Towards agent-based building stock modeling: Bottom-up modeling of long-term stock dynamics affecting the energy and climate impact of building stocks

Artikel i vetenskaplig tidskrift, 2020

Buildings are responsible for a large share of the energy demand and greenhouse gas (GHG) emissions in Europe and Switzerland. Bottom-up building stock models (BSMs) can be used to assess policy measures and strategies based on a quantitative assessment of energy demand and GHG emissions in the building stock over time. Recent developments in BSM-related research have focused on modeling the status quo of the stock and comparatively little focus has been given to improving the modeling methods in terms of stock dynamics. This paper presents a BSM based on an agent-based modeling approach (ABBSM) that models stock development in terms of new construction, retrofit and replacement by modeling individual decisions on the building level. The model was implemented for the residential building stock of Switzerland and results show that it can effectively reproduce the past development of the stock from 2000 to 2017 based on the changes in policy, energy prices, and costs. ABBSM improves on current modeling practice by accounting for heterogeneity in the building stock and its effect on uptake of retrofit and renewable heating systems and by incorporating both regulatory or financial policy measures as well as other driving and restricting factors (costs, energy prices).