Development of a Solar Power Generation Prediction Model Utilizing Photovoltaics and Climate Data

Faced with ongoing resource depletion and climate catastrophes due to global warming, there is an increasing interest in reducing carbon emissions and the need for sustainable renewable energy. To address these issues, the expansion of renewable energy usage is imperative. Among various sources, solar power generation is the most widely used source in renewable energy production and occupies the largest proportion. This study aims to propose a methodology for predicting solar power generation using environmental and power generation data. Considering the characteristics of solar power generation, which are influenced by the continuity of time, a Transformer model specialized in time series data was developed. This model demonstrates performance superiority compared to other existing AI models. The proposed model shows promising results and is expected to be utilized for advanced operation and management of power plants.