Browsing by Author "Alaraasakka, Rosa-Maria"
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Insinööritieteiden korkeakoulu | Bachelor's thesis(2021-03-28) Alaraasakka, Rosa-Maria - Predictive Machine Learning Modeling for Short Term Flexible Load Quantification in Residential Building Thermal Mass: Towards access to flexibility markets
Perustieteiden korkeakoulu | Master's thesis(2024-06-17) Alaraasakka, Rosa-MariaGrid balancing is increasingly challenging due to rising consumption and variable renewable energy production. Flexibility markets are being developed to harness unused flexible potential, exposing the need for flexibility quantification. This thesis examined existing machine learning models' goals and feature selection for flexibility quantification. A novel short-term total flexible load prediction model was developed to ease flexibility market participation. It was found that most existing models focus on building control optimization, revealing a gap in price-independent models. Additionally, a distinction is needed between total flexibility enabled by building thermal mass and relative flexibility enabled by the optimized system, determined by the feature selection. A market-price independent model was developed with a scalable approach. The model was trained with data from a simulated building that included upward flexible events, using a single heating device utilizing the building's thermal mass. The model operated in three phases: In the first phase, recursive machine learning modelling was used to capture the dynamic thermal behaviour of the residential building. The indoor temperatures were computed in the second phase based on the model predictions. Finally, the model computed the total flexible load in kWh within specified indoor temperature limits. Linear-, Ridge-, Lasso Regression and Random Forest were compared. A combination model of Ridge- and Lasso Regression was the best-performing model with excellent accuracy, with MAE of 0.1 C, ranging between 0.01 C and 0.4 C. Flexibility within 21 C to 23.5 C limits was 15.84 kWh on average. It was found that conditions affect flexibility quantity significantly. Results were not verified in the real world. The thesis extends the understanding of data-driven flexible load quantification in residential buildings and aids in developing scalable tools for flexibility markets. Future research should focus on validating the model in real-world settings and expanding modelling to several heating devices.