2019년 추계 학술대회
Current Result Document :
| 한글제목(Korean Title) |
RNN을 이용한 태양광 에너지 생산 예측 |
| 영문제목(English Title) |
Solar Energy Prediction using Environmental Data via Recurrent Neural Network |
| 저자(Author) |
리아크 무사다르
변영철
이상준
Mudassar Liaq
Yungcheol Byun
Sang-Joon Lee
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| 원문수록처(Citation) |
VOL 26 NO. 02 PP. 1023 ~ 1025 (2019. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Coal and Natural gas are two biggest contributors to a generation of energy throughout the world. Most of these resources create environmental pollution while making energy affecting the natural habitat. Many approaches have been proposed as alternatives to these sources. One of the leading alternatives is Solar Energy which is usually harnessed using solar farms. In artificial intelligence, the most researched area in recent times is machine learning. With machine learning, many tasks which were previously thought to be only humanly doable are done by machine. Neural networks have two major subtypes i.e. Convolutional neural networks (CNN) which are used primarily for classification and Recurrent neural networks which are utilized for time-series predictions. In this paper, we predict energy generated by solar fields and optimal angles for solar panels in these farms for the upcoming seven days using environmental and historical data. We experiment with multiple configurations of RNN using Vanilla and LSTM (Long Short-Term Memory) RNN. We are able to achieve RSME of 0.20739 using LSTMs.
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