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Questions about the paper. #64
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Hi, The calibration and optimization approaches are out of the scope of this repo, and unfortunately cannot be released publicly. However, they are direct applications of the CMA-ES (from the cma library) and the NSGA-II (from the pygmo library). If you are planning on using both, I recommend using a more general optimization framework, such as pymoo or optuna. The LSTM model can be found the the benchmark module, along with the other models analyzed in the paper. |
Hello @maxjcohen,
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Hi @fremk , The CMA-ES algorithm is designed for continuous variables. Although there have been attempts to adapt it to discrete variables, most of them where either unclear or ineffective. In our approach, we use the CMA-ES for calibration by treating every variable as continuous, which is far from being the most efficient way, but it worked. For the ranges displayed in Table 4, the discrete steps values only apply to the dataset generation, not the calibration.
The best option I have found and experimented with so far are the NSGA-II and NSGA-III models. The former was used for the optimization process presented in the paper.
In this case, I can recommend generic optimization librairies, such as pymoo or optuna. They implement both the NSGA variants and the CMA-ES models. Hope this helps, |
Hello,
Thank you for your great work!
I was wondering if the calibration and optimization approaches as well as your LSTM model used in the study are available somewhere. This repo is for the transformer used for time series but I am still curious about your approach and the calibration and optimization steps that you conducted for the real buildings. Any insight would be appreciated.
Regards,
Karim
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