Performance Evaluation of Deep Recurrent Neural Networks Architectures: Application to PV Power Forecasting

Mohamed Massaoudi, Ines Chihi, Lilia Sidhom, Mohamed Trabelsi, Shady S. Refaat, Fakhreddine S. Oueslati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2nd International Conference on Smart Grid and Renewable Energy, SGRE 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728129600
DOIs
Publication statusPublished - Nov 2019
Event2nd International Conference on Smart Grid and Renewable Energy, SGRE 2019 - Doha, Qatar
Duration: 19 Nov 201921 Nov 2019

Publication series

Name2nd International Conference on Smart Grid and Renewable Energy, SGRE 2019 - Proceedings

Conference

Conference2nd International Conference on Smart Grid and Renewable Energy, SGRE 2019
CountryQatar
CityDoha
Period19/11/1921/11/19

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Keywords

  • Gated Recurrent Unit
  • GRU
  • Long Short Term Memory
  • LSTM
  • PV Power Forecasting
  • Smart Grids

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

Cite this

Massaoudi, M., Chihi, I., Sidhom, L., Trabelsi, M., Refaat, S. S., & Oueslati, F. S. (2019). Performance Evaluation of Deep Recurrent Neural Networks Architectures: Application to PV Power Forecasting. In 2nd International Conference on Smart Grid and Renewable Energy, SGRE 2019 - Proceedings [9020965] (2nd International Conference on Smart Grid and Renewable Energy, SGRE 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SGRE46976.2019.9020965