Solar power generation forecasting based on AI
As the world aims to reduce carbon emissions, there is a strong future trend of adopting clean energy for power generation. Solar photovoltaic (PV) power energy has grown to be one of the most promising choice of clean energy because of the abundance of solar energy. However, PV power generation...
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Format: | Final Year Project |
Language: | English |
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Nanyang Technological University
2024
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Online Access: | https://hdl.handle.net/10356/181713 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | As the world aims to reduce carbon emissions, there is a strong future trend of adopting clean
energy for power generation. Solar photovoltaic (PV) power energy has grown to be one of the
most promising choice of clean energy because of the abundance of solar energy. However, PV
power generation greatly depends on weather conditions and lead to PV power generation to
be unpredictable. This can affect the stability of power systems. Therefore, this paper aims to
study solutions to predict PV power generation with only historical PV power generation data.
In addition, this paper investigates the effect of the size of lookback window on the forecasting
accuracy of various models. This paper looks into the different types of models used to forecast
PV power generation and focuses on existing research regarding AI based models. AI based
models show great potential in handling nonlinear and complex relationship between weather
conditions and PV power generation. Some examples of AI based models studied in this paper
include ANN, LSTM and transformer model. The models are trained and validated using two
datasets containing hourly energy data from two solar sites in North America from 01/01/2015
to 01/01/2016. Forecasting results of the two datasets are presented and analysed. Lastly,
conclusions will be made about which model and choice of lookback window size are most
suitable for each dataset. |
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