Downloaded 33 times
Uploaded bysenicsummerschool
007 160719 building performance simulation and application of climate changes
This document summarizes a presentation on building performance simulation (BPS) and the application of climate change scenarios. BPS is a multi-disciplinary approach that simulates complex building systems. It aims to integrate simulation into the design process by improving tools and interfaces. Weather data is a key input but has limitations due to availability and errors. Climate models are used to generate future weather scenarios based on emission scenarios. These morph historical data and can be generated with tools like CCWorldWeatherGen and Weather-Shift. However, climate models have uncertainty since climate is complex and models estimate trends.
007 160719 building performance simulation and application of climate changes
- 1.
- 2.
- 3.
- 4.
- 5. 2016 SEniCSummer School Towards computer-supported design • Objective is to integrate BPS inside the design process •Need for improvement of current BPS tools and their user interface
- 6. 2016 SEniCSummer School Terminology in simulation • Model the physics of interest in a limited simulation domain •Inputs = model parameters + domain boundary conditions
- 7. Dry-bulb air temperature* Humidity/wet-bulb temperature/dew-point temperature Solar illuminance (direct and diffuse)** Solar irradiance (direct and diffuse)** Sky temperature Cloudcover/sky condition Wind (velocity and direction)*** Ground temperature Ground surface albedo Weather conditions (e.g. rain) *dry-bulb temperature can be reliably modified with relatively simple methods **Solar and illuminance data are normally generated using models *** Wind sensitive design often needs on-site measurements 2016 SEniCSummer School Weather data required for simulation (Reference: Hensen and R. Lamberts, 2011)
- 8. 2016 SEniCSummer School Characteristics of weather data (Reference: Hensen and R. Lamberts, 2011) One minute temperature, wind, and solar radiation observations for 31 August 2008 at National Renewable Energy Laboratory, Golden, Colorado.
- 9. Application Example Requirements Energy design and compliance analysis of fully conditioned buildings Comparing annual energy saving information for design variants Single-year hourly data Performance of un- or semi- conditioned buildings Overheating analysis, passive solar design, comfort studies Typical data are often not adequate Equipment sizing HVAC design Design-day or near extreme conditions in short period Model calibration, building troubleshooting, control optimization, actual savings estimation Performance of existing building Onsite measured data Engineering studies Heating/Cooling degree days Annual hourly data Natural ventilation design Wind-driven ventilation Local reliable data Daylighting studies Energy savings due to lighting controls Annual hourly illuminance data Visual comfort or control dynamics Sub-hourly data Renewable energy systems Solar electric system, wind turbine Sub-hourly data 2016 SEniCSummer School Simulation applications (Reference: Hensen and R. Lamberts, 2011)
- 10. Weather data is counted as a large source of uncertainty in building performance simulation. What are the limits? • Obtaining data: • Data from project site-Costly, Time consuming, not feasible • Historical weather data observation- large-scale, errors, missing data (notably solar radiation) 2016 SEniCSummer School Limits of weather data for BPS? EnergyPlus Weather File (EPW) Data Dictionary
- 11.
- 12. -25 -20 -15 -10 -5 0 5 10 15 20 25 TMY 2020 20502080 Energyneed,kWh/(m2a) Weather Scenario Heating Cooling Future weather scenarios (Reference: moazami et al., 2016) Primary energy breakdown of the post-retrofit building including electricity generated by the PV system. Overall electricity consumption, production and excess for a year and selected months, for the reference year (1961e1990 CWEC) and morphed horizon years (kWh). (Reference: Robert and Kummert, 2012) Yearly energy need for space heating and cooling per unit of net floor area. (Reference: Pagliano et al., 2016)
- 13. • The IntergovernmentalPanel on Climate Change (IPCC) is the international body for assessing the science related to climate change • They have released set of ”emission scenarios” based on different models for future society development that include demographic, economic, social and technological factors. • A1FI e fossil fuel intensive, A1B e balanced, A1T e predominantly non-fossil fuel. 2016 SEniCSummer School The Intergovernmental Panel on Climate Change (IPCC) (Reference: Robert and Kummert, 2012)
- 14.
- 15. • To adjustcurrent weather files to reflect climate change scenarios. A “morphing” technique is proposed by Belcher et al., 2005 and used by Jentsch et al., 2008 in the publicly available tool CCWorldWeatherGen, 2009. Offset Morphing 2016 SEniCSummer School Future weather scenarios
- 16.
- 17.
- 18. • Climate modelsare mathematical representations of the interactions between the atmosphere, oceans, land surface, ice and the sun. This is clearly a very complex task, so models are built to estimate trendsrather than events. Observed sea level rise since 1970 from tide gauge data (red) and satellite measurements (blue) compared to model projections for 1990-2010 from the IPCC Third Assessment Report (grey band). (Source: The Copenhagen Diagnosis, 2009) 2016 SEniCSummer School How reliable are the models?
- 20.
- 21.