Time Series Forecasting and Control Systems for Renewable Energy Integration
Dr. Malaquias Peña
Associate Professor, University of Connecticut
Abstract
This one-day intensive course provides a focused introduction to the application of machine learning (ML) techniques for time series forecasting and control systems, with a special emphasis on renewable energy integration. Participants will gain insights into how ML-driven approaches can enhance forecasting accuracy, optimize control strategies, and ultimately improve the performance and reliability of renewable energy systems. Through a combination of lectures, demonstrations, and practical examples, attendees will learn to leverage state-of-the-art predictive models for operational decision-making in power grids, microgrids, and energy storage solutions.
Learning Objectives
By the end of this course, participants will be able to:
- Understand the fundamentals of time series analysis and how they apply to renewable energy systems.
- Identify and compare different machine learning approaches for time series forecasting (e.g., ARIMA, CNN-LSTM) and control (e.g., Reinforcement Learning).
- Implement basic forecasting pipelines using Python-based libraries and evaluate model performance for renewable energy forecasting tasks.
- Discuss the role of predictive control systems in optimizing renewable energy penetration and maintaining grid stability.
- Apply gained knowledge to design or recommend machine learning-based strategies for real-world renewable energy integration scenarios.
Course Description
This mini-course is structured to offer a rapid, yet thorough exploration of machine learning methods tailored for time series forecasting and control systems in the context of renewable energy. Participants will first review fundamental concepts of time series analysis—covering stationarity, seasonality, feature engineering, and data preprocessing techniques.
Building on this foundation, the course delves into state-of-the-art machine learning approaches, including classical models like ARIMA and cutting-edge deep learning architectures such as LSTM and GRU. Attendees will also explore practical considerations when deploying these models—such as handling missing data, hyperparameter tuning, and performance evaluation metrics (MAE, RMSE, MAPE).
The latter part of the day focuses on control systems, specifically how machine learning-based control (including Reinforcement Learning) can be used to dynamically adjust renewable energy generation, storage, and load management. Case studies will illustrate how accurate forecasting and robust control algorithms can be the key drivers of stable, cost-effective renewable energy systems.
Key Topics Covered
- Time series data preprocessing and feature engineering
- Classical vs. neural network forecasting methods
- Model evaluation and hyperparameter tuning
- Reinforcement learning and predictive control in energy systems
- Case studies in renewable energy integration (solar, wind, and battery storage)
Prerequisites
- Basic understanding of linear algebra, calculus, and probability/statistics
- Familiarity with Python and data analysis libraries (NumPy, pandas, scikit-learn, Keras)
- Foundational knowledge of control systems is beneficial but not mandatory
References
- Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. (URL: https://mitpress.mit.edu/9780262035613/deep-learning/)
- Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
- Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice (2nd ed.). OTexts. (URL: https://otexts.com/fpp2/)
- Ahmad, T., Zhang, D., & Yan, B. (2020). A review on renewable energy and electricity requirement forecasting models for smart grid and buildings. Sustainable Cities and Society, 55, 102052.
