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)