A Hybrid TFT-NBEATS Framework for Robust Multi-Horizon Photovoltaic Power Forecasting

Key points

• To address these limitations, this study proposes a novel hybrid deep learning framework that integrates a Temporal Fusion Transformer (TFT) and N-BEATS within an attention-based fusion architecture.1
• The proposed model leverages TFT to learn short-term temporal dependencies and dynamic feature importance, while N-BEATS captures long-term trend and seasonal components through interpretable basis decomposition.1
• Experimental results demonstrate superior performance over statistical, machine learning, and deep learning baselines.1
• These results demonstrate that the proposed framework is robust, interpretable, and suitable for practical renewable energy applications.1

Sources (1)

• [1]A Hybrid TFT-NBEATS Framework for Robust Multi-Horizon Photovoltaic Power Forecasting
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:49 PM

  Accurate multi-horizon photovoltaic (PV) power forecasting is essential for grid stability, energy scheduling, and the efficient operation of PVbatterygrid systems.

  To address these limitations, this study proposes a novel hybrid deep learning framework that integrates a Temporal Fusion Transformer (TFT) and N-BEATS within an attention-based fusion architecture.

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