@article{Menberg2016Sensitivityanalysis,
  title = {Sensitivity Analysis Methods for Building Energy Models: {{Comparingcomputational}} Costs and Extractable Information},
  author = {Menberg, Kathrin and Heo, Yeonsook and Choudhary, Ruchi},
  year = {2016},
  month = oct,
  journal = {Energy and Buildings},
  volume = {133},
  pages = {433--445},
  doi = {10.1016/j.enbuild.2016.10.005},
  url = {http://dx.doi.org/10.1016/j.enbuild.2016.10.005},
  abstract = {Though sensitivity analysis has been widely applied in the context of building energy models (BEMs),there are few studies that investigate the performance of different sensitivity analysis methods in rela-tion to dynamic, high-order, non-linear behaviour and the level of uncertainty in building energy models.We scrutinise three distinctive sensitivity analysis methods: (a) the computationally efficient Morrismethod for parameter screening, (b) linear regression analysis (medium computational costs) and (c)Sobol method (high computational costs). It is revealed that the results from Morris method taking thecommonly used measure for parameter influence can be unstable, while using the median value yieldsrobust results for evaluations with small sample sizes. For the dominant parameters the results from allthree sensitivity analysis methods are in very good agreement. Regarding the evaluation of parameterranking or the differentiation of influential and negligible parameters, the computationally costly quan-titative methods provide the same information for the model in this study as the computational efficientMorris method using the median value. Exploring different methods to investigate higher-order effectsand parameter interactions, reveals that correlation of elementary effects and parameter values in Morrismethod can also provide basic information about parameter interactions.},
  project = {b-bem}
}

{"_id":"RHf2ycELuZgMd2Sfg","bibbaseid":"menberg-heo-choudhary-sensitivityanalysismethodsforbuildingenergymodelscomparingcomputationalcostsandextractableinformation-2016","author_short":["Menberg, K.","Heo, Y.","Choudhary, R."],"bibdata":{"bibtype":"article","type":"article","title":"Sensitivity Analysis Methods for Building Energy Models: Comparingcomputational Costs and Extractable Information","author":[{"propositions":[],"lastnames":["Menberg"],"firstnames":["Kathrin"],"suffixes":[]},{"propositions":[],"lastnames":["Heo"],"firstnames":["Yeonsook"],"suffixes":[]},{"propositions":[],"lastnames":["Choudhary"],"firstnames":["Ruchi"],"suffixes":[]}],"year":"2016","month":"October","journal":"Energy and Buildings","volume":"133","pages":"433–445","doi":"10.1016/j.enbuild.2016.10.005","url":"http://dx.doi.org/10.1016/j.enbuild.2016.10.005","abstract":"Though sensitivity analysis has been widely applied in the context of building energy models (BEMs),there are few studies that investigate the performance of different sensitivity analysis methods in rela-tion to dynamic, high-order, non-linear behaviour and the level of uncertainty in building energy models.We scrutinise three distinctive sensitivity analysis methods: (a) the computationally efficient Morrismethod for parameter screening, (b) linear regression analysis (medium computational costs) and (c)Sobol method (high computational costs). It is revealed that the results from Morris method taking thecommonly used measure for parameter influence can be unstable, while using the median value yieldsrobust results for evaluations with small sample sizes. For the dominant parameters the results from allthree sensitivity analysis methods are in very good agreement. Regarding the evaluation of parameterranking or the differentiation of influential and negligible parameters, the computationally costly quan-titative methods provide the same information for the model in this study as the computational efficientMorris method using the median value. Exploring different methods to investigate higher-order effectsand parameter interactions, reveals that correlation of elementary effects and parameter values in Morrismethod can also provide basic information about parameter interactions.","project":"b-bem","bibtex":"@article{Menberg2016Sensitivityanalysis,\n title = {Sensitivity Analysis Methods for Building Energy Models: {{Comparingcomputational}} Costs and Extractable Information},\n author = {Menberg, Kathrin and Heo, Yeonsook and Choudhary, Ruchi},\n year = {2016},\n month = oct,\n journal = {Energy and Buildings},\n volume = {133},\n pages = {433--445},\n doi = {10.1016/j.enbuild.2016.10.005},\n url = {http://dx.doi.org/10.1016/j.enbuild.2016.10.005},\n abstract = {Though sensitivity analysis has been widely applied in the context of building energy models (BEMs),there are few studies that investigate the performance of different sensitivity analysis methods in rela-tion to dynamic, high-order, non-linear behaviour and the level of uncertainty in building energy models.We scrutinise three distinctive sensitivity analysis methods: (a) the computationally efficient Morrismethod for parameter screening, (b) linear regression analysis (medium computational costs) and (c)Sobol method (high computational costs). It is revealed that the results from Morris method taking thecommonly used measure for parameter influence can be unstable, while using the median value yieldsrobust results for evaluations with small sample sizes. For the dominant parameters the results from allthree sensitivity analysis methods are in very good agreement. Regarding the evaluation of parameterranking or the differentiation of influential and negligible parameters, the computationally costly quan-titative methods provide the same information for the model in this study as the computational efficientMorris method using the median value. Exploring different methods to investigate higher-order effectsand parameter interactions, reveals that correlation of elementary effects and parameter values in Morrismethod can also provide basic information about parameter interactions.},\n project = {b-bem}\n}\n\n","author_short":["Menberg, K.","Heo, Y.","Choudhary, R."],"key":"Menberg2016Sensitivityanalysis","id":"Menberg2016Sensitivityanalysis","bibbaseid":"menberg-heo-choudhary-sensitivityanalysismethodsforbuildingenergymodelscomparingcomputationalcostsandextractableinformation-2016","role":"author","urls":{"Paper":"http://dx.doi.org/10.1016/j.enbuild.2016.10.005"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://raw.githubusercontent.com/EECi/home/main/docs/publications/EECi.bib","dataSources":["i79adjzLqYrcX5vqc","oXrzxozuzK4m5k4ju"],"keywords":[],"search_terms":["sensitivity","analysis","methods","building","energy","models","comparingcomputational","costs","extractable","information","menberg","heo","choudhary"],"title":"Sensitivity Analysis Methods for Building Energy Models: Comparingcomputational Costs and Extractable Information","year":2016}