TY - GEN
T1 - Performance Evaluation of Deep Recurrent Neural Networks Architectures
T2 - 2nd International Conference on Smart Grid and Renewable Energy, SGRE 2019
AU - Massaoudi, Mohamed
AU - Chihi, Ines
AU - Sidhom, Lilia
AU - Trabelsi, Mohamed
AU - Refaat, Shady S.
AU - Oueslati, Fakhreddine S.
N1 - Funding Information:
This publication was made possible by NPRP grant [NPRP10-0101-170082] from the Qatar National Research Fund (a member of Qatar Foundation) and the co-funding by IBERDROLA QSTP LLC. The statements made herein are solely the responsibility of the authors.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Smart grid systems require an accurate energy prediction from renewable sources to ensure high sustainability and power quality. For PV plants, a precise estimation of the generated PV power is crucial for the reduction of the production/demand unbalance. This essential need comes from the high variability of weather parameters during the PV electricity generation. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) recurrent neural networks proved their high efficiency in forecasting applications. Thus, this paper proposes a comprehensive evaluation of the LSTM and GRU techniques for PV power estimation in the medium/long horizon. The evaluation is based on a fair assessment of the aforementioned architectures for one week and more than three months (98 days) periods.
AB - Smart grid systems require an accurate energy prediction from renewable sources to ensure high sustainability and power quality. For PV plants, a precise estimation of the generated PV power is crucial for the reduction of the production/demand unbalance. This essential need comes from the high variability of weather parameters during the PV electricity generation. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) recurrent neural networks proved their high efficiency in forecasting applications. Thus, this paper proposes a comprehensive evaluation of the LSTM and GRU techniques for PV power estimation in the medium/long horizon. The evaluation is based on a fair assessment of the aforementioned architectures for one week and more than three months (98 days) periods.
KW - Gated Recurrent Unit
KW - GRU
KW - Long Short Term Memory
KW - LSTM
KW - PV Power Forecasting
KW - Smart Grids
UR - http://www.scopus.com/inward/record.url?scp=85082302943&partnerID=8YFLogxK
U2 - 10.1109/SGRE46976.2019.9020965
DO - 10.1109/SGRE46976.2019.9020965
M3 - Conference contribution
AN - SCOPUS:85082302943
T3 - 2nd International Conference on Smart Grid and Renewable Energy, SGRE 2019 - Proceedings
BT - 2nd International Conference on Smart Grid and Renewable Energy, SGRE 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers (IEEE)
Y2 - 19 November 2019 through 21 November 2019
ER -