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A Hybrid Deep Neural Network Model for Photovoltaic Generation Power Prediction
- Issue Date
- Nov-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Deep neural network; Gated recurrent unit; Photovoltaic generation power prediction.
- Citation
- 2022 International Conference on Electrical Machines and Systems, ICEMS 2022, pp.1 - 5
- Indexed
- SCOPUS
- Journal Title
- 2022 International Conference on Electrical Machines and Systems, ICEMS 2022
- Start Page
- 1
- End Page
- 5
- Abstract
- 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.
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Related Researcher
- Bae, Sung Woo
- COLLEGE OF ENGINEERING (MAJOR IN ELECTRICAL ENGINEERING)