Nghiên cứu sử dụng mô hình học máy tăng cường độ dốc vào dự đoán năng lượng pin mặt trời sử dụng công nghệ quang điện

Abstract

The increasing global energy demand, along with the need for clean and sustainable energy sources, has led to a significant increase in solar power projects worldwide in general and Vietnam in particular. In Vietnam, technology and the ability to develop solar power projects are still heavily dependent on foreign countries, leading to large-scale solar panel power deployment facing many difficulties, especially about price. This makes it difficult for solar power to compete with other traditional power sources. However, the evaluation and design of solar panel power using Solar photovoltaic technology in Vietnam still has many limitations, mainly due to foreign consulting units. It would be extremely meaningful if we could make a preliminary assessment of the solar cell energy source. The use of ML machine learning in solar panel power forecasting has attracted significant attention in recent years, with several studies demonstrating the potential of ML-based models to improve accuracy and reliability of solar forecasts. Therefore, this study will research and present a specific application of a modern machine learning model, Gradient Boosting, in predicting solar cell energy from environmental temperature, heat radiation and solar cell temperature