Using attentive neural processes for forecasting power usage

This project uses attentive neural processes (ANP) for on kaggle smart meter data. The ANP code used here it more flexible and stable than other pytorch ANP implementations available as of 20191101.

Changes for stability:

• in eval mode, take mean of latent space, and mean output, don't sample
• use log_variance where possible
  • and add a minimum bound to std (in log domain) to avoid mode collapse
• use pytorch attention (which has dropout)
• use batchnorm and dropout on channel dimensions
• added log_prob loss
• check and skip nonfinite values because for extreme inputs we can still get nan's

Usage

• clone this repository
• see requirements.txt for requirements and version
• Start and run the notebook smartmeters.ipynb

Data

• Some data is included, you can get more from https://www.kaggle.com/jeanmidev/smart-meters-in-london/version/11

Example outputs

Here the black dots are input data, the dotted line is the true data. The blue line is the prediction, and the blue shadow is the uncertainty.

See also:

• Original code in tensorflow: https://github.com/deepmind/neural-processes/blob/master/attentive_neural_process.ipynb
• First pytorch implementation: https://github.com/soobinseo/Attentive-Neural-Process/blob/master/network.py
• Second pytorch implementation (has some major bugs) https://github.com/KurochkinAlexey/Attentive-neural-processes/blob/master/anp_1d_regression.ipynb
• If you want to try vanilla neural processes: https://github.com/EmilienDupont/neural-processes/blob/master/example-1d.ipynb