PV Generation Forecast

Chapter	Details
Partner	УКЛО-ТФБ
Title	PV Generation Forecast
Service	Course
Target Group	Utility scale PV plants, Prosumers
Format	In-Person Training, Webinar
Focused on Key Technologies	AI, Machine Learning, Conformal Prediction
Status	Ready to offer
Stakeholders from SME/PA Side	Utility Companies and Investors in RES
Requirements for Participation	Basic knowledge of PV technologies
Estimated Duration	Multi-day

Introduction

With the rapid growth of photovoltaic (PV) installations, accurate forecasting of solar energy production has become essential for efficient grid operation, market participation, and energy planning. This course provides an in-depth understanding of PV generation forecasting using Artificial Intelligence (AI) and Machine Learning (ML). It equips participants with cutting-edge skills to develop reliable and interpretable forecasting models.

Technical Context and Examples

PV generation is inherently variable due to its dependence on weather conditions. This variability poses challenges for power system operators, energy traders, and micro-grid planners. To address this, AI and ML techniques are being increasingly applied to forecast PV output across different time horizons. The course dives into:

Feature Engineering for PV Forecasting: Integration of key inputs such as temperature, irradiance, time-based features (hour, day, season), and system characteristics to enhance model performance.
AI and Machine Learning for Forecasting: Application of data-driven methods like Decision Trees, Random Forests, and Neural Networks to capture complex, nonlinear dependencies in PV output.
Comparison with Traditional Methods: Evaluation of how machine learning models outperform classical statistical techniques in accuracy, robustness, and adaptability.
Forecasting PV generation: Emphasis on day-ahead forecasting scenarios to support grid operation, energy trading, and planning for large solar installations.
Hands-On Practice with Real Data: Practical exercises using Python and real-world datasets to build, train, and evaluate forecasting models ready for deployment.

Through the use of real datasets, tools like Python, and hands-on exercises, students will simulate forecasting pipelines and test them under realistic conditions.

Detailed Explanation of Core Concepts

The course begins with feature engineering for PV forecasting, focusing on extracting time-based features (hour, day, season), integrating weather data (temperature, irradiance), and incorporating system metadata. These inputs are essential for improving model accuracy and reliability. Participants then explore AI and machine learning techniques—such as Decision Trees, Random Forests, and Neural Networks—that effectively model nonlinear relationships in PV performance. The course also compares these approaches with traditional statistical models, highlighting the advantages of modern ML methods in terms of accuracy, adaptability, and robustness. A key focus is placed on day-ahead forecasting for PV generation, preparing participants for real-world grid integration challenges. Hands-on exercises using Python, including tools like Pandas, NumPy, Plotly, Matplotlib, Scikit-learn, and PyTorch, allow participants to build and evaluate end-to-end forecasting pipelines with real datasets.

Tentative Agenda of the Course

Part 1: Introduction to PV Generation Forecasting

Basics of PV systems
Intermittency and uncertainty of PV generation
Challenges in PV generation forecasting (weather, PV metadata)
Traditional methods vs. AI/ML-based approaches

Part 2: Regression Methods for PV Generation Forecasting

Linear and Polynomial Regression– Capturing trends and non-linearity
Sinusoidal Regression– Modeling periodic patterns in PV generation
Wavelet Regression– Handling multi-scale patterns in PV generation

Part 3: Machine Learning and Artificial Intelligence Concepts

Supervised vs. Unsupervised Learning– Key differences and applications
Training, Validation, and Testing
Identifying important predictors for forecasting
Overfitting & Underfitting

Part 4: Machine Learning and Artificial Intelligence Algorithms

k-Nearest Neighbors, Neural Network Regression, Decision Trees & Random Forests

Part 5: Measurement of Error with Different Metrics

Mean Absolute Error (MAE)
Mean Squared Error (MSE), Root Mean Squared Error (RMSE)
Comparing models using cross-validation and performance benchmarks

Part 6: Case Studies in PV Generation Forecasting

Utility-scale and roof-top PV generation forecasting scenarios and study cases

Conclusion

This training combines advanced AI techniques with practical forecasting scenarios to empower participants with the tools needed for real-world PV generation forecasting. By course end, learners will be equipped to build, evaluate, and deploy AI-based forecasting models. The hands-on sessions and guided implementation make this course a valuable experience for energy professionals, researchers, and data scientists alike.

Additional Course Information

Category	Details
Developed skills	Participants will acquire knowledge and skills, including:
Developed skills	Skill 1: Preparing and processing real-world PV and meteorological datasets Skill 2: Implementing supervised machine learning models for time-series prediction Skill 3: Evaluating and validating forecasting models using practical metrics and visualization techniques
Learning Methods Used	Lectures
References/Resources	Python: https://docs.python.org/3/ Emmanuel Ameisen, “Buildong machine Learning Powered Appliacations”, O’Reilly Media, Inc. 2022 Gilbert Masters, “Renewable and Efficient Electric Power Systems”, Kohn Wiley & Sons, 2004
Overview Slides	G. Veljanovski, M. Atanasovski, M. Kostov and P. Popovski, “Application of Neural Networks for Short Term Load Forecasting in Power System of North Macedonia,” IEEE 55th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Niš, Serbia, 2020, pp. 99-101, doi: 10.1109/ICEST49890.2020.9232674. P. Popovski, M. Kostov, M. Atanasovski and G. Veljanovski, “Power Load Forecast for North Macedonia Using Machine Learning,” IEEE 55th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Niš, Serbia, 2020, pp. 106-109, doi: 10.1109/ICEST49890.2020.9232837.