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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/8543793http://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] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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