical estimates are concentrated in Western countries, especially the United States. We use daily data on household electricity consumption to estimate how electricity consumption would change in Shanghai in the context of climate change. For colder days <7 °C, a 1 °C increase in daily temperature reduces electricity consumption by 2.8%. On warm days >25 °C, a 1 °C increase in daily temperatures leads to a 14.5% increase in electricity consumption. As income increases, households' weather sensitivity remains the same for hotter days in the summer but increases during the winter. We use this estimated behavior in conjunction with a collection of downscaled global climate models (GCMs) to construct a relationship between future annual global mean surface temperature (GMST) changes and annual residential electricity consumption. We find that annual electricity consumption increases by 9.2% per +1 °C in annual GMST. In comparison, annual peak electricity use increases by as much as 36.1% per +1 °C in annual GMST. Although most accurate for Shanghai, our findings could be most credibly extended to the urban areas in the Yangtze River Delta, covering roughly one-fifth of China's urban population and one-fourth of the gross domestic product.

| S-EPMC6329981 | biostudies-literature

The impact of COVID-19 measures on intraday electricity load curves in the European Union: A panel approach

Project description:This paper examines the impact of the intensity of government measures introduced to reduce the spread of COVID-19 on intraday electricity load curves in 23 European countries. The econometric panel model used covers the entire period from the virus outbreak in Europe up to the release of several vaccines; therefore, the estimation considers the introduction, partial lifting, and reintroduction of the interventions. Based on the results, the impacts of the different stringency measures were similar in the 23 analysed EU member states. More stringent interventions had different effects at different times of day: the morning and evening peaks were significantly affected, as was every hour of the day. The impacts were nonlinear, meaning that different measures mutually amplified each other’s impact and led to more substantial changes in electricity consumption and citizens’ lives. The morning and evening peaks are also found to have decreased, causing a flattening of the load curves. In line with this result, the partial effect of an increase in the stringency index depends on the type of day (weekday or weekend), hour of the day, and initial stringency level. Overall, the lockdown measures led to a decrease in hourly electricity consumption of between 1% and 9% on weekdays and between 1% and 13% on weekends. Total daily consumption decreased by up to 9%. Understanding how hourly electricity demand reacts to different stringency measures provides valuable information in operation scheduling and capacity planning. More accurate demand forecasts can support trading decisions and help prevent extreme market mismatches.

| S-EPMC9484106 | biostudies-literature

Heterogeneous changes in electricity consumption patterns of residential distributed solar consumers due to battery storage adoption.

Project description:This study provides an empirical assessment of how adopting battery storage units can change the electricity consumption patterns of PV consumers using individual-consumer-level hourly smart meter data in Arizona, United States. We find that on average after adding batteries, PV consumers use more solar electricity to power their houses and send less solar electricity back to the grid. In addition, adding battery storage reduces electricity needed from the grid during system peak hours, helping utilities better flatten the load curves. Most importantly, we find a large degree of heterogeneity in the changes in electricity consumption patterns due to adopting battery storage that are not consistent with engineering or economic principles such as those not maximizing consumers' economic benefits. Such heterogeneous changes imply that utilities and policymakers need to further study the underlying behavioral reasons in order to maximize the social benefits of battery storage and PV co-adoption.

| S-EPMC9121249 | biostudies-literature

Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014.

Project description:With the cost of consuming resources increasing (both economically and ecologically), homeowners need to find ways to curb consumption. The Almanac of Minutely Power dataset Version 2 (AMPds2) has been released to help computational sustainability researchers, power and energy engineers, building scientists and technologists, utility companies, and eco-feedback researchers test their models, systems, algorithms, or prototypes on real house data. In the vast majority of cases, real-world datasets lead to more accurate models and algorithms. AMPds2 is the first dataset to capture all three main types of consumption (electricity, water, and natural gas) over a long period of time (2 years) and provide 11 measurement characteristics for electricity. No other such datasets from Canada exist. Each meter has 730 days of captured data. We also include environmental and utility billing data for cost analysis. AMPds2 data has been pre-cleaned to provide for consistent and comparable accuracy results amongst different researchers and machine learning algorithms.

| S-EPMC4896126 | biostudies-literature

Synthetic hourly electricity load data for the paper and food industries.

Project description:The data set includes hourly time series for a period of one year of electricity demand of three different types of industries. The industries include a small food processing company, one small paper industry and one medium-large paper producing company. The data have been synthetized from two years of measured data from industries in Chile using a comprehensive clustering analysis. The synthetic data possess the same statistical characteristics as the measured data but are provided normalized to one kWh and anonymized in order to be used without confidentiality issues. The data were originally used in the associated paper [1] to assess the demand side management potential of the industries but these can be used for further energy system modelling exercises including these types of industries.

| S-EPMC8024589 | biostudies-literature

Electricity consumption data of a student residence in Southern Africa.

Project description:The time-series dataset presented in this article was captured using a real-time energy monitoring device from a distribution panel of a student residence in Johannesburg, South Africa. The data was captured from April 2016 to January 2018. The data from the three conductors supplying the student residence with electricity was automatically aggregated and presented as a single data point. The granularity was at resolution levels of watt-minute and kilowatt-hour. A total of 13,966 hrs of data points was captured. The data has not been processed further. Hence, data consists of 1,209-hour of missing data points. In addition to the energy consumption data, 16 months of hourly data for wind speed, temperature and humidity of the closest weather station has been provided. The data will be useful in the formulation of mathematical models of electricity consumption that is most suitable for a student residence. Furthermore, the data provided in this article will encourage the development of a data-driven electricity consumption management strategy and policy formulation for student residences.

| S-EPMC7452690 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data