Abstract
The amount of new renewable energy generation is increasing worldwide every year. Among many new renewable energy sources, solar energy generation using solar energy accounts for the highest proportion of new renewable energy generation. There is a variation in power production because solar power generation is more affected by climate conditions compared to power generation using crude oil or oil. In order to accurately predict solar energy generation dependent on climate variables, this study compares the performance of machine learning-based solar power generation prediction models using weather forecast data from the current forecast technology, Numeric Weather Prediction (NWP). In this study, we experimented on two NWP types, and 7 machine learning models depending on 21 photovoltaic(pv) power stations. Based on results, we select the model with the lowest statistical indicators nMAE(%) by region as the optimal model for the region. Finally, experimental results show that the 7-Block ANN model devised in this study is better than conventional machine learning models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of the Korean Institute of Industrial Engineers
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
