Holger Teichgraeber

Holger Teichgraeber

PhD Candidate

Stanford University

Bio

I am a Ph.D. candidate in the Department of Energy Resources Engineering at Stanford University. My advisor is Prof. Adam Brandt and I am a Wells Family Stanford Graduate Fellow.

In my research, I focus on applying state-of-the-art computational tools at the intersection of machine learning and optimization to energy systems problems. As an example, I have worked extensively on the development of new algorithms and applications of time-series aggregation for infrastructure planning and operations. Out of my research, two open-source software packages have emerged: TimeSeriesClustering implements unsupervised learning methods for time-series data, and CapacityExpansion provides an extensible, data-driven infrastructure planning tool for energy systems.

I have previously interned at the battery software company Doosan GridTech; in the renewable energy forecasting division of Vaisala (formerly 3Tier); in the market optimization group at RWE Power, one of Europe’s largest utility companies; and at ThyssenKrupp, one of the world’s largest steel producers.

Interests

  • Machine Learning
  • Optimization
  • Energy Systems
  • Supply Chain & Logistics

Education

  • PhD in Energy Resources Engineering, 2020

    Stanford University

  • Ignite Program in Innovation and Entrepeneurship, 2018

    Stanford University, Graduate School of Business

  • MS in Energy Resources Engineering, 2016

    Stanford University

  • BS in Mechanical Engineering, 2014

    RWTH Aachen University

Consulting

I have successfully worked with multiple companies on short-term and medium-term projects in the past. I would be happy to talk about how we can work together, please contact me for an initial consultation.

Publications

Software

CapacityExpansion

An extensible, data-driven infrastructure planning tool for energy systems in Julia. The main purpose of the package is to provide a simple-to-use generation and transmission capacity extension model that allows to address a diverse set of research questions in the area of energy systems planning.

TimeSeriesClustering

Julia implementation of unsupervised learning methods for time series datasets. It provides functionality for clustering and aggregating, detecting motifs, and quantifying similarity between time series datasets.

Honors & ­Awards

Rising Environmental Leaders Program

Outstanding achievement in mentoring award

Grid Sciences Winter School & Conference Scholarship

Centennial Teaching Assistant Award

Stanford Graduate Fellowship in Science and Engineering

Dean’s List and Top 0.5% of Graduating Class

Scholarship of the German Academic Scholarship Foundation

Contact