Introduction

Wind Energy Forecasting and Analytics Training Course provides a practical, data-driven pathway for professionals seeking advanced capabilities in wind power forecasting, renewable energy analytics, SCADA data analysis, machine learning, artificial intelligence (AI), time-series modeling, and grid integration. As wind generation becomes increasingly important to modern power systems, accurate forecasting is essential for energy scheduling, grid stability, balancing, asset optimization, and improved renewable-energy utilization. Current industry and research developments increasingly emphasize deep learning, Transformer architectures, physics-informed machine learning, probabilistic forecasting, digital twins, explainable AI, and real-time analytics.

The course combines technical concepts, practical datasets, analytical exercises, forecasting model development, visualization, and industry case studies to help participants translate operational data into actionable energy insights. Learners explore the complete analytics lifecycle from SCADA and meteorological data acquisition through preprocessing, feature engineering, model development, validation, uncertainty analysis, and deployment. Case-based learning reflects emerging applications such as digital twins for wind farms, predictive maintenance, intelligent operations, wake-effect analysis, and AI-enabled energy management, giving participants a strong foundation for applying advanced analytics to real-world wind-energy challenges.

Programme Curriculum

Wind Energy Forecasting and Analytics Training Course

Introduction

Course Duration

5 days

Course Objectives

By the end of the course, participants will be able to:

Understand wind energy forecasting principles, variability, uncertainty, and operational requirements.
Analyze SCADA, meteorological, IoT, and historical wind-power datasets.
Apply data preprocessing, cleaning, feature engineering, and quality-control techniques.
Build short-term, medium-term, and long-term wind-power forecasting workflows.
Apply machine learning and deep learning algorithms to wind-energy prediction.
Explore LSTM, RNN, Transformer, ensemble, and hybrid forecasting models.
Perform time-series analysis, cross-validation, and forecast-error evaluation.
Develop probabilistic forecasting and uncertainty quantification approaches.
Use explainable AI (XAI) techniques to interpret forecasting and analytics results.
Apply predictive analytics and anomaly detection for wind-turbine performance monitoring.
Explore digital twin and physics-informed machine learning applications in wind-energy operations.
Develop analytics that support grid integration, dispatch, energy scheduling, and renewable-energy optimization.
Translate analytical outputs into data-driven operational and investment decisions.

Target Audience

Wind Energy Engineers and renewable-energy engineers
Electrical and Power Systems Engineers
Energy Data Analysts and data scientists
SCADA, IoT, and Monitoring Specialists
Wind Farm Operations and Maintenance Professionals
Utility, Grid Operations, and Energy Planning Professionals
Renewable-Energy Consultants and Project Managers
Researchers, Academics, and Technical Professionals working with AI and energy analytics

Course Modules

Module 1: Fundamentals of Wind Energy Forecasting

Wind-resource characteristics and power-generation variability
Forecasting horizons
Wind-turbine power curves and forecasting workflows
Forecasting requirements for grid operations and energy markets
Case Study: Developing a baseline forecasting workflow for a utility-scale wind farm

Module 2: Wind Energy Data Acquisition and SCADA Analytics

SCADA data architecture, sensors, tags, and operational parameters
Integration of wind speed, direction, temperature, nacelle orientation, rotor speed, and power output
Meteorological, reanalysis, remote-sensing, and IoT data sources
Data-quality assessment, missing values, outliers, and sensor anomalies
Case Study: Cleaning and analyzing real-world wind-turbine SCADA time-series data

Module 3: Data Analytics, Feature Engineering and Time-Series Modeling

Exploratory data analysis and data visualization
Time-series decomposition, trends, seasonality, and autocorrelation
Feature engineering for wind-speed and power forecasting
Lag variables, rolling statistics, weather features, and turbine operating states
Case Study: Creating predictive features from multi-parameter wind-farm datasets

Module 4: Machine Learning for Wind Power Forecasting

Regression, Random Forest, Gradient Boosting, XGBoost, and ensemble learning
Model training, testing, cross-validation, and hyperparameter optimization
Forecasting performance metrics including MAE, RMSE, MAPE, and R²
Model comparison, overfitting prevention, and generalization
Case Study: Comparing traditional machine learning models for wind-power prediction

Module 5: Deep Learning, Transformers and Advanced Forecasting

LSTM, RNN, BiLSTM, attention mechanisms, and sequence modeling
Introduction to Transformer-based time-series forecasting
Multi-step and spatio-temporal wind-power forecasting
Hybrid and ensemble deep-learning architectures
Case Study: Applying a Transformer-based framework to multi-step wind-power forecasting

Module 6: Probabilistic Forecasting, Uncertainty and Explainable AI

Deterministic versus probabilistic forecasting
Prediction intervals and uncertainty quantification
Forecast-error analysis and calibration
Explainable AI, feature importance, and SHAP-based interpretation
Case Study: Using interpretable AI to understand drivers of wind-power forecast uncertainty

Module 7: Predictive Analytics, Digital Twins and Wind-Farm Optimization

Anomaly detection and predictive maintenance
Wind-turbine performance analytics and early-warning indicators
Digital twin concepts for monitoring, simulation, and optimization
Wake effects, turbine interactions, curtailment, and operational scenarios
Case Study: Using a digital-twin approach to analyze wind-farm performance and operational scenarios

Module 8: Grid Integration, Deployment and Capstone Analytics

Forecast-informed energy scheduling, dispatch, and grid balancing
Real-time analytics and forecasting-system deployment
Dashboard development and communication of forecast insights
Model monitoring, retraining, governance, and operational integration
Case Study: Designing an end-to-end wind forecasting solution for renewable-energy grid integration

Training Methodology

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Sep 21 2026

21 Sep — 25 Sep 2026

💻 Online / Virtual session

Online $1,000