Simstock: a free and open-source tool for urban-scale building energy modelling

Researchers at University College London have released SimStock, a freely available and open-source tool for urban-scale building energy modelling. It is available both as a Python library and a QGIS plugin.

SimStock is a modelling platform which automatically generates dynamic building energy simulation models ready to be executed by EnergyPlus, an open-source whole-building energy modelling (BEM) engine.

The generated models can be simulated locally or with high performance computing, making use of all available computing power by running simulations in parallel. Simulation outputs are collected and post-processed automatically which prepares them for various analysis to be applied, such as sensitivity analysis, regressions, uncertainty quantification etc.

SimStock allows the automatic creation of dynamic thermal simulation models of all buildings within an area of analysis; allowing a wide range of scenario analyses to be performed. These include:

• Analysis of the efficacy of various retrofit measures applied to the building stock, such as improved insulation, replacing an artificial lighting with more efficient LED lighting, glazing replacement, improved heating, ventilating and air-conditioning systems’ control strategies.
• Testing the potential for integration of renewable technologies in the building stock. Roof area availability for installation of Photovoltaic (PV) systems and/or solar-thermal systems and the performance of these systems can be evaluated at both the stock level and individual building level. Similarly, the potential for integration of ground source heat pumps can be assessed.
• Investigating the feasibility of integration of thermal and/or electrical storage systems for the purpose of demand-side management in various-sized areas.
• Assessing daylight availability and a quality of daylight by taking into account the surrounding context.
• Estimating the overheating risk of the stock as a whole, a stock segment (e.g. schools) or individual buildings under both current climate conditions and predictions of future climate conditions.
• Mapping buildings, mainly in dense urban areas, which are under the risk of decreased Indoor Air Quality (IAQ) due to various reasons such as reduced natural ventilation due to Urban Heat Island (UHI) effect, increased particulate pollution either due to poor ventilation or external air pollution and/or exposure to nitrogen oxides (NOx) from traffic due to closeness to the major roads.

Download SimStock

SimStock is available both as a Python library and a QGIS plugin.

• QGIS PluginOpens in a new tab
• Python libraryOpens in a new tab
• See the documentationOpens in a new tab for both versions

SimStock publications:

• Ruyssevelt, P.A. 2019. Modelling London’s building stock and its associated energy use, pdfOpens in a new tab. Presented at the 2019 ASHRAE Winter Conference, Atlanta, GA, USA.
• Amrith, S., Oraiopoulos, A., Korolija, I. and Fennell, P. 2022. Developing an Open access plugin for urban building energy modelling in QGISOpens in a new tab. In: BSO-VI 2022 Sixth Building Simulation and Optimisation Virtual Conference. IBPSA.
• Coffey, B., Stone, A., Ruyssevelt, P. and Haves, P. 2015. An epidemiological approach to simulation-based analysis of large building stocks. In: Proceedings of BS2015 (p. 8). Hyderabad, India.

Research which has made use of SimStock:

• Matthew, C. and Spataru, C. 2023. Time-use data modelling of domestic, commercial and industrial electricity demand for the Scottish Islands. Energies, 16(13): 5057. doi: 10.3390/en16135057Opens in a new tab
• Oraiopoulos, A., Fennell, P., Amrith, S., Wieser, M., Korolija, I. and Ruyssevelt, P. 2022. Towards a universal access to Urban Building Energy Modelling – The case of low-income, self-constructed houses in informal settlements in Lima, PeruOpens in a new tab. In: BSO-VI 2022 Sixth Building Simulation and Optimisation Virtual Conference. IBPSA.
• Fennell, P., Korolija, I. and Ruyssevelt, P. 2021. A comparison of performance of three variance-based sensitivity analysis methods on an urban-scale building energy modelOpens in a new tab. In: (Proceedings) Building Simulation 2021. IBPSA.
• Claude, S. et al. 2019. Evaluating retrofit options in a historical city center: Relevance of bio-based insulation and the need to consider complex urban form in decision-making. Energy and Buildings, 182: 196–204. doi: 10.1016/j.enbuild.2018.10.026Opens in a new tab
• Grassie, D., Korolija, I., Mumovic, D., and Ruyssevelt, P.A. 2018. Feedback and feedforward mechanisms for generating occupant datasets for UK school stock simulation modellingOpens in a new tab. In: Proceedings of Building Simulation and Optimization 2018 (p. 8). Cambridge, UK: International Building Simulation Association, England.

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