SimStock
A modelling platform generating dynamic building energy simulation models.
SimStock is a modelling platform which combines data from multiple sources to automatically generate dynamic building energy simulation models ready to be executed by EnergyPlus, an open-source whole-building energy modelling (BEM) engine.
High Performance Computing (HPC) or cloud computing is used to allow a large number of models to be simulated 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. (Optional: something on storage/demand side management at stock level or communal level; extension of renewable systems integration
- Investigating a feasibility of integration of thermal and/or electrical storage systems for the purpose of a demand side management in various sized areas)
- Evaluating storage capacity and demand side management potential at stock or community level
- Assessing a daylight availability and a quality of daylight by taking into account the surrounding context
- Estimating an overheating risk of the stock as whole, a stock segment (e.g. schools) or individual buildings under both current climate conditions and predictions of future climate condition
- 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, exposure to nitrogen oxides (NOx) from traffic due to closeness to the major roads.
Model summary
| Type | Automatic generation and processing of bottom up energy models |
|---|---|
| Purpose | To permit high resolution simulation of energy consumption of large numbers of buildings. |
| Spatial scale | User determined, principal limitation of scale is available processing power |
| Temporal scale | User determined, sub-hourly to annual |
| Main contact | Ivan Korolija |
| Other contacts | Paul Ruyssevelt |
- Claude, S., Ginestet, S., Bonhomme, M., Escadeillas, G., Taylor, J., Marincioni, V., … Altamirano, H. (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. https://doi.org/10.1016/j.enbuild.2018.10.026
- Coffey, B., Stone, A., Ruyssevelt, P., & Haves, P. (2015). AN EPIDEMIOLOGICAL APPROACH TO SIMULATION-BASED ANALYSIS OF LARGE BUILDING STOCKS. In Proceedings of BS2015 (p. 8). Hyderabad, India.
- Grassie, D., Korolija, I., Mumovic, D., & Ruyssevelt, P. A. (2018). Feedback and feedforward mechanisms for generating occupant datasets for UK school stock simulation modelling. In Proceedings of Building Simulation and Optimization 2018 (p. 8). Cambridge, UK: International Building Simulation Association, England.
- Ruyssevelt, P. A. (2019, January). Modelling London’s Building Stock and Its Associated Energy Use. Presented at the 2019 ASHRAE Winter Conference, Atlanta, GA, USA. Retrieved from http://web.eecs.utk.edu/~new/presentations/2019_ASHRAE_MBEM9_UK.pdf