🌬️ Data Science for Wind Energy – PDF
Applying data analytics to wind power systems
Yu Ding
✔ Data Science | ✔ Wind Energy | ✔ Instant Download
Data-Driven Methods for Modern Wind Energy Systems
Data Science for Wind Energy provides a practical and rigorous introduction to data-driven modeling, analysis, and optimization techniques tailored specifically to wind energy applications.
The book bridges statistical learning, machine learning, and engineering knowledge to address real-world challenges in wind farm operation, performance analysis, and reliability.
What This Book Delivers
- 📊 Data-driven modeling of wind energy systems
- 🧠 Machine learning for performance prediction
- ⚙️ Analytics for wind turbine monitoring
- 📈 Optimization of wind farm operations
- 🎯 Decision support using real-world data
Key Topics Covered
- ✔ Wind energy data characteristics and preprocessing
- ✔ Statistical modeling and regression methods
- ✔ Machine learning for forecasting and diagnostics
- ✔ Condition monitoring and fault detection
- ✔ Optimization and decision-making under uncertainty
Who This Book Is For
- Data scientists working in energy systems
- Wind energy and renewable energy engineers
- Graduate students in data science or energy engineering
- Researchers in sustainable and smart energy systems
Why the PDF Edition Is Ideal
- 💻 Immediate access for research and project work
- 🔍 Search algorithms, models, and equations instantly
- 🖍 Annotate datasets, figures, and workflows
- 📚 Ideal for coursework, industry use, and reference
Frequently Asked Questions
Is this a general data science book?
No. It is specifically focused on wind energy applications.
Does it require prior data science knowledge?
Basic statistics and programming knowledge are recommended.
Is it suitable for industry professionals?
Yes. It is designed with real-world wind energy applications in mind.
How do I receive the PDF?
Instant download access is provided after purchase.