다중 시계열 특성을 고려한 멀티 퓨전 딥러닝 기반 다단계 태양광 발전량 예측 (Multi-Fusion Deep Learning Based Multistep-Ahead Photovoltaic Power Forecasting Considering Multivariate Time Series Characteristics)

서지정보

· 발행기관 : 대한전기학회
· 수록지 정보 : 전기학회논문지 / 73권 / 10호 / 1617 ~ 1623페이지
· 저자명 : 소다영, 문지훈

초록

How can the multistep-ahead prediction of photovoltaic power generation be improved by integrating multivariate time series features in a virtual power plant (VPP) environment? To address this question, this study develops and evaluates a photovoltaic power generation forecasting model that integrates a bidirectional gated recurrent unit (Bi-GRU), a temporal convolutional network (TCN), and a multi-head attention mechanism. Our strategy leverages multi-fusion deep learning (DL), which is known for its ability to synthesize multiple prediction technologies, making it particularly suitable for complex scenarios such as energy forecasting.
Leveraging advances in Internet of Things (IoT) and smart grid technologies, this model improves the management and operational efficiency of distributed energy resources (DERs) within VPPs. Validation with real-world data demonstrates that this sophisticated DL framework effectively improves forecasting accuracy by skillfully capturing the temporal dynamics and interdependencies in the data. Such enhanced predictive capabilities are critical to ensuring the reliability and efficiency of energy systems, and can help provide a stable and balanced power supply in a market shifting to renewable energy sources.

영어초록

How can the multistep-ahead prediction of photovoltaic power generation be improved by integrating multivariate time series features in a virtual power plant (VPP) environment? To address this question, this study develops and evaluates a photovoltaic power generation forecasting model that integrates a bidirectional gated recurrent unit (Bi-GRU), a temporal convolutional network (TCN), and a multi-head attention mechanism. Our strategy leverages multi-fusion deep learning (DL), which is known for its ability to synthesize multiple prediction technologies, making it particularly suitable for complex scenarios such as energy forecasting.
Leveraging advances in Internet of Things (IoT) and smart grid technologies, this model improves the management and operational efficiency of distributed energy resources (DERs) within VPPs. Validation with real-world data demonstrates that this sophisticated DL framework effectively improves forecasting accuracy by skillfully capturing the temporal dynamics and interdependencies in the data. Such enhanced predictive capabilities are critical to ensuring the reliability and efficiency of energy systems, and can help provide a stable and balanced power supply in a market shifting to renewable energy sources.

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