Deep Learning-Based Approach for Short-Term Solar Power Forecasting

In recent years, there has been a significant increase in the importance of solar energy as a clean and renewable energy source. Solar energy has become a clean, widely available, and eco-friendly option in the field of electricity generation. Solar power generation is not constrained by the negative effects of global warming and pollution, in contrast to traditional energy sources. The intermittent and erratic nature of solar energy, however, presents a serious obstacle. Accurate forecasting is crucial for fully using the potential of solar energy since it is closely linked to different weather conditions. Forecasting energy generation is crucial for smart grid operators and solar electricity suppliers since it is necessary to assure power continuity to dispatch and store the energy appropriately. In this study, we present a system for predicting solar power production 36 h in advance at the Yeongam solar power facility in South Jeolla Province, South Korea. The results demonstrate that the suggested method of utilizing several deep neural networks is superior to existing machine learning forecasting models.