I am studying (via simulation) a system that has several input parameters. The output of the system is influenced by the input parameters. My goal is to identify the parameters that have the most impact on the output of the system. I ran a large amount of simulations and for each one of them I changed the value of each parameters using a random algorithm. To identify the parameters that have the most impact on the output I was planning on doing a basic multivariate linear regression including all the parameters, however, it turned out to have a low R2 and low F-stat.
I am working a little bit out of my element here so I am looking for suggestions regarding what I should investigate next to achieve my goal.
The context of the study is building energy efficiency and the main difficulty is that some input parameters have multiple effect on the system. For example: if I lower the total lighting power used in the simulation it will have for effect to decrease the cooling load of my building but if at the same time I was increasing the cooling efficiency of my cooling system the energy saved by increasing the efficiency would be less than if the lighting power was not decreased. In this example I would like to show which has more impact in regards to the energy consumption. However, in what I am trying to do I have a lot more input parameters.