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

被引:0
|
作者
Mehrnaz Anvari
Elisavet Proedrou
Benjamin Schäfer
Christian Beck
Holger Kantz
Marc Timme
机构
[1] Potsdam Institute for Climate Impact Research (PIK),School of Mathematical Sciences
[2] Member of the Leibniz Association,Faculty of Science and Technology
[3] DLR Institute for Networked Energy Systems,undefined
[4] Queen Mary University of London,undefined
[5] Norwegian University of Life Sciences,undefined
[6] Institute for Automation and Applied Informatics,undefined
[7] Karlsruhe Institute for Technology,undefined
[8] The Alan Turing Institute,undefined
[9] Max Planck Institute for the Physics of Complex Systems,undefined
[10] Chair for Network Dynamics,undefined
[11] Center for Advancing Electronics Dresden (cfaed) and Institute for Theoretical Physics,undefined
[12] Technical University of Dresden,undefined
[13] Lakeside Labs,undefined
来源
Nature Communications | / 13卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
相关论文
共 50 条
  • [1] Data-driven load profiles and the dynamics of residential electricity consumption
    Anvari, Mehrnaz
    Proedrou, Elisavet
    Schaefer, Benjamin
    Beck, Christian
    Kantz, Holger
    Timme, Marc
    NATURE COMMUNICATIONS, 2022, 13 (01)
  • [2] Effect of Climate on Residential Electricity Consumption: A Data-Driven Approach
    Xia, Cuihui
    Yao, Tandong
    Wang, Weicai
    Hu, Wentao
    ENERGIES, 2022, 15 (09)
  • [3] A novel data-driven approach for residential electricity consumption prediction based on ensemble learning
    Chen, Kunlong
    Jiang, Jiuchun
    Zheng, Fangdan
    Chen, Kunjin
    ENERGY, 2018, 150 : 49 - 60
  • [4] Assessing the Flexibility Potential of the Residential Load in Smart Electricity Grids - A Data-Driven Approach
    Azari, Delaram
    Torbaghan, Shahab Shariat
    Cappon, Hans
    Gibescu, Madeleine
    Keesman, Karel
    Rijnaarts, Huub
    2017 14TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET (EEM 17), 2017,
  • [5] Data-Driven Load Modeling and Forecasting of Residential Appliances
    Ji, Yuting
    Buechler, Elizabeth
    Rajagopal, Ram
    IEEE TRANSACTIONS ON SMART GRID, 2020, 11 (03) : 2652 - 2661
  • [6] Data-Driven Load Modeling and Forecasting of Residential Appliances
    Ji, Yuting
    Buechler, Elizabeth
    Rajagopal, Ram
    2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), 2021,
  • [7] Data-driven structural modeling of electricity price dynamics
    Mahler, Valentin
    Girard, Robin
    Kariniotakis, Georges
    ENERGY ECONOMICS, 2022, 107
  • [8] A Data-Driven Approach to Predict Hourly Load Profiles From Time-of-Use Electricity Bills
    Lazzeroni, Paolo
    Lorenti, Gianmarco
    Repetto, Maurizio
    IEEE ACCESS, 2023, 11 : 60501 - 60515
  • [9] Data-driven Method for Providing Feedback to Households on Electricity Consumption
    Mononen, Matti
    Saarenpaa, Jukka
    Johansson, Markus
    Niska, Harri
    2014 IEEE NINTH INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS AND INFORMATION PROCESSING (IEEE ISSNIP 2014), 2014,
  • [10] Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach
    Cho, Sooyoun
    Lee, Jeehang
    Baek, Jumi
    Kim, Gi-Seok
    Leigh, Seung-Bok
    ENERGIES, 2019, 12 (21)