# Predicting the Performance of Solar Power Generation Using Deep Learning Methods
###### Abstract  
In recent years, many countries have provided promotion policies related to renewable energy in order to take advantage of the environmental factors of sufficient sunlight. However, the application of solar energy in the power grid also has disadvantages. The most obvious is the variability of power output, which will put pressure on the system. As more grid reserves are needed to compensate for fluctuations in power output, the variable nature of solar power may hinder further deployment. Besides, one of the main issues surrounding solar energy is the variability and unpredictability of sunlight. If it is cloudy or covered by clouds during the day, the photovoltaic cell cannot produce satisfactory electricity. How to collect relevant factors (variables) and data to make predictions so that the solar system can increase the power generation of solar power plants is an important topic that every solar supplier is constantly thinking about. The view is taken, therefore, in this work, we utilized the historical monitoring data collected by the ground-connected solar power plants to predict the power generation, using daily characteristics (24 h) to replace the usual seasonal characteristics (365 days) as the experimental basis. Further, we implemented daily numerical prediction of the whole-point power generation. The preliminary experimental evaluations demonstrate that our developed method is sensible, allowing for exploring the performance of solar power prediction. solar energy; deep learning; power prediction; statistical analysis +
Footnote †: journal: _Sci._  
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How to collect relevant data to make predictions so that the solar system can increase the power generation of solar power plants is an important topic that every solar supplier is constantly thinking about. All sources of solar power are sensitive to weather and environmental changes, but the existing literature mainly focuses on photovoltaic (PV) power generation and changes in solar radiation because it is the most relevant source [4]. For instance, the factor of global temperature will influence the efficiency and power output of the cell [5; 6; 7; 8; 9; 10]. For every \(1\,\mathrm{\SIUnitSymbolCelsius}\) increase in temperature, the efficiency of photovoltaic (PV) modules decreases by about 0.5% [11]. Temperature changes will affect the capacity of underground conductors and soil temperature [12]. Also, the temperature may influence operating costs and affect equipment efficiency [13]. For solar energy, cloud cover, which directly affects the surface downwelling shortwave radiation, is the most important climate factor to be considered. Solar irradiation and cloudiness would affect solar power output [14; 10; 15; 16; 17; 18; 19; 20]. Factors including dirt, dust, snow, atmospheric particles, and others will also influence energy output [7; 10; 14; 19; 21; 22; 23; 24]. In addition, wind speed also affects photovoltaic production [15; 16]. In summary, for the above factors (i.e., climate, haze, environmental dust, bird droppings, etc. ), long-term data tracking can be used to effectively determine the rate of performance degradation. It is one of the key observation items for the daily maintenance of solar energy systems. If we can quickly and accurately determine the cause of system abnormalities, and make immediate judgments for abnormal situations, it will increase the power plant bulk sales benefits. A 25% improvement of the accuracy in solar panel prediction could reduce 1.56% of the generation cost, which about US$ 46.5 million [25]. Therefore, solar power generation prediction is an important grid integration in the solar management system [26; 27]. The problem with solar power generation is that the amount of power generation is not easy to predict in advance and will vary according to weather conditions and site conditions at specific locations.