Enhanced Energy Forecasting for Virtual Power Plants - Leveraging Machine Learning for Improved Efficiency

Aug 3, 2026

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The research examines the intersection of evolving energy resources and machine learning to enhance energy forecasting for Virtual Power Plants (VPPs). The literature review summarizes existing forecasting approaches and sets up deeper analysis of data analytics challenges in a biogas-powered electricity system. Electricity consumption and production patterns are studied alongside changes in weather conditions and spot electricity prices. Key focus areas include data collection, preprocessing, and the role of statistical and machine learning models in improving forecasting accuracy. Results support data-driven solutions that reduce dependence on grid electricity, increase biogas usage, and lower overall energy costs through intelligent automation balancing efficiency and cost-effectiveness.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages25

Reading Time1 h 3 min

AccessFree to download

1 Introduction

2 Virtual Power Plant

3 Methodology

3.1 Data Collection Process

3.2 Data Preprocessing

3.3 Predictive Modelling

3.4 Model Evaluation and Optimization

4 Implementation and Results

4.1 Data Analysis Insights

4.2 Model Implementation

4.3 Performance Evaluation

5 Discussion

5.1 Practical Implications of Energy Management

5.2 Challenges and Limitations

5.3 Ethical Considerations

6 Conclusions

References

Saurav Amatya ENHANCED ENERGY FORECASTING FOR VIRTUAL POWERPLANTS Leveraging Machine Learning for Improved Efficiency CENTRIA UNIVERSITY OF APPLIED SCIENCES Bachelor of Engineering, Information Technology May 2025 ABSTRACT | Centria University
of Applied Sciences | Date
May 2025 | Author
Saurav Amatya | | --- | --- | --- | | Degree programme
Bachelor of Engineering, Information Technology | | | | Name of thesis
ENHANCED ENERGY FORECASTING FOR VIRTUAL POWER PLANTS. Leveraging Machine Learning for Improved Efficiency | | | | Centria supervisor
Panu Wirkkala | | Pages
21+3 | | Instructor representing commissioning institution or company
Fabian Sander | | | | The research examines the changing convergence of energy resources and machine learning with particular focus on enhancing energy forecasting for Virtual Power Plants (VPPs) . This literature review discusses through the existing solutions regarding energy forecasting and paves the way for extensive examination of data analytics challenges for a biogas-powered electricity system. The study investigates electricity consumption and production patterns with regards to the changes in weather conditions and spot electricity prices. The study highlights the challenges related to data collection and preprocessing which in turn is the foundation of predictive forecasting modelling. This paper outlines practical solutions that highlights the importance of various statistical and machine learning models that improve forecasting accuracy. The findings provide the potential of data-driven solutions in minimizing reliance on grid electricity, maximizing biogas electricity usage and reducing overall energy costs. The analysis concludes by advocating for intelligent, automated solutions that balance energy consumption and cost-effectiveness in VPPs. | | | Key words Biogas electricity, data-driven solutions, energy forecasting, machine learning, virtual power plants CONCEPT DEFINITIONS DER (Distributed Energy Resources) is small-scale energy resources usually situated near sites of electricity use. EDA (Exploratory Data Analysis) is a method of analysing the data to comprehend its main characteristics. IOT (Internet of Things) is a network of interconnected devices that communicate with each other over the internet. API (Application Programming Interface) is set of protocols and tools that allow different software application to communicate with each other. LSTM (Long Short-Term Memory) is a type of neural network that can remember past data, used in time-series analysis. XGBoost (Extreme Gradient Boosting) is a machine learning method that makes predictions by combining multiple models. ABSTRACT CONCEPT DEFINITIONS CONTENTS 1 INTRODUCTION................................................................................................................................1 2 VIRTUAL POWER PLANT ..............................................................................................................2 3 METHODOLOGY ..............................................................................................................................4 3.1 Data Collection Process .................................................................................................................4 3.2 Data Preprocessing.........................................................................................................................5 3.3 Predictive Modelling ......................................................................................................................6 3.4 Model Evaluation and Optimization ............................................................................................6 4 IMPLEMENTATION AND RESULTS ............................................................................................8 4.1 Data Analysis Insights.............................................................

FAQs

What is the main objective of the thesis for virtual power plants?

To enhance energy forecasting for Virtual Power Plants by applying statistical and machine learning methods to improve prediction accuracy and support efficient energy management.

Which factors are analyzed to forecast electricity consumption and production?

The study examines electricity consumption and production patterns in relation to weather condition changes and spot electricity prices.

Why are data collection and preprocessing emphasized in the forecasting approach?

Because reliable forecasting models depend on quality data; collection and preprocessing form the foundation for predictive modelling in the biogas-powered electricity system.

Aug 3, 2026

Upload by Sage