The TUDelft spectrally resolved albedometer is a measurement device whose design began in 2019 and aims to accurately reconstruct spectral irradiance and spectral albedo using reconstruction techniques which enable this device to be produced at a lower cost than its competitors.
The measurement of spectral albedo using an albedometer device is a crucial component in predicting energy yield for bifacial PV panels, expected to become the dominant photovoltaic technology by market share in 2030 [13]. Building on the work of previous thesis projects at TUDelft, this thesis focuses on the following three topics of improvement:

Accuracy improvement in spectral reconstruction: The albedometer device is recalibrated according to the recently recalibrated EKO device, succeeding in reducing the error uncertainty in the first and last wavelength ranges of the spectral irradiance reconstruction. Whilst the average errors lie outside of acceptable uncertainty bounds, the systematic errors in the first and last wavelength bands are identified and proposed solutions involve adjusting the PSO algorithm and using machine learning to improve the prediction of atmospheric absorption parameters such as total precipitable water.
Spectral albedo reconstruction using Machine Learning: Machine learning techniques are employed to reconstruct down-facing spectral irradiance, achieving errors below ±5% for various sky classes and demonstrating the method’s potential for spectral albedo reconstruction in future work.
Improving device usability: The Albedometer App centralizes and automates data processing code, simplifying spectral irradiance and albedo reconstruction processes and greatly enhancing user experience.

This research is key for the development for the albedometer’s accuracy, functionality and usability. By integrating the model for spectral albedo reconstruction this thesis advances the overall development of the albedometer device, bring it one step closer to realising its full potential as a high accuracy, low cost measurement device, making it a valuable tool for spectral albedo reconstructions and precise energy yield predictions in the bifacial PV sector.