Module 1: Foundations of Renewable Energy and Data

• Overview of global renewable energy sources (Solar PV, Wind, Hydro, Geothermal) and their data characteristics
• Understanding common energy data types: SCADA, meteorological (irradiance, wind speed), maintenance logs, and financial data
• Key performance indicators (KPIs) in solar and wind: Capacity Factor, Performance Ratio, Availability, and curtailment
• Setting up the integrated development environment (IDE) for Python (Anaconda, Jupyter Notebooks)
• Practical session: Initializing the Python environment and importing a sample solar farm performance dataset.

Module 2: Python Environment Setup and Fundamentals

• Deep dive into the Pandas library for efficient data manipulation and indexing (Series and DataFrames)
• Leveraging NumPy for high-performance numerical operations on large energy arrays
• Introduction to data loading techniques (CSV, Excel, JSON, and connecting to basic databases)
• Best practices for organizing Python code and using virtual environments for project dependencies
• Practical session: Writing Python scripts to load, inspect, and calculate basic statistics (mean, variance, quartiles) on wind speed and direction data.

Module 3: Data Acquisition and Cleaning for Energy Datasets

• Handling missing data: Imputation techniques (mean, median, forward fill) versus removal, and impact assessment
• Identifying and treating outliers and anomalies in sensor data using statistical methods (e.g., Z-scores, IQR)
• Data normalization, standardization, and feature scaling for preparatory machine learning tasks
• Data aggregation and resampling of time-series data (e.g., from 1-minute to hourly averages) using Pandas
• Practical session: Implementing a function to clean a noisy solar irradiance dataset, including outlier removal and resampling to hourly intervals.

Module 4: Exploratory Data Analysis (EDA) of Solar PV Data

• Creating informative visualizations of solar performance metrics using Matplotlib and Seaborn (histograms, scatter plots, box plots)
• Analyzing the correlation between meteorological variables (temperature, irradiance) and power output
• Identifying seasonal and diurnal patterns in solar energy generation profiles
• Using rolling statistics and lag plots to understand temporal dependencies in performance data
• Practical session: Generating a comprehensive EDA report for a PV plant, visualizing PR degradation over time and generating a heat map of key variable correlations.

Module 5: Wind Energy Resource Assessment and Data Preparation

• Understanding wind speed distribution (Weibull and Rayleigh models) and calculating the Power Density
• Handling directional data: Plotting wind roses and converting polar coordinates for analysis
• Processing SCADA data: Filtering for turbine operational status, yaw misalignment, and blade pitch angles
• Creating custom feature variables (e.g., turbulence intensity, wind shear exponent) for advanced modeling
• Practical session: Calculating the Weibull parameters for a given site's wind speed data and generating a comprehensive wind rose visualization using Python.

Module 6: Advanced Time Series Analysis for Energy Data

• Decomposition of time series data: Identifying trend, seasonality, and residual components
• Stationarity and differencing: Testing for unit roots (ADF test) and preparing data for ARIMA models
• Introduction to ARIMA, SARIMA, and Prophet models for single-variable energy forecasting
• Implementing exogenous variables (weather data) into advanced time series models (ARIMAX/SARIMAX)
• Practical session: Building and evaluating a SARIMAX model in Python to forecast hourly electricity demand or solar generation, incorporating temperature as an exogenous variable.

Module 7: Introduction to Machine Learning for Renewables

• Fundamentals of supervised learning: Regression vs. Classification in the energy context (e.g., power curve modeling)
• Feature engineering and selection techniques to improve model performance (e.g., Polynomial Features)
• Understanding model validation: Cross-validation, Train/Test/Validation splits, and preventing overfitting
• Evaluating regression models: Metrics like MAE, MSE, RMSE, and $R^2$ for energy prediction tasks
• Practical session: Developing a baseline Linear Regression model to predict PV power output based on irradiance and module temperature, and assessing its performance metrics.

Module 8: Predictive Modeling for Energy Output Forecasting

• Implementing advanced regression techniques: Random Forests and Gradient Boosting Machines (XGBoost, LightGBM) for power curve analysis
• Hyperparameter tuning using GridSearchCV and RandomizedSearchCV for optimized model performance
• Developing anomaly detection systems using Isolation Forest or One-Class SVM to flag underperforming assets
• Model interpretability: Using SHAP values to explain feature influence on energy output predictions
• Practical session: Training an XGBoost model for 24-hour-ahead wind power forecasting and visualizing the feature importance scores to understand model drivers.

Module 9: Power BI Desktop and Data Modeling Fundamentals

• Connecting Power BI to various data sources (Local files, databases, web APIs for energy data)
• Introduction to the Power Query Editor (M language) for data transformation, cleaning, and shaping
• Establishing efficient data models: Understanding and managing relationships between fact and dimension tables
• Creating a star schema for optimal performance in renewable energy reporting
• Practical session: Importing data from three different sources (SCADA, weather, maintenance) into Power BI and cleaning and transforming the data using Power Query functions.

Module 10: Creating Interactive Solar PV Dashboards in Power BI

• Best practices for dashboard design: Layout, color theory, and visual hierarchy for energy reports
• Utilizing native Power BI visualizations: Cards, Gauges, Tables, and common charts for KPI tracking
• Implementing interactive slicers, filters, and drill-through actions for dynamic analysis
• Creating calculated columns and simple measures using Data Analysis Expressions (DAX)
• Practical session: Building a dynamic Power BI dashboard to monitor the daily, monthly, and annual Performance Ratio (PR) and availability of a solar farm.

Module 11: Advanced DAX and Calculated Measures

• Deep dive into fundamental DAX concepts: Evaluation context, row context, and filter context
• Mastering the CALCULATE function, the most powerful function in DAX, for complex calculations
• Time intelligence functions (TOTALYTD, SAMEPERIODLASTYEAR, DATEADD) for comparing performance metrics
• Creating sophisticated measures for metrics like rolling averages, cumulative generation, and cost variance
• Practical session: Writing advanced DAX measures to compare the current month's generation against the same month last year and calculate the year-to-date capacity factor.

Module 12: Geospatial Analytics of Renewable Assets

• Introduction to geospatial data in Python: Using libraries like GeoPandas and Folium
• Visualizing wind farm layout and solar panel distribution using coordinates
• Integrating geospatial data into Power BI using map visuals and custom shape maps
• Analyzing proximity effects, wake effects (wind), and shading losses (solar) using spatial data
• Practical session: Mapping the coordinates of all wind turbines in a sample park in a Power BI map visual and visualizing their generation output.

Module 13: Predictive Maintenance Analytics for Wind Turbines

• Defining the predictive maintenance problem: Anomaly detection and Remaining Useful Life (RUL) estimation
• Feature extraction from vibration and temperature sensor data for machine learning
• Implementing supervised classification models (e.g., Logistic Regression, SVM) to predict component failure probability
• Evaluating maintenance models: Confusion matrix, Precision, Recall, and F1-score
• Practical session: Using a classification model to predict the probability of a gearbox overheating based on historical temperature and vibration readings.

Module 14: Financial Modeling and Levelized Cost of Energy (LCOE)

• Understanding the key financial inputs for renewable energy projects (CAPEX, OPEX, PPA price)
• Calculating the Net Present Value (NPV) and Internal Rate of Return (IRR) using Python financial libraries
• Step-by-step calculation of the Levelized Cost of Energy (LCOE)
• Creating sensitivity analysis charts to show the impact of key variables (e.g., capacity factor, discount rate) on LCOE
• Practical session: Developing a Python script to calculate the LCOE for a hypothetical 50MW solar project and performing a sensitivity analysis on the project's capacity factor.

Module 15: Grid Impact Analysis and Load Profile Modeling

• Analyzing load profiles: Understanding peak demand, base load, and duck curve phenomena
• Modeling the impact of intermittent renewables on grid stability and balancing costs
• Creating synthetic load and generation profiles using statistical techniques
• Integrating battery energy storage system (BESS) data into forecasting models to smooth supply
• Practical session: Using Python to combine a renewable generation profile with a typical urban load profile to simulate and visualize the net load curve.

Module 16: Optimization and Scenario Analysis in Renewable Projects

• Introduction to optimization techniques: Linear programming for resource allocation
• Modeling optimal dispatch strategies for hybrid renewable energy systems (solar + storage)
• Running Monte Carlo simulations to quantify risk and uncertainty in long-term energy forecasts
• Designing and implementing "What-If" parameters in Power BI to test different project scenarios (e.g., changes in PPA price)
• Practical session: Developing a basic optimization model in Python (using libraries like PuLP) to find the optimal mix of solar and battery capacity to meet a fixed energy demand profile.

Module 17: Capstone Project: End-to-End Analytics Workflow

• Defining the project scope, objectives, and success metrics for a real-world energy problem
• Data ingestion, processing, and storage architecture for the capstone project
• Selecting and implementing the most appropriate analytical models (ML/Time Series/Financial)
• Integrating Python model outputs back into the Power BI data model for reporting
• Practical session: Guided execution of the capstone project, requiring participants to integrate a Python-generated forecast into a Power BI dashboard.

Module 18: Communicating Insights and Data Storytelling

• Principles of effective data storytelling for non-technical stakeholders (investors, executives)
• Techniques for summarizing complex analytical findings into clear, concise business recommendations
• Advanced Power BI features: Report security, sharing, and deployment strategies (Power BI Service)
• Reviewing and critiquing capstone project presentations and analytical reports
• Practical session: Presenting the final Capstone Project findings to the class, focusing on clear visuals and a compelling narrative.

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588