Forecasting of Photovoltaic Solar Power Production Using LSTM Approach
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
King Abdullah University of Science and Technology, KAUST, Thuwal, Saudi Arabia.
KAUST Grant NumberOSR-2019-CRG7-3800
Permanent link to this recordhttp://hdl.handle.net/10754/662457
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AbstractSolar-based energy is becoming one of the most promising sources for producing power for residential, commercial, and industrial applications. Energy production based on solar photovoltaic (PV) systems has gained much attention from researchers and practitioners recently due to its desirable characteristics. However, the main difficulty in solar energy production is the volatility intermittent of photovoltaic system power generation, which is mainly due to weather conditions. For the large-scale solar farms, the power imbalance of the photovoltaic system may cause a significant loss in their economical profit. Accurate forecasting of the power output of PV systems in a short term is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. The aim of this chapter is to provide reliable short-term forecasting of power generation of PV solar systems. Specifically, this chapter presents a long short-term memory (LSTM)-based deep learning approach for forecasting power generation of a PV system. This is motivated by the desirable features of LSTM to describe dependencies in time series data. The performance of the algorithm is evaluated using data from a 9 MWp grid-connected plant. Results show promising power forecasting results of LSTM.
CitationHarrou, F., Kadri, F., & Sun, Y. (2020). Forecasting of Photovoltaic Solar Power Production Using LSTM Approach. Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems. doi:10.5772/intechopen.91248
SponsorsThis publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.
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