Electric Load Forecasting

Chapter	Details
Partner	UKLO-FTSB
Title	Electric Load Forecasting
Service	Course
Target Group	System Operators, Utility Companies, Energy Traders, Large Consumers, Prosumers, Researchers
Format	In-Person Training, Webinar
Focused on Key Technologies	AI, Machine Learning, Time-Series Analysis
Status	Ready to offer
Stakeholders from SME/PA Side	DSOs, TSOs, Retailers, Smart Grid Operators, Prosumers
Requirements for Participation	Basic understanding of power systems and data handling
Estimated Duration	Multi-day

Introduction

Accurate electricity demand forecasting is key to efficient power system operation and planning. As systems grow more dynamic, AI/ML offer powerful tools to capture complex patterns. This course offers hands-on training in modern AI/ML techniques for electric load forecasting, preparing participants to build and evaluate robust models for real-world use.

Technical Context and Examples

Load forecasting is a critical function in modern power systems, enabling optimal scheduling, market participation, and real-time grid management. As consumption patterns become more volatile due to decentralized energy sources and changing user behavior, accurate short-term forecasts are essential. This course covers:

Data Pre-processing and Feature Engineering: Timestamps, temperature and weather alignment, holiday effects, lag features, and trend/seasonality detection.
Model Evaluation and Deployment: Time-series cross-validation, and metrics like MAPE, RMSE, and MAE for rigorous performance tracking.
AI/ML Techniques: From Decision Trees and Random Forests to Neural Networks and k-NN, with a focus on capturing nonlinearities and temporal dependencies.
Applied Scenarios: Hands-on exercises using Python and real datasets ensure practical, deployment-ready skills.

Detailed Explanation of Core Concepts

The course begins with essential data handling techniques, focusing on temporal feature extraction (hour of day, day of week, month, season), treatment of missing data and outliers, and the integration of exogenous variables such as temperature, public holidays, and human mobility data. These preprocessing steps are critical for uncovering hidden patterns and improving model performance.

Participants then explore AI and machine learning approaches for load forecasting, including supervised learning for time-series problems, modeling consumption trends from historical data, and applying methods to reduce overfitting and ensure strong model generalization. Emphasis is placed on understanding model behavior and adapting it to diverse load types.

Hands-on practice includes the use of Pandas and NumPy for data manipulation, Plotly and Matplotlib for advanced visualization and error analysis, and model building using Scikit-learn and PyTorch within the Python ecosystem.

Tentative Agenda of the Course

Part 1: Introduction to Power Load Forecasting

Basics of power load and its importance in energy systems
Types of power load forecasting: Short-term, Medium-term, Long-term
Challenges in load forecasting (seasonality, demand fluctuation, external factors)
Traditional methods vs. AI/ML-based approaches

Part 2: Regression Methods for Load Forecasting

Linear and Polynomial Regression – Capturing trends and non-linearity
Sinusoidal Regression – Modeling periodic patterns in load demand
Wavelet Regression – Handling multi-scale patterns in power consumption

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 Power Load Forecasting

Residential and commercial forecasting scenarios and study cases

Conclusion

This course enables energy professionals to harness AI and ML for accurate load forecasting, ensuring efficient operation, cost savings, and better energy management. Through real-world data and project-based learning, participants gain practical, future-proof skills in intelligent energy forecasting.

Additional Course Information

Category	Details
Developed skills	Participants will acquire knowledge and skills, including:
Developed skills	Skill 1: Handling and preparing real-world load and weather datasets Skill 2: Building supervised ML and deep learning models for time-series data Skill 3: Evaluating and validating forecasting performance using practical metrics Skill 4: Visualizing time-series data and model performance using Python plotting libraries Skill 5: Deploying forecasting models in realistic scenarios for different loads
Learning Methods Used	Lectures
References/Resources	Python: https://docs.python.org/3/ Emmanuel Ameisen, “Building machine Learning Powered Applications”, 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.