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A Hybrid Deep Neural Network Model for Photovoltaic Generation Power Prediction
- 이채은;
- 정대웅;
- 장요한;
- Bae, Sungwoo;
- Oh, Jaeyoung;
- 외 1명
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2초록
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.
키워드
Deep neural network; Gated recurrent unit; Photovoltaic generation power prediction.; Forecasting; Network architecture; Neural network models; Recurrent neural networks; Time series; Deep neural networks; Gated recurrent unit; Multi-layer architectures; Neural network model; Photovoltaic generation power prediction.; Photovoltaic generation system; Photovoltaics generations; Power; Power predictions; Prediction accuracy; Time series characteristic
- 제목
- A Hybrid Deep Neural Network Model for Photovoltaic Generation Power Prediction
- 저자
- 이채은; 정대웅; 장요한; Bae, Sungwoo; Oh, Jaeyoung; Lim, Seungbeom
- 발행일
- 2022-11
- 유형
- Proceedings Paper
- 저널명
- 2022 25TH INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS (ICEMS 2022)
- 페이지
- 1 ~ 5