Predictions of renewable energy (RE) generation and electricity load are critical to smart grid operation. However, the prediction task remains challenging due to the intermittent and chaotic character of RE sources, and the diverse user behavior and power consumers. This article presents a novel method for the prediction of RE generation and electricity load using improved stacked gated recurrent unit-recurrent neural network (GRU-RNN) for both univariate and multivariate scenarios. First, multiple sensitive monitoring parameters or historical electricity consumption data are selected according to the correlation analysis to form the input data. Second, a stacked GRU-RNN using a simplified GRU is constructed with improved training algorithm based on AdaGrad and adjustable momentum. The modified GRU-RNN structure and improved training method enhance training efficiency and robustness. Third, the stacked GRU-RNN is used to establish an accurate mapping between the selected variables and RE generation or electricity load due to its self-feedback connections and improved training mechanism. The proposed method is verified by using two experiments: prediction of wind power generation using multiple weather parameters and prediction of electricity load with historical energy consumption data. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods of machine learning or deep learning in achieving an accurate energy prediction for effective smart grid operation.

中文翻译：

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基于堆叠式 GRU-RNN 的方法用于预测智能电网运行的可再生能源和电力负荷


可再生能源 (RE) 发电和电力负荷的预测对于智能电网运行至关重要。然而，由于可再生能源的间歇性和混乱性以及用户行为和电力消费者的多样化，预测任务仍然具有挑战性。本文提出了一种使用改进的堆叠门控循环单元循环神经网络 (GRU-RNN) 预测可再生能源发电和电力负荷的新方法，适用于单变量和多变量场景。首先，根据相关性分析选择多个敏感监测参数或历史用电数据，形成输入数据。其次，使用基于 AdaGrad 和可调动量的改进训练算法构建了使用简化 GRU 的堆叠 GRU-RNN。修改后的GRU-RNN结构和改进的训练方法提高了训练效率和鲁棒性。第三，堆叠式 GRU-RNN 凭借其自反馈连接和改进的训练机制，用于在所选变量与可再生能源发电或电力负荷之间建立准确的映射。通过两个实验验证了所提出的方法：利用多个天气参数预测风力发电量和利用历史能耗数据预测电力负荷。实验结果表明，所提出的方法在实现有效智能电网运行的准确能量预测方面优于最先进的机器学习或深度学习方法。