A Hybrid Deep Neural Network Model for Photovoltaic Generation Power Prediction

This paper presents a hybrid deep neural network (DNN) model for predicting the power of a photovoltaic generation (PV) system. The proposed model consists of multilayer architecture by synthesizing a DNN model and a gated recurrent unit (GRU) model. This architecture enhances prediction accuracy by...

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Bibliographic Details
Main Authors: Lee, Chaeeun, Jeong, Daeung, Jang, Yohan, Bae, Sungwoo, Oh, Jaeyoung, Lim, Seungbeom
Format: Conference Proceeding
Language:English
Subjects:
Deep learning
Deep neural network
Gated recurrent unit
Graphics processing units
Logic gates
Neural networks
Photovoltaic generation power prediction
Photovoltaic systems
Predictive models
Simulation
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Description
Summary:This paper presents a hybrid deep neural network (DNN) model for predicting the power of a photovoltaic generation (PV) system. The proposed model consists of multilayer architecture by synthesizing a DNN model and a gated recurrent unit (GRU) model. This architecture enhances prediction accuracy by reflecting the nonlinearity and time-series characteristics of the PV power. The performance of the proposed model is verified by comparative simulation with the DNN model and the GRU model. As a simulation result, the proposed model improved the prediction accuracy by up to 98% compared to the DNN model. Therefore, the proposed model can accurately predict the PV power by reflecting the time-series characteristics.
ISSN:2642-5513
DOI:10.1109/ICEMS56177.2022.9983405