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 Evaluating the Impact of Weather Factors on Solar Power Generation in Da Nang City, Vietnam
Tác giả hoặc Nhóm tác giả: Huy Vu Tran, Dinh Duong Le, Kim Anh Nguyen, and Nhi Thi Ai Nguyen
Nơi đăng: GMSARN International Journal; Số: 19(2);Từ->đến trang: 378-388;Năm: 2024
Lĩnh vực: Khoa học công nghệ; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
The study focuses on evaluating the impact of weather factors such as temperature, humidity, wind speed and cloud cover on the output of solar power systems in Da Nang city, Vietnam. Weather and solar power generation data from various sources are collected and analyzed. Correlation and regression analysis methods are used to determine the relationship between weather variables and solar power generation. Among the weather factors, temperature and cloud cover are the factors that have the most influence on solar power generation. The results obtained provide important information for the effective operation of solar power systems in particular and the power grid in general. Moreover, such information will support the scheduling of mobilization of solar power for energy management based on information about weather conditions, ensuring energy security. This study also provides an important database for applying modern technologies, such as AI and machine learning, for developing solar energy management and forecasting models.
ABSTRACT
The study focuses on evaluating the impact of weather factors such as temperature, humidity, wind speed and cloud cover on the output of solar power systems in Da Nang city, Vietnam. Weather and solar power generation data from various sources are collected and analyzed. Correlation and regression analysis methods are used to determine the relationship between weather variables and solar power generation. Among the weather factors, temperature and cloud cover are the factors that have the most influence on solar power generation. The results obtained provide important information for the effective operation of solar power systems in particular and the power grid in general. Moreover, such information will support the scheduling of mobilization of solar power for energy management based on information about weather conditions, ensuring energy security. This study also provides an important database for applying modern technologies, such as AI and machine learning, for developing solar energy management and forecasting models.
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