Accurate short-term solar and wind power predictions play an important role in the planning and operation of power systems. However, the short-term power prediction of renewable energy has always been considered a complex regression problem, owing to the fluctuation and intermittence of output powers and the law of dynamic change with time due to local weather conditions, i.e. spatio-temporal correlation. To capture the spatio-temporal features simultaneously, this paper proposes a new graph neural network-based short-term power forecasting approach, which combines the graph convolutional network (GCN) and long short-term memory (LSTM). Specifically, the GCN is employed to learn complex spatial correlations between adjacent renewable energies, and the LSTM is used to learn dynamic changes of power curves. The simulation results show that the proposed hybrid approach can model the spatio-temporal correlation of renewable energies, and its performance outperforms popular baselines on real-world datasets.

翻译：准确的短期太阳能和风能预测在电力系统的规划和运行中发挥着重要作用,然而,由于产出力的波动和间歇性,以及由于当地天气条件,即地表-时空相关关系,随着时间的变化规律,可再生能源的短期电力预测一直被视为复杂的回归问题。为了同时捕捉地表-时空特征,本文件提议采用新的图形神经网络短期电力预测方法,将图形革命网络(GCN)和长期短期记忆(LSTM)结合起来。具体地说,GCN用于学习相邻可再生能源之间的复杂的空间关系,而LSTM用于学习动力曲线的动态变化。模拟结果表明,拟议的混合方法可以模拟可再生能源的地表-时空关系及其在现实世界数据集上的性能超过流行基线。