将天气图当令牌，用Transformer预测风光发电量。
Weather Maps as Tokens: Transformers for Renewable Energy Forecasting
- 把天气图转成空间令牌，用轻量卷积网络编码
- 45小时预报内风能误差降60%，太阳能降20%
- 适合能源调度、电网规划人员参考
精准的可再生能源预测对减少化石燃料依赖、推动电网脱碳至关重要。然而，现有方法难以有效融合天气模式的丰富空间信息与时间演变特征。本文提出一种新方法：将小时级天气图作为Transformer序列中的空间令牌进行处理。首先通过轻量卷积神经网络将天气图编码为空间令牌，再由Transformer捕捉跨45小时预报时长的时间动态。尽管输入初始化存在局限，但与ENTSO-E运营预报相比，风能预测的均方根误差（RMSE）降低约60%，太阳能预测降低20%。每日预报实时可视化仪表盘已上线：https://www.sardiniaforecast.ifabfoundation.it。
原文摘要 · Abstract (English)
Accurate renewable energy forecasting is essential to reduce dependence on fossil fuels and enabling grid decarbonization. However, current approaches fail to effectively integrate the rich spatial context of weather patterns with their temporal evolution. This work introduces a novel approach that treats weather maps as tokens in transformer sequences to predict renewable energy. Hourly weather maps are encoded as spatial tokens using a lightweight convolutional neural network, and then processed by a transformer to capture temporal dynamics across a 45-hour forecast horizon. Despite disadvantages in input initialization, evaluation against ENTSO-E operational forecasts shows a reduction in RMSE of about 60% and 20% for wind and solar respectively. A live dashboard showing daily forecasts is available at: https://www.sardiniaforecast.ifabfoundation.it.
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