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Electric Power Research Institute
Palo Alto, CA, United States
30+ days ago
Electric Power Research Institute
Palo Alto, California, United States
30+ days ago

Description

This student position will perform timeseries data analytics and machine-learning models to support load and renewables forecasting research projects to meet challenges of the future electrical grid. The student will work in with small teams of EPRI researchers to perform analyses that support will support real world electrical utility analytics directly or through research studies that influence their decision making. Students will improve their timeseries data analytics skills, software skills, understanding important needs in load and renewables forecasting in energy and potentially machine-learning modeling.

Candidates should have strong timeseries data analytics skills using a scripting language (Python preferred), plotting tools. Candidates should have familiarity with collecting and analyzing publicly available weather data sets, such as Numerical Weather Prediction (NWP) forecasts and satellite data (GOES satellites in North America). Nice to have skills include feature selection analyses (e.g., correlation statistics, select k best) and able to build timeseries machine-learning models (e.g., neural networks, decision trees, regression), and familiarity with performance metrics (RMSE, MBE, etc.). Ideal candidates would also have interest in probabilistic forecasting, be familiar with unit commitment and economic dispatch concepts in power systems, and interest in how to assess value to forecast (e.g., applying a monetary value to improved forecast performance). Experience with weather dynamics and/or modeling of renewable energy power output is also desirable.

Programming Languages:

  • Python or R


Development Libraries/Tools:

  • Python scientific computing suites of packages: numpy, scipy, pandas (or equivalent in R)
  • Machine-learning toolkits (1 or more): scikit-learn, keras, tensorflow, pytorch or similar (or equivalent in R)
  • Version control: git (creating issues, performing pull requests)

EPRI participates in E-Verify, an online system operated jointly by the Department of Homeland Security and the Social Security Administration (SSA). EPRI uses the system to check the work status of new hires by comparing information from the employee's I-9 form against SSA and Department of Homeland Security databases.

Note: To ensure compliance with U.S. export controls, please indicate your U.S. citizenship or (for foreign citizens) your U.S. visa/immigration status in your resume or cover letter.

EPRI is an equal opportunity employer. EEO/AA/M/F/VETS/Disabled

Together . . . Shaping the Future of Energy.

www.epri.com



Requirements

Candidates should have strong timeseries data analytics skills using a scripting language (Python preferred), plotting tools. Candidates should have familiarity with collecting and analyzing publicly available weather data sets, such as Numerical Weather Prediction (NWP) forecasts and satellite data (GOES satellites in North America). Nice to have skills include feature selection analyses (e.g., correlation statistics, select k best) and able to build timeseries machine-learning models (e.g., neural networks, decision trees, regression), and familiarity with performance metrics (RMSE, MBE, etc.). Ideal candidates would also have interest in probabilistic forecasting, be familiar with unit commitment and economic dispatch concepts in power systems, and interest in how to assess value to forecast (e.g., applying a monetary value to improved forecast performance). Experience with weather dynamics and/or modeling of renewable energy power output is also desirable.

Programming Languages:

  • Python or R


Development Libraries/Tools:

  • Python scientific computing suites of packages: numpy, scipy, pandas (or equivalent in R)
  • Machine-learning toolkits (1 or more): scikit-learn, keras, tensorflow, pytorch or similar (or equivalent in R)
  • Version control: git (creating issues, performing pull requests)

Job Information

  • Job ID: 61612175
  • Location:
    Palo Alto, California, United States
    Knoxville, Tennessee, United States
    Remote Is Possible, Various, United States
  • Position Title: Forecasting, Machine Learning and Data Analytics Internship
  • Company Name For Job: Electric Power Research Institute
  • Job Function: Engineering
  • Job Type: Internship
  • Job Duration: 3-6 Months
  • Min Education: Master's Degree
  • Min Experience: None
  • Required Travel: 0-10%

Please refer to the company's website or job descriptions to learn more about them.

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