Recommendation for Best Neural Network Type (in TensorFLow or PyTorch) For Fitting Problems
I am looking to develop a simple Neural Network in PyTorch or TensorFlow to predict one numeric value based on several inputs. 

For example, if one has data describing the interior comfort parameters for a building, the NN should predict the numeric value for the energy consumption. 

Both PyTorch or TensorFlow documented examples and tutorials are generally focused on classification and time dependent series (which is not the case). Any idea on which NN available in those libraries is best for this kind of problems? I'm just looking for a hint about the type, not code.
Thanks!   

The type of problem you are talking about is called a regression problem. In such types of problems, you would have a single output neuron with a linear activation (or no activation). You would use MSE or MAE to train your network.

If your problem is time series(where you are using previous values to predict current/next value) then you could try doing multi-variate time series forecasting using LSTMs.

If your problem is not time series, then you could just use a vanilla feed forward neural network. This article explains the concepts of data correlation really well and you might find it useful in deciding what type of neural networks to use based on the type of data and output you have.
