I am trying to evaluate the energy potential for solar and wind farms using a multi criteria evaluation for each resource. I have two scenarios for each evaluation and other layers that I consider relevant for this kind of installations. But I do not know how estimate the weight factors for each layer and/or scenario.
I would say that something interesting when evaluating “efficacy” of weights is applying sensitivity and uncertainty analyses in the weighting production stage. However, I think that you are more interest in optimization weighting problems linked to optimization models. I have no experience in working with optimization models but I found a conference proceedings paper that could be interesting for you: http://mme2012.opf.slu.cz/proceedings/pdf/061_Chyna.pdf
If you use Linear Programming you do not need weights factors.
In fact they are compùted by the model ittrself.
Or, you could use Shannon entropy to calculate the weights without bias if so you wish
Anyway, if you think that this can help you write me
A possible solution is start with the weight structure of SMARTER method considering boundaries and present this to decision maker. Then, you should apply the least squares approach to minimize the distance between the weight value, the boundary and the decision maker value reference. Another possible approach to this "calibration" is the frontier evaluatin method presented in Keeney adn Raiffa (1993) - Decisions with multiple objectives: preferences and value tradeoffs.
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