| 학회 |
한국화학공학회 |
| 학술대회 |
2013년 봄 (04/24 ~ 04/26, 광주 김대중컨벤션센터) |
| 권호 |
19권 1호, p.228 |
| 발표분야 |
공정시스템 |
| 제목 |
Solar Power Energy System Network Modeling |
| 초록 |
Renewable energies can be best useful when their output are forecasted within a reasonable margin. The performance of renewable energy forecasting does thereby play an important role in increasing the stability of renewable energy systems and in the end raising their reliability in the overall energy grid. Motivated by this need, this paper investigates a methodology to forecast power output of photovoltaic energy generation. The state-of-the-art artificial intelligence based neural network and the up-to-date linear/nonlinear time series methodologies are studied with the comparison of their performance. It has been shown that the specially tuned neural network based method showed the best results. |
| 저자 |
이수빈1, 류준형2, 이인범1
|
| 소속 |
1포항공대, 2동국대 |
| 키워드 |
optimization; solar power
|
| E-Mail |
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| 원문파일 |
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