## 4 Conclusions and Outlook
}}\)) and [PERSON] et al.’s original model (\(\text{CS}_{2018,\text{origin}}\)) for a second set of measured daylight spectra not included in the training data. The measurement uncertainty for \(\text{CS}_{2018,\text{origin}}\) is of the order of \(\pm 8\%\). Here, the annotation “\((Meas.-Reconstr. )\)” indicates that the given accuracy estimates \(|\Delta\text{CS}_{2018}|\) and colorimetric prediction errors \(\Delta u^{\prime}v^{\prime}\) are determined by comparing the corresponding quantities derived from the performed radiometric measurements (i.e., _“Meas.”_) and the application of [PERSON] et al.’s model formalism to those obtained by processing the sensor readouts for the subsequent application of the CIE daylight reconstruction method (i.e., _“Reconstr.”_) as proposed in this work. The annotation “\((Meas.-Calc. )\)”, on the other hand, again denotes the use of [PERSON] et al.’s approximation method also for daylight spectra. i.e., from \(XYZ\) to \((u^{\prime},2/3v^{\prime})\), a nonlinear optimization method was used to determine the daylights' CCTs, which are the main input in the CIE model to reconstruct their spectral compositions. With known illuminance \(E_{\
u}\), corresponding CS values could then be calculated without further approximations by applying [PERSON] et al. 's original model to the estimated daylight spectra. Compared to [PERSON] et al. 's model when applied directly to the measured light source data, the proposed daylight reconstruction method yielded excellent model performance with maximal prediction errors \(\Delta CS_{\text{max}}\) of less than 0.002 for both known (i.e., part of the training data) and unknown (i.e., not part of the training data) daylight conditions. In addition, it could be shown that for both test cases, the daylight reconstruction approach outperformed [PERSON] et al. 's approximation method in terms of CS prediction accuracy, making the former the appropriate method of choice when dealing with daylight conditions. When comparing the results achieved for both artificial sources and daylight, it becomes clear that there might be still room for improvements regarding the application of the proposed methodology for the estimation of circadian effectiveness for artificial lighting conditions. Whereas for daylight, the reported model prediction errors were more or less of the same small order (see Tables 6 and 7), considerably larger discrepancies and greater errors were observed for their artificial counterparts (see Tables 3 and 4). Instead of using [PERSON] et al. 's approximation method, it could therefore be expedient to also implement a spectral reconstruction strategy for artificial light sources so that [PERSON] et al. 's original model can be applied without any further approximations. The first approaches for multi-channel sensors found in the literature using a multilayer perceptron seem to be quite promising for this purpose and report reconstructed SPD errors of less than 2% [134,135]. However, it remains questionable whether such a high accuracy can also be achieved with a simple RGB color sensor. Of course, the methodology proposed in this work could easily be adapted to be used in conjunction with a multi-channel sensor instead, which potentially would increase spectral reconstruction accuracy. However, additional experiments will be required to draw final conclusions. For daylight conditions, on the other hand, the procedure described in this work so far dealt with outdoor measurements only. Nonetheless, with regard to the establishment of human-centered lighting control strategies, the accuracy of the proposed methodology must still be confirmed for daylight, which is measured indoors behind windows that are made of glass or other materials.