## 2 Review and Evaluation of Solar Irradiance Forecasting Sensors
### Persistence Model and Error Metrics  
The forecasting field commonly employs a benchmark for comparison based on the assumption that future conditions will remain unchanged over the forecast horizon. This assumption is known as the \"persistence\" model and serves as the basis for evaluating the performance of other forecasting methods. For instance, in solar irradiance forecasting, the persistence model assumes consistent future values for irradiance, power output, and the clear sky index (CSI/clearness index). To compare two different models, such as the \"persistence\" model and the one studied, the forecast skill score, or \(SS\), is calculated. The forecast skill score (\(SS\)) is a valuable metric for comparing the performance of two forecasting methods. \[SS=\left(1-\frac{\varepsilon_{prediction}}{\varepsilon_{persistence}}\right) \times 100\% \tag{1}\]  
Therefore, to indicate the improvement or deterioration of their results compared to persistence or baseline models, many researchers utilize the \(SS\) to guide their conclusions. Nonetheless, caution should be exercised when comparing \(SS\) values between studies due to the large number of baseline models. Comparisons can only be made meaningfully when the persistence models agree on one of the persistence variables mentioned above. To this end, it is worth noting a list of commonly used persistence models. One such model is the \"naive persistence\" model, which assumes that the future forecast will be identical to the last measured value. This model is particularly suitable for stationary time series with minor variations, such as when the expected weather conditions are like those of the previous day or when the forecast for the next hour is expected to be the same as the current measured value. Given these characteristics, such models are usually most appropriate for situations where little variation in observed patterns is anticipated. For example, the persistence model used in [3], as reported in [4], is calculated using a clear sky model. On the other hand, the persistence model proposed by [5] predicts that the solar energy will be equal to the last observation corrected by the clear sky index. The authors of [6], on the other hand, employs a clear sky index model based on extraterrestrial solar radiation. Meanwhile, Ref. [7] utilizes a two-day persistence model, where the forecast power output at each hour is the value of the power generated at the same hour two days earlier. Given the non-stationary nature of solar irradiance time series, the naive persistence approach is often inadequate for intervals longer than one hour. To address this limitation, a new approach has emerged, which is applicable not only for longer time horizons but also for hourly intervals. Referred to as the \"smart persistence\" model, this method is reliable in situations where there is low variability and for short-term forecasting. It involves breaking down the solar power output into a stationary and a stochastic component. The stationary component is usually assigned to the clear sky index. The stochastic component is attributed to cloud-induced variations. The method was first introduced by [8], where the stochastic component was incorporated into the power output. Since then, several authors have modified and implemented the model in different ways. For instance, Ref. [9] added a solar power index that depends on cloud cover, while [10] proposed a cloud speed persistence and ramp model to forecast power output and ramp events using endogenous measurements of cloud motion vectors. Similarly, Ref. [11] applied the smart persistence model, calculating the predicted photovoltaic (PV) power output as a function of the clear sky GHI irradiance derived from a Python package for solar power analysis. The method was also used by [12] when comparing their SolarNet model with the same persistence model based on a cloud cover index that they used in their previous research [13].