## 1 Introduction  
Worldwide, solar power is being incorporated into power grids to replace highly polluting power generation methods such as burning fossil fuels. While this is sure to reduce damage to the earth's environment and aid in our quest for sustainable development, solar power is inherently uncontrollable and intermittent. It relies on hours of sunlight, the intensity of which is subject to natural phenomena such as cloud cover and rainfall. Thus, incorporating solar power into the power grids of large regions can cause unstable grid frequencies, severe power surges, and even power grid failures. To overcome this issue, power grid management teams mostly use solar power forecasting technologies based on weather forecasting data to predict the power output at the next time point. The output provided by solar power is then stabilized using other types of power such as thermal power or hydropower plants [1; 2]. This means solar power forecasting is a key technology for countries developing green energy, and as such, it has been widely studied. Researchers have utilized physical, statistical, and machine learning models to study solar power forecasting. Physical models consider weather variables such as illuminance, relative humidity, wind speed, and wind direction [3; 4; 5]. Over the long term, this approach proves accurate when weather conditions are stable, but prediction accuracy declines greatly when weather factors fluctuate. Many researchers therefore suggested replacing physical models with statistical methods because the latter can identify patterns in large amounts of historical data and provide accurate prediction results even when weather conditions vary [6; 7; 8]. In recent years, however, researchers pointed out that for most nonlinear prediction problems such as solar power forecasting, machine learning methods can offer more accurate results. Subsequently, machine learning methods became the mainstream in solar power forecasting research; for instance, decision trees [9; 10], random forests [11; 12], and neural networks [13; 14; 15] were all demonstrated to be effective on the target problem. The most popular models were deep learning models [16; 17; 18], because compared to other machine learning methods, their massive network architectures can better learn the subtleties of the nonlinear factors in the target problem to provide outstanding prediction results. On the whole, the advent of deep learning models has enabled solar power forecasting to become highly accurate. Thus, the focus has shifted from increasing accuracy to overcoming difficulties encountered in practice. In studies using machine learning models to predict solar power generation, various sensors such as light meters, rain gauges, and hygrometers are first set up around the solar power plant, and then, various models are established to investigate the relationship between sensor data and power output. However, in real-world environments, the prediction values of this approach can be inaccurate due to problems with the sensors. Sensors consist of electronic components; thus, in addition to the aging of the components themselves causing measurements to gradually lose accuracy, their operation in extreme environments with high sun exposure, high temperatures, heavy rainfall, or humid air can easily damage components and affect the accuracy of their measurement results. For the solar power plants discussed in this study, it is necessary to install sensors outdoors to collect environmental data, so sensor wear is likely to be more severe than that in other applications, increasing the possibility of inaccuracy. Furthermore, sensor wear occurs irregularly, and power plants do not have enough personnel to closely monitor and replace sensors. Therefore, ensuring that machine learning models can output accurate prediction results despite inaccurate sensor values has become a crucial issue.