Optimizing models and controllers for distributed energy resources

Problem

Ensemble Stacking

Figure 1. Schema illustrating the fitting stage and the prediction stage of stacking with cross-validation.

Experiments

  • performance bound (PB)
  • total power consumed.

Results

Figure 2. Convergence plot maximizing the R2 metric between the predicted
and the real values over the training data set using dynamic stacking for 500
different experiments. The grey lines represent every single experiment, while
the dotted line is the mean per iteration.
Figure 3. Contour lines of the loss function for one of the experiments. In red
the minimum is found for 44 iterations γ and 4 models M.
Table I. R2 values related to different models and calculated on the test set.
Figure 5. Data illustrating the behavior of the same MPC algorithm with different predictions coming from different models, using
data collected in the month of September.
Table II. Total energy consumed by the MPC controller using different prediction models. PB is the performance bound with perfect predictions. The dynamic stacker (DS) is the one performing best.

How can LotusLabs help you?

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Lotus Labs

Lotus Labs

25 Followers

Transform your business into an AI-driven enterprise. We specialize in Machine learning for Retail, Insurance, and Healthcare industries. www.lotuslabs.ai