Modelling the energy demand of buildings

1. Modelling the energy demand of buildings Energy Systems Modelling Energy Demand � buildings 1st February 2010 Phillip Biddulph

2. Themes from Marks Services in buildings, Technological options for new and refurbishment, Individual building modelling, Stock modelling scenarios.

3. sustainable management of indoor pollution. the provision, conservation and use of energy in buildings.� environmental, health and economic implications. principal options for future home energy. integrated decision-support framework. http://www.pureintrawise.org/

4. Air Permeability

5. Future scenarios Buildings will become more air tight to reduce heat loss. ? potentially big increase in indoor pollutant concentrations. Moisture, ? mould, dust mites PM CO2 Heat etc One solution is to use Mechanical Ventilation and Heat Recovery (MVHR) systems.

6. Schematic of an MVHR

7. Sustainability trade-offs between energy efficiency, ventilation and pollution What level of permeability should be recommended by Building Regulations for new (and refurbished?) dwellings? What ventilation systems should be installed by developers in new (and refurbished?) dwellings? What will be the practical optimum balance between improving energy efficiency via infiltration/ventilation heat loss control and the associated health impacts? How will this apply to new and refurbished buildings?

8. Build a model of the stock, and play with the ventilation!

9. Need a sub-model for the Building and Occupation

10. Which details are needed?

11. Phill�s Schedule

12. EP_Generator Software, developed at UCL, to generate details of buildings that can be simulated. Easy to change and scan over all the main drivers, Built Form, Environment, Efficiency package, Occupation/Household.

13. EP_Generator

14. How do you calculate the energy response of all these buildings? Get the best possible input data limited data sets available, difficult to interpret�. Classify into sub sets. Calculate Space heating Thermal Insulation Ventilation, permeability, MVHR Solar, Incidental gains Water heating. Cooking People, Lighting, etc� Need a very detailed building physics simulator.

15. � that also simulates Indoor Pollutants and can calculate individual exposures.

16. Building physics Simulation There are two types of simulator. Fast, simple, Non-dynamic, Works on yearly or monthly weather data, and simple geometery. Misses all the interacting dynamics, Crude ball park answers. Slow, Complex Dynamic, Works on hourly inputs, and detailed building descriptions, Includes all the interacting dynamics, Gives detailed answers, which can be averaged.

17. Heat Transfer � Thermal Mass

20. Would like a SIMPLE model�.however, For good energy response need Thermal Mass. Thermal mass ? dynamic simulation (non-linear). time step ~ hour. Dynamic ? heating system schedule set point. Heating + Incidentals ? iterations. Simple model ? complex. (Time consuming)

21. Simulate, dynamically, practically every energy aspect of a building. Tested, maintained, developed & documented. (>3,000 pages!) Large user Group. It�s Free� UCL have added Moisture Buffering and Pollution tracking. http://apps1.eere.energy.gov/buildings/energyplus/

22. Complicated. Long steep learning curve. Difficult to set up. Takes time to run. (many minutes.) No user interface. http://www.designbuilder.co.uk/content/view/43/64/ Need to think and understand outputs.

23. Can we individually simulate every house in the UK? 25 million houses. 60 million people. Even if all the initial conditions and a crude set of schedules for everyone were available, then it would take a long time to run. the predictions for each house would be highly sensitive to small input data errors. The house environment system is �CHAOTIC�.

24. Chaos Very sensitivity to initial conditions. Completely deterministic. Non-linear dynamics. An individual house is NEVER in exactly the same state twice. It is not possible to accurately predict the �state� of every parameter of a real house. The prediction gets worse the further into the future you go. Known as the �Butterfly Effect�.

30. What a mess ! Periodic, but not exactly.

31. What do we get from modelling then ? As long as we do the dynamics first and then average the results, the averages should help us to determine underlying trends. Therefore the effects of changing, say, the efficiency of building material on the average energy consumption is meaningful.

32. Intrawise at UCL Can�t do every combination, Simplify Categorise the houses Use a small set of weather and external pollution files Use only a few efficiency packages Pick a few Households/occupations. Scan over different variables tighter ventilation improved thermal insulation changes in heating type Future weather Improvements in household efficiencies Look at effects on Exposures Energy implications

33. Built forms Flat Detached Terrace Environment London Heathrow Occupancy Phillip Biddulph (Living alone) Efficiency Bad ? Drafty Walls, Single Pane Windows etc Standard ? Typical modern standard Best ? Fully insulated, Triple glazed Windows. Example: Simulations 9 Combinations

34. Flat temperature profiles

35. Total Power requirements

36. Kitchen Temperatures

37. Kitchen Temperatures

38. Flat Temperatures in different zones

39. Phillip Average Temperatures

40. Phillip Exposure to PM2.5

42. Phillip Average Exposure to PM2.5

43. Where are the Buildings ? Ordnance Survey Topography Layer and Cities Revealed GIS files for London Identify the most common types of dwellings in London 47 Buildings entered into database so far.

44. Build up the stock�

45. �.for example

46. How many People ?

47. Mix together to get households

48. Different Scenario's  Present, Houses, Efficiencies, etc, Weather, Pollution concentrations. Future (2050) Depends on scenario. Houses, Weather, Pollution concentration.

49. To help you understand the measurements and observations of a real system. understand data you�ve already got! To help you predict the state of the system at some future (or past) point. provide data when you don�t have any! Why Model?

50. Systems may be Chaotic. Don�t look for exact absolute predictions. Look for trends. Example, Weather vs Climate Precise prediction becomes less valid as you go further from the starting point. Weather ? One week at maximum. The more complex the system is, the more initial measurements are required and the less precise the prediction is. Weather ? Hopeless before satellite data and bigger computers. Making a simple model out of a complex system, does not make the system simple. Red Sky at night �� Beware!