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Renewable Energy Prediction using Weather Forecasts for Optimal Scheduling in HPC Systems by Sahai, Ankur is a scholarly article available to read on EtoBox.

What is Renewable Energy Prediction using Weather Forecasts for Optimal Scheduling in HPC Systems about?

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 (c

Author

Sahai, Ankur

Published

2014

Language

EN