Introduction

Solar Resource Assessment and Forecasting Training Course provides a practical, industry-focused foundation for evaluating solar energy potential and developing reliable solar irradiance and photovoltaic (PV) power forecasts. The course covers Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI), Plane-of-Array (POA) irradiance, solar radiation databases, meteorological datasets, satellite remote sensing, GIS, time-series analysis, energy-yield assessment, and uncertainty analysis. Participants learn how resource-quality information supports solar PV feasibility studies, project development, plant design, grid integration, energy yield estimation, operational planning, and investment decisions. Modern resource-assessment workflows increasingly combine ground measurements, satellite observations, numerical weather prediction, and statistical or machine-learning approaches.

The training also introduces emerging AI-powered solar forecasting, machine learning, deep learning, hybrid forecasting, satellite-based nowcasting, probabilistic forecasting, physics-informed models, and spatiotemporal analytics. Current research highlights the growing use of satellite imagery and advanced ML architectures for intraday forecasting, while hybrid approaches combine physical and data-driven models to improve operational usefulness. Through practical exercises and case studies, participants develop the ability to transform raw solar and weather data into actionable resource assessments, forecast models, performance indicators, and decision-support outputs for utility-scale, commercial, distributed, and hybrid renewable-energy projects.

Programme Curriculum

Solar Resource Assessment and Forecasting Training Course

Introduction

Course Duration

5 days

Course Objectives

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

Understand the principles of solar resource assessment, solar radiation, and photovoltaic energy conversion.
Analyze GHI, DNI, DHI, POA irradiance, solar geometry, and meteorological parameters.
Acquire and validate solar datasets from ground stations, satellite products, reanalysis databases, and TMY datasets.
Apply GIS and geospatial analytics to solar resource mapping and project-site evaluation.
Conduct solar data quality control, cleaning, gap filling, validation, and uncertainty analysis.
Evaluate long-term solar resource variability for PV feasibility and bankable energy-yield assessments.
Develop short-term, intraday, day-ahead, and longer-horizon solar forecasting workflows.
Apply statistical models, time-series methods, and machine learning to solar irradiance forecasting.
Explore deep learning, LSTM, CNN, transformer, and spatiotemporal forecasting concepts.
Integrate satellite imagery, cloud information, weather forecasts, and ground observations into forecasting systems.
Measure forecast performance using MAE, RMSE, nRMSE, MAPE, bias, skill scores, and uncertainty metrics.
Translate irradiance forecasts into PV power forecasts for operational and grid-integration applications.
Interpret forecasting results for energy yield optimization, grid flexibility, battery dispatch, asset management, and investment decisions.

Target Audience

Solar PV engineers and renewable-energy engineers
Solar resource assessment specialists
Energy analysts and forecasting professionals
GIS, remote sensing, and geospatial professionals
Solar project developers and feasibility-study consultants
Utility, grid, and power-system professionals
Energy planners, policymakers, and sustainability professionals
Researchers, academics, graduates, and clean-energy entrepreneurs

Course Modules

Module 1: Fundamentals of Solar Resource Assessment

Solar radiation fundamentals
Solar geometry, sun path, zenith and azimuth angles
Atmospheric effects, aerosols, clouds, humidity and temperature
Solar resource variability across climates and geographical regions
Case Study: Preliminary solar resource assessment for a utility-scale PV site

Module 2: Solar and Meteorological Data Sources

Ground-based pyranometers, pyrheliometers and weather stations
Satellite-derived solar irradiance and cloud products
Reanalysis, Typical Meteorological Year (TMY) and long-term datasets
Data resolution, temporal coverage, metadata and uncertainty
Case Study: Comparing satellite-derived and ground-measured irradiance datasets

Module 3: GIS and Remote Sensing for Solar Resource Mapping

GIS workflows for solar resource mapping and spatial analysis
Satellite imagery and remote-sensing applications
Terrain, elevation, land cover and environmental constraints
Solar-site suitability and spatial decision-support analysis
Case Study: GIS-based identification of high-potential solar PV zones

Module 4: Solar Data Quality Control and Long-Term Resource Assessment

Data cleaning, filtering, outlier detection and missing-data treatment
Sensor calibration, measurement uncertainty and quality assurance
Long-term correction, climatology and interannual variability
P50, P90 and uncertainty concepts for energy-yield assessment
Case Study: Quality-control and long-term correction of a solar measurement campaign

Module 5: PV Energy Yield Assessment and Forecasting Fundamentals

Converting irradiance and weather variables into PV power forecasts
PV performance ratios, losses, temperature effects and system availability
Forecast horizons: nowcasting, intraday, day-ahead and longer-term
Forecast baselines, persistence models and physical approaches
Case Study: Estimating annual PV yield and developing a preliminary power forecast

Module 6: Machine Learning and AI for Solar Forecasting

Data preprocessing and feature engineering for forecasting
Regression, Random Forest, Gradient Boosting and Support Vector Regression
Artificial Neural Networks and LSTM approaches for time-series forecasting
Emerging CNN, Transformer and spatiotemporal deep-learning methods
Case Study: Developing and evaluating an ML-based solar irradiance forecasting workflow

Module 7: Satellite-Based Forecasting, Hybrid Models and Uncertainty

Satellite imagery and cloud-motion/nowcasting techniques
Numerical Weather Prediction (NWP) integration
Hybrid physical-statistical and physics-informed forecasting
Probabilistic forecasting and prediction intervals
Case Study: Satellite-based intraday PV forecasting with hybrid model blending

Module 8: Forecast Validation, Optimization and Industry Applications

Forecast performance using MAE, RMSE, nRMSE, MAPE, bias and skill scores
Model benchmarking, cross-validation and error diagnostics
Forecast uncertainty, reliability and operational decision-making
Applications in grid integration, battery storage, energy trading and asset management
Case Study: Using solar forecasts to support PV plant operations and battery dispatch

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