State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China.
Department of Geosciences, University of Oslo, Blindern, P.O. Box 1047, N-0316, Oslo, Norway.
Environ Sci Pollut Res Int. 2023 Feb;30(7):17741-17764. doi: 10.1007/s11356-022-23305-0. Epub 2022 Oct 6.
Energy efficiency is crucial to greenhouse gas (GHG) emission pathways reported by the Intergovernmental Panel on Climate Change. Electrical overload frequently occurs and causes unwanted outages in distribution networks, which reduces energy utilization efficiency and raises environmental risks endangering public safety. Electrical load, however, has a dynamically fluctuating behavior with notoriously nonlinear hourly, daily, and seasonal patterns. Accurate and reliable load forecasting plays an important role in scheduling power generation processes and preventing electrical systems from overloading; nevertheless, such forecasting is fundamentally challenging, especially under highly variable power load and climate conditions. This study proposed a deep learning-based monotone composite quantile regression neural network (D-MCQRNN) model to extract the multiple non-crossing and nonlinear quantile functions while conquering the drawbacks of error propagation and accumulation encountered in multi-step-ahead probability density forecasting. The constructed models were assessed by an hourly power load series collected at the electric grid center of Henan Province in China in two recent years, along with the corresponding meteorological data collected at 16 monitoring stations. The results demonstrated that the proposed D-MCQRNN model could significantly alleviate the time-lag and biased-prediction phenomena and noticeably improve the accuracy and reliability of multi-step-ahead probability density forecasts on power load. Consequently, the proposed model can significantly reduce the risk and impact of overload faults and effectively promote energy utilization efficiency, thereby mitigating GHG emissions and moving toward cleaner energy production.
能源效率对于政府间气候变化专门委员会报告的温室气体（GHG）排放途径至关重要。电力过载经常发生，并导致配电网络中的意外停电，从而降低能源利用效率并增加危及公共安全的环境风险。然而，电力负荷具有动态波动的行为，具有众所周知的非线性小时、日和季节性模式。准确可靠的负荷预测对于调度发电过程和防止电力系统过载起着重要作用；然而，这种预测从根本上具有挑战性，尤其是在高度变化的电力负荷和气候条件下。本研究提出了一种基于深度学习的单调复合分位数回归神经网络（D-MCQRNN）模型，用于提取多个非交叉和非线性分位数函数，同时克服多步向前概率密度预测中遇到的误差传播和积累的缺点。所构建的模型通过在中国河南省电网中心收集的近两年的每小时电力负荷序列以及在 16 个监测站收集的相应气象数据进行评估。结果表明，所提出的 D-MCQRNN 模型可以显著减轻时间滞后和有偏预测现象，并显著提高电力负荷多步向前概率密度预测的准确性和可靠性。因此，所提出的模型可以显著降低过载故障的风险和影响，并有效提高能源利用效率，从而减少温室气体排放并朝着更清洁的能源生产方向发展。
