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Proceedings Paper

Recurrent network based planning and management of PV based islanded microgrid
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Paper Abstract

Solar energy is an intermittent source and purely Photo-voltaic (PV) based, or PV and storage based microgrids require characterization and modelling of PV resources for an effective planning and effective operations. In this research work long-short term memory (LSTM) as a recurrent neural network model is created for forecasting the PV solar resources, in which can assist in quantifying PV generation in various time intervals (hourly, daily, weekly). PV based microgrids often experience expensive or inaccurate resources planning due to the lack of accurate forecasting tools. The proposed LSTM model is simulated based on a real-time basis and the results are analyzed for its impact on planning and operations, and compared with conventional models such as Support Vector Machines - Regression (SVR). Hence, this model can be integrated further with existing energy management (demand side) and monitoring systems to streamline microgrid operations in its entirety.

Paper Details

Date Published: 6 September 2019
PDF: 9 pages
Proc. SPIE 11126, Wide Bandgap Materials, Devices, and Applications IV, 111260A (6 September 2019); doi: 10.1117/12.2532030
Show Author Affiliations
Ahmad Almadhor, Univ. of Denver (United States)
M. Matin, Univ. of Denver (United States)
D. Gao, Univ. of Denver (United States)

Published in SPIE Proceedings Vol. 11126:
Wide Bandgap Materials, Devices, and Applications IV
Mohammad Matin; Andrew P. Lange; Achyut K. Dutta, Editor(s)

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