What’s Next for Power Demand? Rethinking Load Forecasting for a Changing Grid
As the demand for power continues to accelerate rapidly, accurate load forecasting is essential for grid reliability and smart capital investment. But current forecasts are highly uncertain, at both the grid-scale and nodal levels.
Derya Eryilmaz, PhD
Derya Eryilmaz, PhD
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Dr. Derya Eryilmaz is a power markets expert with about 15 years of experience in the power sector, specializing in economic and statistical modeling, power market design, consumer demand modeling and renewable energy procurement. She recently worked at Amazon and Walmart integrating power market analysis and analytics to inform clean firm power investment decisions and corporate decarbonization. She also led teams of data scientists and analysts to deliver enterprise scale AI initiatives resulting in multimillion-dollar cost savings. In her previous work, Dr. Eryilmaz provided advisory and consulting services for the top 20 investor-owned electric and gas utilities clients in North America and she also worked in various oil pipeline, natural gas and power litigation and regulatory proceedings.
Dr. Eryilmaz is teaching renewable energy finance at the Carroll School of Management at Boston College. She also taught data analytics and energy economics in the Department of Economics at Northeastern University, and has published peer-reviewed research in The Energy Journal, Energy Economics, and The Electricity Journal. She holds a PhD in Applied Economics from the University of Minnesota, with a focus on consumer economics and environmental economics.
“Rising Star of the Profession” for Excellence in Energy, Consulting Magazine (2020)
In This Session
- Why forecast accuracy matters for grid investment
- The economic and operational cost of incorrect forecasting for individual companies and the industry at large
- Which commercial and technical barriers are driving today’s uncertainty and how to combat them
- Which emerging techniques are showing the most practical promise
- Some uncertainty comes from the commercial sensitivity around large load siting (i.e., data centers), which requires a market-wide perspective to address. Other gaps can be narrowed with better weather forecasting, more granular grid data, and AI/machine learning.
