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näkemys - Renewable Energy Forecasting
심층 학습 및 환경 의사 결정 지원 시스템을 통한 재생 에너지 수요 예측을 위한 LTPNet 모델 개발
본 연구에서는 LSTM, Transformer 모델, PSO 알고리즘을 결합한 LTPNet 모델을 제시하여 재생 에너지 수요 예측의 정확성과 신뢰성을 향상시키는 방법을 제안합니다.
LTPNet: A Deep Learning Model for Renewable Energy Demand Forecasting
Integrating deep learning techniques, specifically LSTM and Transformer models optimized by the PSO algorithm, significantly improves the accuracy and reliability of renewable energy demand forecasting.
MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting
MATNet is a novel self-attention transformer-based architecture that significantly outperforms current state-of-the-art methods in day-ahead PV generation forecasting, demonstrating the potential to improve accuracy and facilitate the integration of PV energy into the power grid.
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