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

Petri Hietaharju, Jari Pulkkinen, Mika Ruusunen, Jean Nicolas Louis (Corresponding Author)

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)

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.

Original languageEnglish
Article number116962
Number of pages16
JournalApplied Energy
Volume295
DOIs
Publication statusPublished - 1 Aug 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • 2050 strategy
  • District heating
  • Energy efficiency
  • Morphing technique
  • Renovation strategy
  • Stock driven model

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