AI-based solar PV power forecasting
As a kind of renewable energy technology, photovoltaic power generation has been more and more widely used. However, since the output power of photovoltaic power generation is affected by many factors(such as weather conditions, seasonal changes, geographical location), it is quite necessary to accu...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/177282 |
| _version_ | 1826116003365388288 |
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| author | Wang, Yi Fan |
| author2 | Xu Yan |
| author_facet | Xu Yan Wang, Yi Fan |
| author_sort | Wang, Yi Fan |
| collection | NTU |
| description | As a kind of renewable energy technology, photovoltaic power generation has been more and more widely used. However, since the output power of photovoltaic power generation is affected by many factors(such as weather conditions, seasonal changes, geographical location), it is quite necessary to accurately predict photovoltaic power generation. This project presents two different methods, long short-term memory network (LSTM) and gradient descent, to forecast photovoltaic power (PV) generation and compared their advantages and disadvantages. Gradient descent algorithm is a common optimization algorithm, which can be used to a variety of problems. I found gradient descent to be very good at predicting PV power, especially in the case of large amount of data and few iterations. However, it is sensitive to initial parameters and requires careful adjustment of the learning rate and other parameters. LSTM has the advantage of processing time series data to capture long-term dependencies. My experimental results show that LSTM performs well in predicting PV power, especially when the amount of data is small and the number of iterations is large. However, LSTM takes longer to train and requires more computing resources. |
| first_indexed | 2024-10-01T04:04:19Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/177282 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:04:19Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1772822024-05-31T15:44:19Z AI-based solar PV power forecasting Wang, Yi Fan Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering As a kind of renewable energy technology, photovoltaic power generation has been more and more widely used. However, since the output power of photovoltaic power generation is affected by many factors(such as weather conditions, seasonal changes, geographical location), it is quite necessary to accurately predict photovoltaic power generation. This project presents two different methods, long short-term memory network (LSTM) and gradient descent, to forecast photovoltaic power (PV) generation and compared their advantages and disadvantages. Gradient descent algorithm is a common optimization algorithm, which can be used to a variety of problems. I found gradient descent to be very good at predicting PV power, especially in the case of large amount of data and few iterations. However, it is sensitive to initial parameters and requires careful adjustment of the learning rate and other parameters. LSTM has the advantage of processing time series data to capture long-term dependencies. My experimental results show that LSTM performs well in predicting PV power, especially when the amount of data is small and the number of iterations is large. However, LSTM takes longer to train and requires more computing resources. Bachelor's degree 2024-05-27T07:30:07Z 2024-05-27T07:30:07Z 2024 Final Year Project (FYP) Wang, Y. F. (2024). AI-based solar PV power forecasting. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177282 https://hdl.handle.net/10356/177282 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Wang, Yi Fan AI-based solar PV power forecasting |
| title | AI-based solar PV power forecasting |
| title_full | AI-based solar PV power forecasting |
| title_fullStr | AI-based solar PV power forecasting |
| title_full_unstemmed | AI-based solar PV power forecasting |
| title_short | AI-based solar PV power forecasting |
| title_sort | ai based solar pv power forecasting |
| topic | Engineering |
| url | https://hdl.handle.net/10356/177282 |
| work_keys_str_mv | AT wangyifan aibasedsolarpvpowerforecasting |