Deep learning approaches have shown superior advantages than shallow techniques in the field of electrical load forecasting; however, their applications in existing studies encounter thorny issues despite their excellent forecasting performance: heavy computing costs due to complicated network structure and restricted to the deterministic point forecasting. This paper aims to solve above two problems by proposing a deep learning approach based on K-nearest neighbors to capture uncertainty and reflect the range of electrical load fluctuation. First, the K-nearest neighbors algorithm is applied to seek features of historical electrical load time series that are similar to the future values by calculating the distance between the training and testing datasets. Then the second generation of non-dominated sorting genetic algorithm is adopted for multi-objective optimization to find out the smallest category number of K-nearest neighbors and the highest forecasting accuracy. Based on the forecasting results of the deep belief network, modified non-parameter kernel density estimation is used to obtain the prediction intervals. Five datasets collected from Australia are employed to examine the effectiveness of the proposed model. By a series of comparisons with other state-of-the-art models, experimental results confirm that the proposed interval forecasting model cannot only improve the forecasting efficiency and accuracy, but also simplify the forecasting process of deep learning approaches, which can provide great referential value for future work.

在电力负荷预测领域，深度学习方法已显示出比浅层技术优越的优势。然而，尽管它们具有出色的预测性能，但它们在现有研究中的应用仍然遇到棘手的问题：由于复杂的网络结构以及确定性点的预测，计算成本很高。本文旨在通过提出一种基于K近邻的深度学习方法来解决上述两个问题，以捕获不确定性并反映电力负载波动的范围。首先，K-最近邻居算法通过计算训练数据集和测试数据集之间的距离，以寻找与未来值相似的历史电负载时间序列的特征。然后采用第二代非支配排序遗传算法进行多目标优化，找出K的最小类别数。-最邻近的邻居和最高的预测准确性。根据深度置信网络的预测结果，使用改进的非参数核密度估计来获得预测间隔。从澳大利亚收集的五个数据集被用来检验该模型的有效性。通过与其他最新模型的一系列比较，实验结果证实了所提出的区间预测模型不仅可以提高预测效率和准确性，而且可以简化深度学习方法的预测过程，这可以提供很大的参考价值未来工作的价值。