Noise Reduction and Precision Enhancement: Data Smooting Techniques for Dye-Sensitized Solar Cell Measurement Data

The accuracy of measurement data plays an important role in the solar cell technology development. This is especially crucial in case of the examination of small-sized solar cells, where the results from the measurement can be noisy. These noises can be resulted from random fluctuation occurring during measurement process or from the measuring instruments. The main goal is to design and conduct experiment to investigate the electrical parameters of dye-sensitized solar cell, as well as to collect, process and evaluate data. Smoothing analysis have been applied on the experimental data to filter out random fluctuations which are moving average, weighted moving average and locally estimated scatterplot smoothing. The effectiveness of each smoothing techniques has been assessed by using key metric such as the mean absolute error and the curve fitting quality. The results demonstrated that the locally estimated scatterplot smoothing method improved the highest in both the mean absolute error and the curve fitting quality, suggesting that this method is the most effective. These findings are supported by table data and visual evidence.