View source for Deep Learning and Energy Forecasting

Title: Deep Learning and Energy Forecasting

Host:  GasDay Laboratory

Responsible faculty: Richard Povinelli, George Corliss, Ron Brown, Tom Quinn

Description:

How much natural gas is needed each day for the next week?

At GasDay we answer this question for over 30 local distribution
companies.  Each day, we help forecast about 20% of the natural gas
delivered to residential, industrial, and commercial customers in the
US.  We use modern software architecture and tools, including
databases, multi-tiered systems, distributed computing, automated
testing, and user interface design.

We offer research opportunities in mathematical modeling, statistical
analysis of data, and software engineering.  Previous REU
participants' projects have involved water demand forecasting, high
performance computing, data mining, hourly natural gas demand
forecasting, electricity demand forecasting using model trees, and
evaluating strategies for weather-normalizing natural gas demand.  If
you work with GasDay, your interests and skills will be matched with a
suitable project, you will have plentiful attention from your
mentor(s), and you will enjoy working in an active community of
scholars. Current projects will focus on deep learning and
probabilistic forecasting.

Wish list of skills: Computer programming (MATLAB)