How can I find out the most impactful inputs to use as the predictors for the Recurrent Neural Network (RNN) modeling? I have a CSV file that has 25 columns and all of them are numeric. I want to predict one of the columns using the rest of the columns (24 columns). How can I find out how many of those 24 columns are impactful enough to be used as input using Mutual Information Analysis in python?
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Usually Energy Disaggregation is done from a single input (grid consumption) to multiple outputs (appliances in a particular home). If you want to include multiple inputs, try building a multiple-input branch Neural Network and then stack your RNN layers.
You can also have a look this blog for a better understanding of Disaggregation.
If you want to get started with Energy Disaggregation and NILM using Deep Learning, you can have a look at this Open Source library: https://github.com/plexflo/plexflo. There is a Deep Learning model also included (LSTM) that can do basic Energy DIsaggregation.