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2026-10-22, Week 43 |
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* Edit or answer any Q&A by selecting Q&A number Hyper Link below + Write button (Save)
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ICACT20230158 Question.1
Questioner: mjs9908@g.skku.edu 2023-02-22 오전 10:35:12 |
ICACT20230158 Answer.1
Answer by Auhor yangkoon@gmail.com 2023-02-22 오전 10:35:12
Chrome  Click!! |
| hi thank you for presentation.
Are the two workflows(the LSTM workflow and LGBM work flow) are running at the same time? |
LSTM and LGBM had separate workflows as shown in the presentation and did not run at the same time. |
ICACT20230158 Question.2
Questioner: 21m107fl@stu.teikyo-u.ac.jp 2023-02-22 오전 10:37:09 |
ICACT20230158 Answer.2
Answer by Auhor yangkoon@gmail.com 2023-02-22 오전 10:37:09
Chrome Click!! |
| Thank you for your presentation. What is the approximate training time for your proposed prediction model? |
The size of the dataset used in this paper was one year of data, so the total training time took about 10 minutes. But this will depend on individual computer specifications.
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ICACT20230158 Question.3
Questioner: a9991204@gmail.com 2023-02-22 오전 10:40:58 |
ICACT20230158 Answer.3
Answer by Auhor yangkoon@gmail.com 2023-02-22 오전 10:40:58
Chrome Click!! |
| Dear author,thank you for presentation.In the paper,the model is just take the LSTM or LGBM ? or it work together? |
In this paper, we developed a total of four predictive models that predict solar power generation and power load, respectively, using LSTM (Deep Learning) and LGBM (Machine Learning). We applied XAI to four predictive models to increase the reliability of the results. LGBM and LSTM operate separately and use XAI together for each prediction model. |
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