Abstract 

This thesis presents the development of a Python-based analysis tool designed to support ISO 9806:2017-compliant performance evaluation of solar thermal and hybrid photovoltaic-thermal (PVT) collectors. Motivated by workflow inefficiencies and error-prone manual processing in the Hus 45 solar laboratory at the University of Gävle, the tool automates data cleaning, time alignment, dataset merging, and preparation of regression-ready outputs for both steady-state and quasi-dynamic testing. Implemented with a modular architecture in Python, it integrates scientific libraries for solar geometry calculation, thermal parameter derivation, and regression modelling, while providing a user-friendly graphical interface for non-programmers. The development of the Python code was done with experimental datasets from the HiG Test Rig, and demonstrated the tool’s ability to produce accurate and reproducible thermal performance coefficients, with results aligning closely to those reported in the literature for comparable collector technologies. The system enhances throughput, minimizes human error, and facilitates certification, research, and educational applications. Future enhancements could integrate advanced regression models, spectral analysis, or machine learning for predictive performance modelling.