Solar irradiance prediction
In recent years, the increased of the world's electricity consumption has resulted in a significant rise in carbon dioxide emissions from electricity generation, contributing heavily to global warming. To combat this, renewable energy sources like solar power are being increasingly adopted to r...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/167645 |
| _version_ | 1811692416547160064 |
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| author | Mohamad Salihin Bin Mohamad Kassim |
| author2 | Lee Yee Hui |
| author_facet | Lee Yee Hui Mohamad Salihin Bin Mohamad Kassim |
| author_sort | Mohamad Salihin Bin Mohamad Kassim |
| collection | NTU |
| description | In recent years, the increased of the world's electricity consumption has resulted in a significant rise in carbon dioxide emissions from electricity generation, contributing heavily to global warming. To combat this, renewable energy sources like solar power are being increasingly adopted to reduce reliance on fossil fuels and slow down global warming. However, to effectively use solar power, accurate forecasting of solar irradiance is crucial for predicting the output of solar PV systems and optimizing their operation for better system reliability. This study aims to investigate various short-term forecasting methods for solar irradiance and evaluate their prediction accuracy using mean absolute error and root mean squared error measures. Specifically, the report focuses on implementing three machine learning methods which are Long Short-Term Memory (LSTM), Feedforward Neural Network (FNN), and Autoregressive Integrated Moving Average (ARIMA). Based on the research, extracting results and comparison, Long Short-Term Memory has proven to be the most effective and easiest method to implement compared to Feedforward Neural Network (FNN) and Autoregressive Integrated Moving Average (ARIMA). |
| first_indexed | 2024-10-01T06:35:26Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/167645 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:35:26Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1676452023-07-07T17:56:01Z Solar irradiance prediction Mohamad Salihin Bin Mohamad Kassim Lee Yee Hui School of Electrical and Electronic Engineering EYHLee@ntu.edu.sg Engineering::Electrical and electronic engineering In recent years, the increased of the world's electricity consumption has resulted in a significant rise in carbon dioxide emissions from electricity generation, contributing heavily to global warming. To combat this, renewable energy sources like solar power are being increasingly adopted to reduce reliance on fossil fuels and slow down global warming. However, to effectively use solar power, accurate forecasting of solar irradiance is crucial for predicting the output of solar PV systems and optimizing their operation for better system reliability. This study aims to investigate various short-term forecasting methods for solar irradiance and evaluate their prediction accuracy using mean absolute error and root mean squared error measures. Specifically, the report focuses on implementing three machine learning methods which are Long Short-Term Memory (LSTM), Feedforward Neural Network (FNN), and Autoregressive Integrated Moving Average (ARIMA). Based on the research, extracting results and comparison, Long Short-Term Memory has proven to be the most effective and easiest method to implement compared to Feedforward Neural Network (FNN) and Autoregressive Integrated Moving Average (ARIMA). Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-31T06:30:29Z 2023-05-31T06:30:29Z 2023 Final Year Project (FYP) Mohamad Salihin Bin Mohamad Kassim (2023). Solar irradiance prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167645 https://hdl.handle.net/10356/167645 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Mohamad Salihin Bin Mohamad Kassim Solar irradiance prediction |
| title | Solar irradiance prediction |
| title_full | Solar irradiance prediction |
| title_fullStr | Solar irradiance prediction |
| title_full_unstemmed | Solar irradiance prediction |
| title_short | Solar irradiance prediction |
| title_sort | solar irradiance prediction |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/167645 |
| work_keys_str_mv | AT mohamadsalihinbinmohamadkassim solarirradianceprediction |