Accurate generation forecasting can effectively accelerate the use of renewable energy in hybrid energy systems, contributing significantly to the delivery of the net-zero emission target. Recently, neural-network-based quantile forecast models have shown superior performance on renewable energy generation forecasting, partially because they have subtly embedded quantile forecast evaluation metrics into their loss functions. However, the non-differentiability of involved metrics has rendered their metric-embedded loss functions not everywhere-derivable, resulting in inapplicability of gradient-based training approaches. Instead, they have resorted to heuristic searches for Neural Network (NN) training, bringing low training efficiency and a rigid restriction on the size of the resultant NN. In this paper, the Indicator Gradient Descent (IGD) is proposed to overcome the non-differentiability of involved metrics, and several metric-embedded loss functions are innovatively customized combining IGD, enabling NNs to be trained efficiently in a ‘gradient-descent-like’ manner. Moreover, the deep Bidirectional Long Short-Term Memory (BiLSTM) is adopted to capture the periodicity of renewable generation (diurnal and seasonal patterns), and the residual technique is used to improve the training efficiency of the deep BiLSTM. Finally, a Deep Quantile Forecast Network (DQFN) based on IGD and deep residual BiLSTM is developed for wind and solar power quantile forecasting. Practical experiments in four cases have verified the effectiveness and efficiency of DQFN and IGD, where DQFN has achieved the lowest average proportion deviations (all below 1.7%) and the highest skill scores.

准确的发电量预测可以有效加速可再生能源在混合能源系统中的使用，为实现净零排放目标做出重大贡献。最近，基于神经网络的分位数预测模型在可再生能源发电预测方面表现出卓越的性能，部分原因是它们巧妙地嵌入了分位数预测评估指标进入他们的损失函数。然而，所涉及的度量的不可微性使得它们的度量嵌入损失函数不能随处推导，导致基于梯度的训练方法不适用。相反，他们对神经网络 (NN) 训练采取了启发式搜索，这带来了低训练效率和对结果 NN 大小的严格限制。在本文中，提出了指标梯度下降（IGD）来克服所涉及指标的不可微性，并且结合IGD创​​新地定制了几个嵌入指标的损失函数，使神经网络能够在“类梯度下降”中高效训练' 方式。此外，采用深度双向长短期记忆（BiLSTM）来捕捉可再生发电的周期性（昼夜和季节性模式），使用残差技术提高深度 BiLSTM 的训练效率。最后，基于 IGD 和深度残差 BiLSTM 的深度分位数预测网络 (DQFN) 被开发用于风能和太阳能的分位数预测。四个案例的实际实验验证了 DQFN 和 IGD 的有效性和效率，其中 DQFN 实现了最低的平均比例偏差（均低于 1.7%）和最高的技能分数。