Integrating Machine Learning Models into a Real-Time Energy Forecasting System - Master’s thesis

Aug 5, 2026

Upload by Oliver

Energy markets face viability, fluctuations, and high complexity driven by renewable resources, making accurate real-time forecasting essential for effective planning and decision-making. This thesis develops a real-time energy forecasting system integrating advanced machine learning and deep learning models, including Bidirectional Long Short-Term Memory (BiLSTM) and Autoencoder Convolutional LSTM (AE-CLSTM). The solution emphasizes scalability, maintainability, adaptability, and availability for large volatile datasets and dynamic market conditions. Results demonstrate improved prediction quality compared with traditional approaches, supporting more stable and sustainable energy-market operations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages59

Reading Time2 h 29 min

AccessFree to download

Abstract

Acknowledgments

Abbreviations

Introduction

Literature Review

Traditional Forecasting Methods

Emergence of Advanced Techniques

Challenges in Energy Forecasting Software and Systems

Dynamic Behavior of Energy Markets

Data Volume and Complexity

INTEGRATING MACHINE LEARNING MODELS INTO A REAL-TIME ENERGY FORECASTING SYSTEM Lappeenranta–Lahti University of Technology LUT Master’s Programme in Software Engineering and Digital Transformation, Master’s thesis Amin Hassanzadehmoghaddam Examiners: Professor Kari (Tech.) Smolander Behnam M. Ivatloo, Professor. (Tech.) ABSTRACT Lappeenranta–Lahti University of Technology LUTLUT School of Engineering Science Master’s Program in Software Engineering Amin Hassanzadehmoghaddam Integrating Machine Learning Models into A Real-Time Energy Forecasting System Master’s thesis 2024 59 pages, 16 figures, 6 tables Examiners: Professor Kari Smolander and Behnam M. Ivatloo, Professor. (Electrical Engineering) Keywords: Scalability, Machine Learning Integration, Containerization, Real-Time Data Processing. Energy markets experience viability, fluctuations, and complexity because of their resources, which are renewable energy sources. This enables the challenges that stakeholders face to make informed and effective decisions and planning for the market that can affect consumer behavior and other industries as well. A critical problem in this case is having accurate and real-time forecasting to address the challenges. To solve this problem, this thesis develops a real-time energy forecasting system that contains advanced machine learning and deep learning models, Bidirectional Long Short-Term Memory (BiLSTM), and Autoencoder Convolutional LSTM (AE-CLSTM) . The proposed solution considers scalability, maintainability, adaptability, and availability for handling large, volatile datasets and dynamic market conditions. Results illustrate the system’s effectiveness by showing the improvements in prediction and comparing them to the traditional models. These findings highlight the potential of the proposed system to enhance decision-making and planning in energy markets, ultimately contributing to their stability and sustainability. ACKNOWLEDGEMENTS I am deeply grateful to a multitude of individuals whose support and guidance have made this thesis possible. First and foremost, I would like to express my profound appreciation to Professor Kari Smolander for his unwavering guidance, invaluable feedback, and continued encouragement throughout my research. Your expertise, patience, and mentorship have been instrumental in shaping this work, and I am truly privileged to have been under your tutelage. I would also like to extend my heartfelt gratitude to my second supervisor, Behnam Ivatloo, for his unwavering support, motivation, insightful critiques, and remarkable patience throughout this challenging journey. His steadfast guidance and compassionate assistance have been invaluable in navigating this arduous path. I will perpetually hold dear the selfless contributions and the kind-hearted encouragement that he has bestowed upon me, as these have undoubtedly rendered this challenging endeavor more manageable for my journey. I want to express my heartfelt thanks to my wife and parents for their unwavering love and support throughout my life. Your encouragement, guidance, and sacrifices have been instrumental in shaping the person I am today. I am eternally grateful for all that you have done for me, and I hope to make you proud of my accomplishments. I hold an immense affection for you both that surpasses my ability to convey in words. I would like to acknowledge my friends and family, whose unwavering love and support have been my anchor throughout this journey. A special thank you to my wife for your endless patience, understanding, and love. Your presence has made this journey not only bearable but also enjoyable. Lastly, I would like to dedicate this endeavor to all those who share the passion for preserving our planet from all kinds of pollution and striving to make it a better place to live. Furthermore, to those who persevere in pushing the boundaries of knowledge, seeking a more comprehensive comprehensio

FAQs

Which machine learning and deep learning models are integrated in the real-time energy forecasting system?

The system integrates Bidirectional Long Short-Term Memory (BiLSTM) and Autoencoder Convolutional LSTM (AE-CLSTM). It uses these models to improve real-time prediction quality.

What main software/system qualities does the proposed solution emphasize?

The solution focuses on scalability, maintainability, adaptability, and availability to handle large, volatile datasets and dynamic market conditions.

How does the thesis validate the system’s effectiveness?

It compares prediction results against traditional forecasting models and shows improvements in forecasting performance.

Aug 5, 2026

Upload by Oliver