Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks
- Title
- Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks
- Author
- 김영훈
- Keywords
- Solar power forecasting; deep learning; convolutional neural networks; long-short term memory
- Issue Date
- 2018-11
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Citation
- IEEE ACCESS, v. 6, Page. 73068-73080
- Abstract
- As solar photovoltaic (PV) generation becomes cost-effective, solar power comes into its own as the alternative energy with the potential to make up a larger share of growing energy needs. Consequently, operations and maintenance cost now have a large impact on the profit of managing power modules, and the energy market participants need to estimate the solar power in short or long terms of future. In this paper, we propose a solar power forecasting technique by utilizing convolutional neural networks and long-shortterm memory networks recently developed for analyzing time series data in the deep learning communities. Considering that weather information may not be always available for the location where PV modules are installed and sensors are often damaged, we empirically confirm that the proposed method predicts the solar power well with roughly estimated weather data obtained from national weather centers as well as it works robustly without sophisticatedly preprocessed input to remove outliers.
- URI
- https://ieeexplore.ieee.org/abstract/document/8543793https://repository.hanyang.ac.kr/handle/20.500.11754/106165
- ISSN
- 2169-3536
- DOI
- 10.1109/ACCESS.2018.2883330
- Appears in Collections:
- COLLEGE OF COMPUTING[E](소프트웨어융합대학) > COMPUTER SCIENCE(소프트웨어학부) > Articles
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