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.

翻译：准确的可再生能源预测对于减少对化石燃料的依赖和实现电网脱碳至关重要。然而，现有方法未能有效整合天气模式丰富的空间背景与其时间演变。本研究提出一种创新方法，将天气图视为Transformer序列中的令牌来预测可再生能源。每小时天气图通过轻量级卷积神经网络编码为空间令牌，随后由Transformer处理以捕捉45小时预测范围内的时序动态。尽管在输入初始化方面存在不足，但与ENTSO-E运营预测的评估比较显示，风能和太阳能的均方根误差分别降低了约60%和20%。展示每日预测的实时仪表板可在以下网址访问：https://www.sardiniaforecast.ifabfoundation.it。