Using attentive neural processes for forecasting power usage

This project uses Attentive Neural Process (ANP) on kaggle smart meter data.

This repository also includes a pytorch implementation that has been tweaked to be more flexible and stable. It may be usefull if you are looking for a ANP model in pytorch, and seems more stable than others available now (as of 2019-11-01).

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
• Inputs are:
  • Weather
  • Time features: time of day, day of week, month of year, etc
  • Bank holidays
  • Position in sequence: days since start of window
• Target is: mean power usage on block

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.

Code

This is based on the code listed in the next section, with some changes. The most notable ones add stability, others are to make sure it can handle predicting into the future:

Changes for stability:

• in eval mode, take mean of latent space, and mean of output isntead of sampling
• use log_variance where possible (there is a flag to try without this)
  • and add a minimum bound to std (in log domain) to avoid mode collapse
• use pytorch attention (which has dropout) instead of custom attention
• use batchnorm and dropout on channel dimensions
• use log_prob loss (no mseloss or BCELoss)
• check and skip nonfinite values because for extreme inputs we can still get nan's

Changes for a predictive use case:

• target points are always in the future, context is in the past
• context and and targets are still sampled randomly during training

See also:

A list of projects I used as reference, is modified to make this one:

• Original code in tensorflow from hyunjik11 (author of the original paper) : https://github.com/deepmind/neural-processes/blob/master/attentive_neural_process.ipynb
• First pytorch implementation by soobinseo: https://github.com/soobinseo/Attentive-Neural-Process/blob/master/network.py
• Second pytorch implementation KurochkinAlexey (has some bugs currently) 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