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This question needs to be stated more clearly for your goals to be understood. Typically in a optimization problem you are trying to find a "sweet spot" within a set of model parameters which minimizes (or maximizes) the evaluation of some objective function (sometimes called a cost function).

An example optimization problem might be minimizing annual energy use in the HVAC system by varying the size of some external glazing. Intuitively we hypothesis there may be a "sweet spot" window size because heating energy use can be offset by solar gains, but windows also lower the U-value of the envelope increasing HVAC energy use. So you may set up an optimization problem with the objective function calculating annual HVAC energy use, and the parameter space being the window to wall ratio (WWR).

You wouldn't want to minimize temperature outright in an optimization, this would mean your optimal solution would have the coldest temperature be the optimal solution. Perhaps you want to minimize the time where some temperature range isn't maintained within the zone, where the objective function returns "unmet hours"?

For a clear mathematically rigorous explanation and classification of optimization problems, specifically related to building performance simulation, you should check out the GenOpt Manual.

This question needs to be stated more clearly for your goals to be understood. Typically in a optimization problem you are trying to find a "sweet spot" within a set of model parameters which minimizes (or maximizes) the evaluation of some objective function (sometimes called a cost function).

An example optimization problem might be minimizing annual energy use in the HVAC system by varying the size of some external glazing. Intuitively we hypothesis there may be a "sweet spot" window size because heating energy use can be offset by solar gains, but windows also lower the U-value of the envelope increasing HVAC energy use. So you may set up an optimization problem with the objective function calculating annual HVAC energy use, and the parameter space being the window to wall ratio (WWR).

You wouldn't want to minimize temperature outright in an optimization, this would mean your optimal solution would have the coldest temperature be the optimal solution. Perhaps you want to minimize the time where some temperature range isn't maintained within the zone, where the objective function returns "unmet hours"?

For a clear mathematically rigorous explanation and classification of optimization problems, specifically related to building performance simulation, you should check out the GenOpt Manual.

This question needs to be stated more clearly for your goals to be understood. Typically in a optimization problem you are trying to find a "sweet spot" within a set of model parameters which minimizes (or maximizes) the evaluation of some objective function (sometimes called a cost function).

An example optimization problem might be minimizing annual energy use in the HVAC system by varying the size of some external glazing. Intuitively we hypothesis there may be a "sweet spot" window size because heating energy use can be offset by solar gains, but windows also lower the U-value of the envelope increasing HVAC energy use. So you may set up an optimization problem with the objective function calculating annual HVAC energy use, and the parameter space being the window to wall ratio (WWR).

You wouldn't want to minimize temperature outright in an optimization, this would mean your optimal solution would have the coldest temperature be the optimal solution. Perhaps you want to minimize the time where some temperature range isn't maintained within the zone, where the objective function returns "unmet hours"?

For a clear mathematically rigorous explanation and classification of optimization problems, specifically related to building performance simulation, you should check out the [GenOpt Manual], GenOpt Manual, section 4(http://simulationresearch.lbl.gov/GO/download/manual-3-1-0.pdf).

This question needs to be stated more clearly for your goals to be understood. Typically in a optimization problem you are trying to find a "sweet spot" within a set of model parameters which minimizes (or maximizes) the evaluation of some objective function (sometimes called a cost function).

An example optimization problem might be minimizing annual energy use in the HVAC system by varying the size of some external glazing. Intuitively we hypothesis hypothesize there may be a "sweet spot" window size because heating energy use can be offset by solar gains, but windows also lower the U-value of the envelope increasing HVAC energy use. So you may set up an optimization problem with the objective function calculating annual HVAC energy use, and the parameter space being the window to wall ratio (WWR).

You wouldn't want to minimize temperature outright in an optimization, this would mean your optimal solution would have the coldest temperature be the optimal solution. Perhaps you want to minimize the time where some temperature range isn't maintained within the zone, where the objective function returns "unmet hours"?

For a clear mathematically rigorous explanation and classification of optimization problems, specifically related to building performance simulation, you should check out the GenOpt Manual, section 4.

This question needs to be stated more clearly for your goals to be understood. Typically in a optimization problem you are trying to find a "sweet spot" within a set of model parameters which minimizes (or maximizes) the evaluation of some objective function (sometimes called a cost function).

An example optimization problem might be minimizing annual energy use in the HVAC system by varying the size of some external glazing. Intuitively we hypothesize there may be a "sweet spot" window size because heating energy use can be offset by solar gains, but windows also lower increase the U-value of the envelope increasing HVAC energy use. So you may set up an optimization problem with the objective function calculating annual HVAC energy use, and the parameter space being the window to wall ratio (WWR).

You wouldn't want to minimize temperature outright in an optimization, this would mean your optimal solution would have the coldest temperature be the optimal solution. Perhaps you want to minimize the time where some temperature range isn't maintained within the zone, where the objective function returns "unmet hours"?

For a clear mathematically rigorous explanation and classification of optimization problems, specifically related to building performance simulation, you should check out the GenOpt Manual, section 4.