Due to the intermittent and stochastic nature of solar power, solar power interval prediction is of great importance for grid management and power dispatching. A combination interval prediction model based on the lower and upper bound estimation (LUBE) is proposed to efficiently quantify the solar power prediction uncertainty. In the proposed model, the upper and lower bounds are separately predicted by two prediction engines. The extreme learning machine (ELM) is selected as the basic prediction engine. The auto-encoder technique is used to initialize the input weight matrix of ELM for efficient feature learning. A novel biased convex cost function is developed for ELM to predict the interval boundary. The output weight matrix of ELM can be solved via the convex optimization technique instead of the conventional heuristic algorithm. The proposed interval prediction model can be formulated as a bi-level optimization problem. In the lower-level problem, the lower and upper ELMs are trained under different candidate hyper-parameters of the biased cost function. In the upper-level problem, the optimal combination of the lower and upper prediction engines is determined by evaluating the interval prediction performance. Comprehensive experiments based on public data set are conducted to validate the superiority of the proposed interval prediction model.

中文翻译：

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一种基于偏置凸成本函数和自编码器的太阳能功率预测组合区间预测模型

由于太阳能发电的间歇性和随机性，太阳能发电间隔预测对于电网管理和电力调度具有重要意义。提出了一种基于上下限估计（LUBE）的组合区间预测模型，以有效量化太阳能功率预测的不确定性。在提出的模型中，上限和下限由两个预测引擎分别预测。极限学习机（ELM）被选为基本预测引擎。自动编码器技术用于初始化 ELM 的输入权重矩阵，以实现高效的特征学习。为 ELM 开发了一种新的有偏凸成本函数来预测区间边界。ELM 的输出权重矩阵可以通过凸优化技术而不是传统的启发式算法来求解。提出的区间预测模型可以表述为一个双层优化问题。在下层问题中，下层和上层 ELM 在偏置成本函数的不同候选超参数下进行训练。在上层问题中，通过评估区间预测性能来确定下层和上层预测引擎的最佳组合。基于公开数据集的综合实验验证了所提出的区间预测模型的优越性。通过评估区间预测性能来确定上下预测引擎的最佳组合。基于公开数据集的综合实验验证了所提出的区间预测模型的优越性。通过评估区间预测性能来确定上下预测引擎的最佳组合。基于公开数据集的综合实验验证了所提出的区间预测模型的优越性。