  The objective of the GreenPAD project is to use green energy (wind, solar and
biomass) for powering data-centers that are used to run HPC jobs. As a part of
this it is important to predict the Renewable (Wind) energy for efficient
scheduling (executing jobs that require higher energy when there is more green
energy available and vice-versa). For predicting the wind energy we first
analyze the historical data to find a statistical model that gives relation
between wind energy and weather attributes. Then we use this model based on the
weather forecast data to predict the green energy availability in the future.
Using the green energy prediction obtained from the statistical model we are
able to precompute job schedules for maximizing the green energy utilization in
the future. We propose a model which uses live weather data in addition to
machine learning techniques (which can predict future deviations in weather
conditions based on current deviations from the forecast) to make on-the-fly
changes to the precomputed schedule (based on green energy prediction).
  For this we first analyze the data using histograms and simple statistical
tools such as correlation. In addition we build (correlation) regression model
for finding the relation between wind energy availability and weather
attributes (temperature, cloud cover, air pressure, wind speed / direction,
precipitation and sunshine). We also analyze different algorithms and machine
learning techniques for optimizing the job schedules for maximizing the green
energy utilization.
