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A stochastic dynamic building stock model for determining long-term district heating demand under future climate change

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  • Hietaharju, Petri
  • Pulkkinen, Jari
  • Ruusunen, Mika
  • Louis, Jean-Nicolas

Abstract

District heating networks will face major changes on the demand side resulting from future demographic change, building energy efficiency improvements and climate change in cities. A stochastic dynamic building stock model was developed to investigate the impact of climate change and renovation strategies on district heat demand. The model was applied to a representative city in Finland comprising 3880 real buildings with hourly-resolution data, for which heat demand scenarios for buildings were simulated up to 2050 using results from global and regional climate change models. The novel stochastic dynamic building stock model utilises the real building stock as a basis and considers demolition, construction of new buildings and renovation of existing buildings. It is used in the precised dynamic heat demand model (mean MAPE 7.7%) to calculate the future heat demand. Model outputs indicated that early adoption of building renovation will decrease long-term energy consumption by 3% for every 0.5% increase in the renovation rate by 2050. Increasing the yearly renovation rate from the current 1% to 3% could reduce the district heat demand by 22% (range 19–28%). Early adoption of building renovation could reduce the relative peak load by 50% compared with late adoption. Climate change will reduce the overall heat demand for district heating but will increase the annual relative daily variation from 3.6% to 4.5%, meaning that the peaks in heat demand will be more visible.

Suggested Citation

  • Hietaharju, Petri & Pulkkinen, Jari & Ruusunen, Mika & Louis, Jean-Nicolas, 2021. "A stochastic dynamic building stock model for determining long-term district heating demand under future climate change," Applied Energy, Elsevier, vol. 295(C).
    • Handle: RePEc:eee:appene:v:295:y:2021:i:c:s0306261921004384
    DOI: 10.1016/j.apenergy.2021.116962
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    References listed on IDEAS

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    1. Kaandorp, Chelsea & Miedema, Tes & Verhagen, Jeroen & van de Giesen, Nick & Abraham, Edo, 2022. "Reducing committed emissions of heating towards 2050: Analysis of scenarios for the insulation of buildings and the decarbonisation of electricity generation," Applied Energy, Elsevier, vol. 325(C).
    2. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
    3. Yuan, Jianjuan & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei & Zhou, Zhihua, 2022. "Evaluation of the operation data for improving the prediction accuracy of heating parameters in heating substation," Energy, Elsevier, vol. 238(PB).
    4. Pablo Benalcazar & Jacek Kamiński & Karol Stós, 2022. "An Integrated Approach to Long-Term Fuel Supply Planning in Combined Heat and Power Systems," Energies, MDPI, vol. 15(22), pages 1-22, November.
    5. Ming Hu & Siavash Ghorbany, 2024. "Building Stock Models for Embodied Carbon Emissions—A Review of a Nascent Field," Sustainability, MDPI, vol. 16(5), pages 1-18, March.
    6. Yang, Xining & Hu, Mingming & Tukker, Arnold & Zhang, Chunbo & Huo, Tengfei & Steubing, Bernhard, 2022. "A bottom-up dynamic building stock model for residential energy transition: A case study for the Netherlands," Applied Energy, Elsevier, vol. 306(PA).

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