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Presentation_1376168115602

  1. 1. introduction simulation result analysis conclusions and future work conlcusions EMPLOYING LOCAL AND GLOBAL SENSITIVITY ANALYSIS TECHNIQUES TO GUIDE USER INTERFACE DEVELOPMENT OF ENERGY CERTIFICATION AND COMPLIANCE SOFTWARE TOOLS Filippo Monari filippo.monari@strath.ac.uk Department of Mechanical and Aerospace Engineering University of Strathclyde September 2012 A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  2. 2. introduction simulation result analysis conclusions and future work conlcusions Abstract This work reports on how sensitivity analysis techniques, applied to the inputs of calculation engines for energy certification and regulation compliance purposes, can provide guidance for simplifying their user interfaces and simplify model imput. A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  3. 3. introduction simulation result analysis conclusions and future work conlcusions SBEM The focus of the research is SBEM (Simplified Building Energy Model) which is the standard software used in the UK for energy certification and regulation compliance of non-domestic buildings. It was developed by BRE (Building Research Establishment), based on the BS EN ISO 13790 Standard. A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  4. 4. introduction simulation result analysis conclusions and future work conlcusions analysed cases Two building models from the iSBEM’s installation package have been considered: Approval Case 1 (case 1) Example Building Complete (case 2) A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  5. 5. introduction simulation result analysis conclusions and future work conlcusions analysed cases Two building models from the iSBEM’s installation package have been considered: Approval Case 1 (case 1) Example Building Complete (case 2) A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  6. 6. introduction simulation result analysis conclusions and future work conlcusions analysed cases Two building models from the iSBEM’s installation package have been considered: Approval Case 1 (case 1) Example Building Complete (case 2) A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  7. 7. introduction simulation result analysis conclusions and future work conlcusions analysed cases Two building models from the iSBEM’s installation package have been considered: Approval Case 1 (case 1) Example Building Complete (case 2) A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  8. 8. introduction simulation result analysis conclusions and future work conlcusions analysed cases case2 it is developed on two levels: ground floor: supermarket and coffee shops first floor: offices it is composed of 19 thermal zones total area 2900 square metres it is provided with a solar energy system it is served by an HWS and HVAC (heating, cooling and heat recovery) A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  9. 9. introduction simulation result analysis conclusions and future work conlcusions employed methods Two different sensitivity techniques were applied to the input data required: the Morris Method which is used to screen the input factors the Monte Carlo Analysis which is used to assess the effects of groups of parameters A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  10. 10. introduction simulation result analysis conclusions and future work conlcusions employed methods Two different sensitivity techniques were applied to the input data required: the Morris Method which is used to screen the input factors the Monte Carlo Analysis which is used to assess the effects of groups of parameters A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  11. 11. introduction simulation result analysis conclusions and future work conlcusions employed methods Two different sensitivity techniques were applied to the input data required: the Morris Method which is used to screen the input factors the Monte Carlo Analysis which is used to assess the effects of groups of parameters A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  12. 12. introduction simulation result analysis conclusions and future work conlcusions Morris Method elementary effects The Morris Method characterizes the sensitivity of a model respect to its inputs through the concept of elementary effects (EE) Definition the elementary effects (EE) can be defined as approximations of the partial derivatives of the model EEi = y(¯x + ei ∗ ∆i) − y(¯x) ∆i where: ei : is a zero vector wherein only the position i is in equal to 1 y: is the fucntion representing the model to assess x: a vector of variables A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  13. 13. introduction simulation result analysis conclusions and future work conlcusions Morris Method calculating elementary effects the EE are estimated along traictories of points, randomly selected on an adequately discretized space but each one differing from the preious just in one coordinate. Definition the discretized space is represented by p-level k-dimensional grid, where: k: number of input variables of the model p: assumed number of steps defining the values of the variables A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  14. 14. introduction simulation result analysis conclusions and future work conlcusions Morris Method calculating elementary effects For each parameter, a finite distribution (Fi) of r EE (r within [10, 50]) is estimated Then for each Fi are calculated: absolute mean: µ∗ i = 1 r r t=1 |EEit |, indicator of the magnitude of the effect standard deviation: σi = 1 r−1 ∗ r t=1(EEit − µi)2, indicator of the typology of the effect A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  15. 15. introduction simulation result analysis conclusions and future work conlcusions Morris Method calculating elementary effects For each parameter, a finite distribution (Fi) of r EE (r within [10, 50]) is estimated Then for each Fi are calculated: absolute mean: µ∗ i = 1 r r t=1 |EEit |, indicator of the magnitude of the effect standard deviation: σi = 1 r−1 ∗ r t=1(EEit − µi)2, indicator of the typology of the effect A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  16. 16. introduction simulation result analysis conclusions and future work conlcusions Morris Method calculating elementary effects For each parameter, a finite distribution (Fi) of r EE (r within [10, 50]) is estimated Then for each Fi are calculated: absolute mean: µ∗ i = 1 r r t=1 |EEit |, indicator of the magnitude of the effect standard deviation: σi = 1 r−1 ∗ r t=1(EEit − µi)2, indicator of the typology of the effect A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  17. 17. introduction simulation result analysis conclusions and future work conlcusions Morris Method effect typology σi µi ≤ 0.1 ⇒ xi has an almost linear effect 0.1 ≤ σi µi ≤ 0.5 ⇒ xi has a monotonic effect 0.5 ≤ σi µi ≤ 1 ⇒ xi has a quasi-monotonic effect σi µi ≥ 1 ⇒ xi has a non-linear non-monotocnic effect A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  18. 18. introduction simulation result analysis conclusions and future work conlcusions Morris Method effect typology σi µi ≤ 0.1 ⇒ xi has an almost linear effect 0.1 ≤ σi µi ≤ 0.5 ⇒ xi has a monotonic effect 0.5 ≤ σi µi ≤ 1 ⇒ xi has a quasi-monotonic effect σi µi ≥ 1 ⇒ xi has a non-linear non-monotocnic effect A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  19. 19. introduction simulation result analysis conclusions and future work conlcusions Morris Method effect typology σi µi ≤ 0.1 ⇒ xi has an almost linear effect 0.1 ≤ σi µi ≤ 0.5 ⇒ xi has a monotonic effect 0.5 ≤ σi µi ≤ 1 ⇒ xi has a quasi-monotonic effect σi µi ≥ 1 ⇒ xi has a non-linear non-monotocnic effect A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  20. 20. introduction simulation result analysis conclusions and future work conlcusions Morris Method effect typology σi µi ≤ 0.1 ⇒ xi has an almost linear effect 0.1 ≤ σi µi ≤ 0.5 ⇒ xi has a monotonic effect 0.5 ≤ σi µi ≤ 1 ⇒ xi has a quasi-monotonic effect σi µi ≥ 1 ⇒ xi has a non-linear non-monotocnic effect A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  21. 21. introduction simulation result analysis conclusions and future work conlcusions uncertainty analysis macro-parameters the parameters for both the cases have been collected and grouped in order to create comparable macro-parameters; then for each one of them it has been attributed: a probability distribution and suitable uncertainty factors (standard deviation (σ) or Delta (∆) depending on the distribution) based on a literature review A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  22. 22. introduction simulation result analysis conclusions and future work conlcusions uncertainty analysis macro-parameters the parameters for both the cases have been collected and grouped in order to create comparable macro-parameters; then for each one of them it has been attributed: a probability distribution and suitable uncertainty factors (standard deviation (σ) or Delta (∆) depending on the distribution) based on a literature review A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  23. 23. introduction simulation result analysis conclusions and future work conlcusions uncertainty analysis macro-parameters the parameters for both the cases have been collected and grouped in order to create comparable macro-parameters; then for each one of them it has been attributed: a probability distribution and suitable uncertainty factors (standard deviation (σ) or Delta (∆) depending on the distribution) based on a literature review A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  24. 24. introduction simulation result analysis conclusions and future work conlcusions uncertainty analysis distributions and uncertainties macro-parameter id distribution uncertainty set 0, 1, 2 class factor(%) ext wall U 1 normal σ 15, 15, 15 MIP inf 50 Pa 20 normal σ 30, 30, 30 lighting Wattage 22 uniform ±∆ 10, 10, 10 zone area 14 log-normal σ 2, 2, 2 ext wall area 38 log-normal σ 2, 2, 2 hot water generator sesonal efficiency 7 uniform ±∆ 3, 3, 3 effective thermal mass 2 normal σ 7, 7, 7 HVAC cooling sesonal efficiecny 11 uniform ±∆ 3, 3, 3 SFP air distribution system 13 uniform ±∆ 3, 3, 3 SFP zone thermal units 19 uniform ±∆ 3, 3, 3 FIXED HVAC heating sesonal efficiency 12 uniform ±∆ 3, 3, 3 LIP heat recovery sesonal efficiency 10 uniform ±∆ 3, 3, 3 lighting control parasitic power 23 uniform ±∆ 3, 3, 3 Air flow rate MEV 16 uniform ±∆ 3, 3, 3 A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  25. 25. introduction simulation result analysis conclusions and future work conlcusions uncertainty analysis distributions and uncertainties macro-parameter id distribution uncertainty set 0, 1, 2 class factor(%) SFP MEV 17 uniform σ 3, 3, 3 FIXED window frame factors 40 log-normal σ 2, 2, 2 LIP window aspect ratios 41 log-normal σ 4, 4, 4 window areas 39 log-normal σ 2, 10, 20 APPROX thermal bridges Psi − values 24-36 uniform ±∆ 10, 15, 20 LIP glazing U 4 normal σ 5, 10, 15 glazing solar transmission 5 uniform ±∆ 5, 10, 15 glazing light transmission 6 uniform ±∆ 5, 10, 15 SES storage volumes 9 log-normal σ 3, 15, 30 SES panels areas 8 log-normal σ 2, 10, 20 ext wall length 37 normal σ 1, 5, 10 ext door areas 42 log-normal σ 2, 10, 20 int wall U 3 normal σ 15, 20, 25 length hot water pipework in zones 21 normal σ 1, 5, 10 zone height 15 normal σ 1, 5, 10 int wall length 43 normal σ 1, 5, 10 int wall areas 44 log-normal σ 2, 10, 20 A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  26. 26. introduction simulation result analysis conclusions and future work conlcusions simulation process work flow step 1 the Morris Method has been run according to the defined uncertainties A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  27. 27. introduction simulation result analysis conclusions and future work conlcusions simulation process work flow step 2 for each output the variables have been classified in most important (MIP) and least important (LIP) parameters A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  28. 28. introduction simulation result analysis conclusions and future work conlcusions simulation process work flow step 3 LIP have been divided in: FIXED LIP: coefficients mainly relative to the building services, for which the uncertainties are low and suitable approximated values could be easily found through technical specification or literature. APPROX LIP: physical properties and dimensions of secondary importance for the models, which could be defined within certain ranges A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  29. 29. introduction simulation result analysis conclusions and future work conlcusions simulation process work flow step 3 LIP have been divided in: FIXED LIP: coefficients mainly relative to the building services, for which the uncertainties are low and suitable approximated values could be easily found through technical specification or literature. APPROX LIP: physical properties and dimensions of secondary importance for the models, which could be defined within certain ranges A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  30. 30. introduction simulation result analysis conclusions and future work conlcusions simulation process work flow step 3 LIP have been divided in: FIXED LIP: coefficients mainly relative to the building services, for which the uncertainties are low and suitable approximated values could be easily found through technical specification or literature. APPROX LIP: physical properties and dimensions of secondary importance for the models, which could be defined within certain ranges A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  31. 31. introduction simulation result analysis conclusions and future work conlcusions simulation process work flow step 4 the possibility of use approximated values has been investigated by running Monte Carlo simulations for increasing APPOX LIP’s uncertainties A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  32. 32. introduction simulation result analysis conclusions and future work conlcusions Morris method Morris Method - energy demand The total energy demand showed linear and monotonic effects for most of the MIP and LIP with the majority of them having a monotonic influence. Non-linear effects are caused by glass transmittances, internal wall areas, zone areas (ids: 4, 3 and 14). A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  33. 33. introduction simulation result analysis conclusions and future work conlcusions Morris method Morris Method - energy consumption All the MIP variables have linear and monotonic effect. Only the U of the external envelope has a non-linear influence. Considering the LIP irregular influences are shown by effective thermal mass, inifltration at 50 Pa, heat recovery efficiency, glazing U, envelop area, int wall areas and U (ids: 2, 20, 4, 38, 44, 3). A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  34. 34. introduction simulation result analysis conclusions and future work conlcusions Morris method Morris Method - asset rating The number of non-linearities and non-monotonic effects increases for the building asset rating. All the parameters have at least a non-monotonic effect. A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  35. 35. introduction simulation result analysis conclusions and future work conlcusions Monte Carlo Monte Carlo - increased uncertainties output index set 0 set 1 set 2 energy demand s(MJ/m2 ) 3.758 3.856 4.737 s/¯x 0.016 0.016 0.020 energy consumption s(MJ/m2 ) 4.678 4.59 4.798 s/¯x 0.013 0.013 0.013 asset rating s(MJ/m2 ) 0.581 0.594 0.629 s/¯x 0.016 0.016 0.017 The incremented uncertainties for the APPROX-LIP, do not lead to any relevant growth of the global uncertainties. Comparing the different values of standard deviation, increments are always less than or equal to the 1.5% of the mean A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  36. 36. introduction simulation result analysis conclusions and future work conlcusions final results quantifying the error increments the previous result show that it should be possible to replace the "most exact" set of input data (i.e. in these example SET-0), with an "approximated" one (i.e. in these examples SET-1 and SET-2), without sensibly affecting the result of the calculation A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  37. 37. introduction simulation result analysis conclusions and future work conlcusions final results quantifying the error increments The possible increment in the percentage errors produced could be calculated as follow: increased error IEi,n = 2(σ%i,n − σ%i,0) where: σ%i,0: standard deviation as % of the mean, relative to the probability distribution of the i − th SBEM’s output produced by the "most exact" set of data available. It represents the unavoidable amount of uncertainty σ%i,n: standard deviation as % of the mean, relative to the probability distribution of the i − th SBEM’s output produced by the "approximated" set of data. It represents the sum of the unavoidable amount and the increment in the uncertainty A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  38. 38. introduction simulation result analysis conclusions and future work conlcusions final results quantifying the error increments error increments for case 1 and case 2 case output set 1 set 2 case 1 energy demand 0.02 0.05 energy consumption 001 0.02 asset rating 0.01 0.03 case 2 energy demand 0.00 0.01 energy consumption 0.00 0.00 asset rating 0.00 0.01 A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  39. 39. introduction simulation result analysis conclusions and future work conlcusions main findings [1] At a general level the calculation method showed an almost linear character. In particular, the most influencing factors have linear and monotonic influences on SBEM’s outputs. A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  40. 40. introduction simulation result analysis conclusions and future work conlcusions main findings [2] The opportunity to approximate the two main models as meta-models depending only upon the MIP has been demonstrated, as well as the possibility of considering the least important ones in a simplified way. LIP have been divided, depending on the kind of possible approximations: FIXED-LIP: parameters that can be fixed to reasonable values APPROX-LIP: parameters which can be defined within rabges A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  41. 41. introduction simulation result analysis conclusions and future work conlcusions main findings [3] A criterion to quantify the error incremnt caused by the possible approximation has been proposed: IEi,n = 2(σ%i,n − σ%i,0) where: σ%i,0: standard deviation as % of the mean, relative to the probability distribution of the i − th SBEM’s output produced by the "most exact" set of data available. It represents the unavoidable amount of uncertainty σ%i,n: standard deviation as % of the mean, relative to the probability distribution of the i − th SBEM’s output produced by the "approximated" set of data. It represents the sum of the unavoidable amount and the increment in the uncertainty A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  42. 42. introduction simulation result analysis conclusions and future work conlcusions applications The method described is flexible and not software dependent. It can help in: guiding the design of user interfaces developing guide lines for all the data input and collection processes structuring the assessors’ training, so that the focus would be proportionally distributed depending on the influence and importance of each input parameter A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  43. 43. introduction simulation result analysis conclusions and future work conlcusions future developments [1] the design and definition of procedures and tools involved in the analysis of a multitude of buildings should be based on relevant statistically results. Thus the methodology in this paper should be applied to a statistically relevant sample of buildings to confirm the results presented. A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  44. 44. introduction simulation result analysis conclusions and future work conlcusions future developments [2] there is a significant gap between predicted and real data. In future developments a similar approach could be adopted in calibration studies employing metered data in order to see how and to what extent different parameters contribute to the mismatch between predictions and reality. A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde
  45. 45. introduction simulation result analysis conclusions and future work conlcusions thank you for your interest and attention A Sensitivity Analysis on the SBEM’s inputs University of Strathclyde

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