A Hybrid Deep Neural Network Model for Photovoltaic Generation Power Prediction

이채은; 정대웅; 장요한; Bae, Sungwoo; Oh, Jaeyoung; Lim, Seungbeom

doi:10.1109/ICEMS56177.2022.9983405

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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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초록

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

DOI: 10.1109/ICEMS56177.2022.9983405

발행일: 2022-11

유형: Proceedings Paper

저널명: 2022 25TH INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS (ICEMS 2022)

페이지: 1 ~ 5

언어: ENG

출판사: IEEE

발행국가: 미국

분량: 5 페이지