Generation of heat and electricity load profiles with high temporal resolution for Urban Energy Units using open geodata

被引:0
|
作者
Blanco, Luis [1 ,2 ]
Zabala, Alejandro [3 ]
Schiricke, Bjoern [1 ]
Hoffschmidt, Bernhard [1 ,2 ]
机构
[1] German Aerosp Ctr DLR, Inst Solar Res, Cologne, Germany
[2] Rhein Westfal TH Aachen, Chair Solar Components, Aachen, Germany
[3] German Aerosp Ctr DLR, Inst Networked Energy Syst, Oldenburg, Germany
关键词
Open-source; GIS; Urban planning; Heat load profile; Electricity load profile; Urban Energy Units; BUILDINGS; MODEL;
D O I
10.1016/j.scs.2024.105967
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Urban areas account for up to 87% of global energy consumption, with around a third of CO2 emissions from the building sector. Germany recently enacted a law targeting carbon neutrality in heating by 2045, requiring all municipalities to submit transformation plans for their heating infrastructure. Many are in early stages and need innovative methods to achieve these goals. This study proposes an automated GIS-based approach to generate heat and electricity load profiles for geographically referenced residential buildings and districts in Germany, using only open data. The methodology offers hourly temporal resolution and spatial detail from individual buildings to Urban Energy Units (UEUs), a concept introduced in prior studies. Nine distinct heating load profiles and nine electricity load profiles were identified. These profiles can adapt to different weather datasets and to three building refurbishment scenarios. The methodology and energy analysis were applied to a district in Oldenburg, Germany, demonstrating the model's flexibility under varying boundary conditions. For this district, the analysis revealed a total heat demand of 9.9 +/- 7 GWh/a and an electricity demand of 2.3 +/- 0.126 GWh/a, with respective errors of 45% and 39% when compared to other local data, this demand is presented in both yearly and hourly resolutions. This methodology intends to support German municipalities by accelerating the initial phases of the municipal heating plans and deliver high-quality data on building heat and electricity demand.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Generation, analysis, and applications of high resolution electricity load profiles in Qatar
    Bayram, Islam Safak
    Saffouri, Faraj
    Koc, Muammer
    JOURNAL OF CLEANER PRODUCTION, 2018, 183 : 527 - 543
  • [2] Towards Sustainable Urban Energy Systems: High Resolution Modelling of Electricity and Heat Demand Profiles
    Wang, Han
    Mancarella, Pierluigi
    2016 IEEE INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON), 2016,
  • [3] Impacts of Raw Data Temporal Resolution Using Selected Clustering Methods on Residential Electricity Load Profiles
    Granell, Ramon
    Axon, Colin J.
    Wallom, David C. H.
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2015, 30 (06) : 3217 - 3224
  • [4] High-Resolution Electricity Load Profiles of Selected Houses in Qatar
    Alrawi, Omar
    Bayram, Islam Safak
    Koc, Muammer
    PROCEEDINGS 2018 IEEE 12TH INTERNATIONAL CONFERENCE ON COMPATIBILITY, POWER ELECTRONICS AND POWER ENGINEERING (CPE-POWERENG 2018), 2018,
  • [5] Household electricity demand profiles - A high-resolution load model to facilitate modelling of energy flexible buildings
    Marszal-Pomianowska, Anna
    Heiselberg, Per
    Larsen, Olena Kalyanova
    ENERGY, 2016, 103 : 487 - 501
  • [6] A High Resolution Spatiotemporal Urban Heat Load Model for GB
    Siddiqui, Salman
    Barrett, Mark
    Macadam, John
    ENERGIES, 2021, 14 (14)
  • [7] The Generation of Electricity Load Profiles Using K-Means Clustering Algorithm
    Uzupyte, Ruta
    Babarskis, Tomas
    Krilavicius, Tomas
    JOURNAL OF UNIVERSAL COMPUTER SCIENCE, 2018, 24 (09) : 1306 - 1329
  • [8] Characterizing probability density distributions for household electricity load profiles from high-resolution electricity use data
    Munkhammar, Joakim
    Ryden, Jesper
    Widen, Joakim
    APPLIED ENERGY, 2014, 135 : 382 - 390
  • [9] High temporal resolution generation expansion planning for the clean energy transition
    Levin, Todd
    Blaisdell-Pijuan, Paris L.
    Kwon, Jonghwan
    Mann, W. Neal
    RENEWABLE AND SUSTAINABLE ENERGY TRANSITION, 2024, 5
  • [10] Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model
    Lombardi, Francesco
    Balderrama, Sergio
    Quoilin, Sylvain
    Colombo, Emanuela
    ENERGY, 2019, 177 : 433 - 444