Wind/solar power forecasting using improved LSTM neural networks
Nowadays, new energy become more and more important not only for industry but also for our citizens. How to forecast the wind and solar power correctly is also necessary for power plant. In this dissertation, four kinds of forecasting system based respectively on NARX model, BP neural network model,...
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sg-ntu-dr.10356-785722023-07-04T16:23:05Z Wind/solar power forecasting using improved LSTM neural networks Liu, Shixian Ponnuthurai N. Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Nowadays, new energy become more and more important not only for industry but also for our citizens. How to forecast the wind and solar power correctly is also necessary for power plant. In this dissertation, four kinds of forecasting system based respectively on NARX model, BP neural network model, RNN model, and LSTM neural network model are described and the performance of these model are compared. It is shown that the forecast result of LSTM model is much better than NARX model and other models. With a very small MSE, the LSTM model is really suitable for wind power and solar power forecasting. Master of Science (Computer Control and Automation) 2019-06-24T02:34:09Z 2019-06-24T02:34:09Z 2019 Thesis http://hdl.handle.net/10356/78572 en 58 p. application/pdf |
| institution |
Nanyang Technological University |
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NTU Library |
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Asia |
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Singapore Singapore |
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NTU Library |
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English |
| topic |
DRNTU::Engineering::Electrical and electronic engineering |
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DRNTU::Engineering::Electrical and electronic engineering Liu, Shixian Wind/solar power forecasting using improved LSTM neural networks |
| description |
Nowadays, new energy become more and more important not only for industry but also for our citizens. How to forecast the wind and solar power correctly is also necessary for power plant. In this dissertation, four kinds of forecasting system based respectively on NARX model, BP neural network model, RNN model, and LSTM neural network model are described and the performance of these model are compared. It is shown that the forecast result of LSTM model is much better than NARX model and other models. With a very small MSE, the LSTM model is really suitable for wind power and solar power forecasting. |
| author2 |
Ponnuthurai N. Suganthan |
| author_facet |
Ponnuthurai N. Suganthan Liu, Shixian |
| format |
Theses and Dissertations |
| author |
Liu, Shixian |
| author_sort |
Liu, Shixian |
| title |
Wind/solar power forecasting using improved LSTM neural networks |
| title_short |
Wind/solar power forecasting using improved LSTM neural networks |
| title_full |
Wind/solar power forecasting using improved LSTM neural networks |
| title_fullStr |
Wind/solar power forecasting using improved LSTM neural networks |
| title_full_unstemmed |
Wind/solar power forecasting using improved LSTM neural networks |
| title_sort |
wind/solar power forecasting using improved lstm neural networks |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10356/78572 |
| _version_ |
1772828603535327232 |