Scalable Wind Farm Layout Optimization.
An integrated framework combining graph-based spatial modeling, machine learning, and mathematical optimization to solve large-scale wind turbine placement problems.
The Problem
For just n = 2,000 candidate sites and k = 20 turbines, the combinatorial search space is C(2000, 20) ≈ 2.6 × 10⁴⁷ configurations. Exhaustive search is computationally impossible. Existing methods (genetic algorithms, PSO, gradient-based) suffer exponential scaling.
Our Solution
A hierarchical 4-stage computational pipeline that combines graph-based spatial modeling (Ball Tree KNN), spectral clustering, and Mixed-Integer Linear Programming (MILP) to reduce 2,000 candidates to 20 optimal turbine positions in 3.39 seconds.
This research project tackles one of the most challenging combinatorial problems in renewable energy: optimal wind turbine placement. For just n = 2,000 candidate sites and k = 20 turbines, the combinatorial search space is C(2000, 20) ≈ 2.6 × 10⁴⁷ configurations — making brute-force approaches computationally impossible. By integrating spectral clustering, KNN graph modeling, and MILP optimization in a hierarchical pipeline, we achieve a 95% reduction in candidates while preserving 76% of optimal solution quality — all in under 3.4 seconds.
Step-by-step Pipeline.
Data Collection & Feature Extraction
Wind data from NREL WIND Toolkit: 8,760 hourly measurements per location at 100m hub height across California. Statistical features extracted per site: mean wind speed, turbulence intensity, Weibull distribution parameters (shape k & scale c), and capacity factor.
Key Metrics
Stack
Let's discuss
clean energy together.
I'm always open to discussing research collaborations, internship opportunities, or just talking about machine learning and renewable energy optimization. If you're passionate about building a sustainable future, reach out!