Energy / Data Engineering / 2025

Renewable Asset Analytics & Automation

Built and used engineering-data workflows for wind and solar assets, combining SCADA and meteorological records with automated quality checks, performance analysis, and decision-ready reporting.

Wind and solar asset analysis across 250M+ time-series records, with an automated review pipeline that reduced processing time by 91%.

Role
Data Science & Engineering Intern
Status
Completed
Tools
Wind SCADAMeteorological dataExcel / VBAPythonTime-series analysisAEPP50 / P75 / P90Root-cause analysis
Renewable asset data pipeline diagram
Non-confidential summary of the workflow I built and used: ingest SCADA and meteorological records, validate data quality, investigate performance, and deliver consistent engineering outputs.

Context

Wind and solar performance analysis depended on large, inconsistent time-series data sets. Manual review was slow and made it difficult to apply the same quality standards across projects.

Work

  • Analyzed more than 250 million SCADA and meteorological data points across five wind and solar projects, supporting performance assessment and technical due diligence.
  • Built an automated Excel/VBA quality-control pipeline with cross-sensor checks, missing-data flags, range validation, and repeatable review outputs.
  • Used turbine and meteorological data to investigate underperformance, wake effects, yaw or sensor issues, and anomalies affecting expected generation.
  • Developed standardized wind-shear and hub-height adjustment tools for more consistent energy-yield calculations.

Engineering approach

  • Separated data-quality validation from engineering interpretation so questionable records were identified before performance conclusions were made.
  • Standardized repeated calculations and review steps to make results easier to compare across projects and analysts.
  • Connected technical findings to the financial and operational questions being reviewed by engineering, finance, and investor stakeholders.

Outcome

  • Reduced the recurring data-review process by 91%, from approximately eight hours to 45 minutes per site.
  • Produced reusable workflows for asset-performance analysis, uncertainty review, and decision-ready reporting.
  • Gained direct experience working with operating renewable assets and communicating engineering findings across technical and business teams.