close
R I A

AI in Wind Energy

School of Energy · 14-Week Foundational Cohort (Expandable)

AI in Wind & Renewable Energy

AI in Wind & Renewable Energy is a concise, 14-week foundational training program covering energy basics, core wind engineering, data visualization, machine learning (supervised & unsupervised), two dedicated time series modules, and generative and agentic AI applied to renewable operations.

Each module is kept brief by design — a focused set of core concepts plus one hands-on exercise — so the program is easy to run as a short course. Any module can be expanded into a deeper, multi-week track (for example adding SCADA analytics, predictive maintenance, computer vision inspection, or robotics) once the audience and goals are confirmed.

Format: instructor-led, cohort-based — classroom or live virtual, with recorded sessions. Ideal for renewable energy analysts, engineering and operations staff, students, and anyone seeking a foundational AI-for-energy specialization. Pathways continue into AI in Solar, AI in Renewable Energy Systems, and the School of Energy hub.

Program snapshot

Duration

14 weeks (12 core modules + 2-week capstone). Expandable to 16–20 weeks on request.

Format

Instructor-led cohort — classroom or live virtual, with recorded sessions.

Prerequisites

None mandatory. Entry-level Python is helpful but not required.

Certification

RIA Certificate in AI for Wind & Renewable Energy on capstone completion.

Learning outcomes

  • Explain energy and renewable-energy fundamentals, including wind, solar, and hybrid systems
  • Apply AI to wind resource assessment and site optimization
  • Build clear data visualizations and dashboards for energy and operational data
  • Distinguish and apply supervised learning (regression, classification) and unsupervised learning (clustering, anomaly detection)
  • Analyze time series data and apply both classical and deep-learning forecasting techniques
  • Design hybrid solar + wind + battery systems and apply AI-based dispatch optimization
  • Use generative AI and agentic AI to build copilots and automate renewable-operations tasks
  • Understand the basics of green-hydrogen integration with renewable assets

Detailed curriculum

Twelve brief core modules over twelve weeks — including two dedicated time series modules — followed by a two-week guided capstone. Each module is intentionally concise and can be expanded if more depth is needed.

  • 1. Basics of Energy & Renewable Energy (Week 1)

    Build a shared language for energy systems before applying AI to wind and renewables.

    • What energy is, and primary vs. renewable energy sources
    • Overview of solar, wind, hydro, biomass, and hybrid systems
    • Key metrics: capacity factor, Levelized Cost of Energy (LCOE), energy yield
  • 2. Wind Engineering Essentials (Week 2)

    Core wind engineering literacy so AI models map to real assets and decisions.

    • Turbine components, power curves, and wind speed basics
    • Onshore vs. offshore vs. floating wind — a quick comparison
    • Where AI creates value across the wind asset lifecycle
  • 3. AI-Driven Wind Resource Assessment & Site Optimization (Week 3)

    Use ML to augment classical resource modelling and improve site / layout choices.

    • How ML augments classical wind resource modeling (WAsP/WindPRO concepts)
    • AI-assisted layout optimization to maximize energy output
    • Hands-on: a simple site-scoring model in Python
  • 4. Data Visualization for Energy & AI Insights (Week 4)

    Turn operational and forecast data into decision-ready views for engineering and ops teams.

    • Why visualization matters for engineering and operations decisions
    • Building production, performance, and forecast dashboards
    • Tools: Power BI, Python (Matplotlib/Plotly), Excel
  • 5. Supervised & Unsupervised Learning Foundations (Week 5)

    Foundational ML methods applied directly to wind-speed and turbine-power problems.

    • Supervised learning: regression and classification (Random Forest, XGBoost, SVM)
    • Unsupervised learning: clustering and anomaly detection
    • Hands-on: wind-speed and turbine-power prediction models
  • 6. Time Series Analysis Fundamentals (Week 6)

    Classical time-series thinking required before deep-learning forecasts.

    • What time series data is: trend, seasonality, and stationarity
    • Classical methods: moving averages, decomposition, and ARIMA basics
    • Hands-on: exploring and decomposing a renewable energy time series dataset
  • 7. Advanced Time Series Forecasting with Deep Learning (Week 7)

    Neural forecasting for short-term wind and renewable power.

    • Neural network approaches to forecasting: RNN, LSTM/GRU, and Transformer basics
    • Multivariate and probabilistic forecasting for wind and renewable power
    • Hands-on: building a short-term wind power forecast
  • 8. Renewable Energy Forecasting & Grid Integration (Week 8)

    Connect forecasts to planning horizons, curtailment and market-facing decisions.

    • Forecasting horizons: short-term vs. long-term planning
    • Grid integration basics: curtailment and forecast-driven bidding
  • 9. AI + Smart Grid, Storage & Hybrid Renewable Systems (Week 9)

    Design and dispatch hybrid solar + wind + battery assets with AI assist.

    • Smart grids, microgrids, and battery storage basics
    • Designing hybrid Solar + Wind + Battery systems
    • AI-based dispatch optimization for hybrid assets
  • 10. Generative AI & Agentic AI for Renewable Operations (Week 10)

    Build copilots and multi-step agents for renewable operations and reporting.

    • GenAI fundamentals: LLMs, prompting, and retrieval-augmented generation (RAG)
    • Agentic AI: autonomous, multi-step agents for energy operations and reporting
    • Project: a Renewable Energy Copilot for natural-language operational queries
  • 11. Green Hydrogen Integration (Week 11)

    Link variable wind power to green-hydrogen pathways and AI-tuned electrolysis.

    • Renewable-powered electrolysis basics
    • Wind → Electricity → Hydrogen conversion pathways
    • Where AI optimizes electrolyzer operation
  • 12. Capstone Project & Industry Applications (Weeks 12–13)

    End-to-end project work with mentoring and a stakeholder-ready presentation.

    • End-to-end capstone: forecasting, site optimization, hybrid-system design, or a GenAI/agentic copilot
    • Guided mentoring and a stakeholder-ready results presentation
    • Career pathways in renewable energy analytics and AI

Practical AI use cases

Beyond the module labs, the program is grounded in industry-relevant applications — so you can map each technique to a problem you will encounter on the job.

AI-assisted micrositing

Combines terrain, wind, and historical data to recommend turbine positions that maximize energy output. Site Assessment

Short-term power forecasting for grid bidding

Forecasts feed day-ahead and intraday market bids, reducing imbalance penalties. Forecasting / Grid

Anomaly detection on operational data

Unsupervised learning flags unusual turbine or plant behavior for follow-up. ML

Time series decomposition & forecasting

Breaks production data into trend/seasonality and forecasts near-term output. Time Series

Operational dashboards

Visualization turns raw production and performance data into decision-ready views. Data Visualization

AI-based hybrid dispatch optimization

Balances dispatch across solar + wind + battery assets to minimize curtailment and cost. Smart Grid / Hybrid

Renewable Energy Copilot

A GenAI assistant for natural-language questions about operational data and documentation. Generative AI

Autonomous reporting agent

An agentic AI workflow drafts and routes routine operational reports. Agentic AI

AI-optimized electrolyzer operation

Tunes green-hydrogen electrolyzer operation against variable wind output. Green Hydrogen

Tools & technologies

  • Python, Pandas, NumPy; Scikit-learn (supervised & unsupervised ML)
  • Matplotlib / Plotly / Power BI; Excel for quick dashboards
  • TensorFlow / PyTorch (basics)
  • WAsP / WindPRO concepts; open wind & renewable datasets
  • LLM APIs (OpenAI, Anthropic); LangChain / RAG frameworks
  • AI agent frameworks; battery/storage simulation basics
  • Open-source hydrogen / electrolysis references

Who should attend

  • Renewable energy analysts, planning engineers, and operations staff
  • Engineers and graduates new to AI who want a foundational, practical grounding
  • Teams exploring data visualization and dashboarding for energy operations
  • Teams evaluating generative AI or agentic AI copilots for renewable operations
  • Smart-grid, storage, and hybrid-system staff exploring AI-based dispatch
  • Anyone seeking an entry point before a deeper, specialized track

Assessment & certification

  • Weekly hands-on lab assignments (graded)
  • Module quizzes covering ML foundations, forecasting, and grid/hybrid concepts
  • Capstone: end-to-end pipeline and stakeholder presentation
  • Certificate of Completion from RIA Institute of Analytics

Contact & enrollment

Program: AI in Wind & Renewable Energy — 14-Week Foundational Cohort Program (expandable)
Institute: RIA Institute of Analytics · Saligramam, Chennai
Contact the institute for upcoming cohort dates, fees, and enrollment details.

Enquire about AI in Wind Energy

Explore other School of Energy tracks

AI in Renewable Energy Systems
AI in Solar Energy
Fuel Cells / PEMFC
Electric Vehicle Engineering
AI Industry Specializations
School of Energy hub

Ready to start this energy track?

Enquire with RIA for batch schedules, mentoring pathways and project support.

ENQUIRE NOW
Go To Top