Predicting solar energy consumption

Single-feature and multi-feature, auto-regressive LSTM RNN vs linear regression for higly-volatile time series.

Project given for the first HackDay at IFIC (University of Valencia/CSIC), research institute where I completed my PhD in Physics.

• Hourly:
• 12-hour basis:
• 24-hour basis:

Consumption_energy.ipynb

A small town in Spain is sourced by solar energy thanks to a solar plant. Over a year (2015), the plant's energy output and the town's energy consumption data were gathered; and along with it, data from a nearby meteorological station.

We then come with several models that predict the amount of energy output/consumption given a set of weather conditions. In the notebook, we employ several machine learning techniques to tackle these tasks, among which we construct Long Short-Term Memory Recurrent Neural Networks.

We try and predict with an hourly basis, then a 12-hour basis and finally with a 24-hour basis, and compare. Analizing the differences has important consequences because a model will be more or less accurate depending on the time window to predict.

Contents

1. Data Preparation

   1. Data cleaning
   2. Data merging

2. Predicting energy consumption

   1. Data preparation
   2. Multi-feature linear regression
   3. Single-feature, auto-regressive linear regression
   4. Single-feature, auto-regressive long short-term memory (LSTM) recurrent neural network (RNN)
   5. Multi-feature, auto-regressive LSTM RNN.

3. Summary

4. References